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README.md

@@ -1,12 +1,117 @@
----
-title: Agent Space
-emoji: 🌖
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 6.5.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Optimax-agent
+emoji: 🛡️
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 6.3.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# 🛡️ Optimax Security Agent
+
+AI-powered Salesforce security analysis API that receives metadata from Salesforce orgs and returns comprehensive security assessments.
+
+## Architecture
+
+```
+Salesforce Org → Sends Metadata → Optimax Agent API → Returns Analysis
+```
+
+**Key Feature:** This API processes **ONLY METADATA** - no credentials are ever received or stored.
+
+## Features
+
+- 🤖 **AI-Powered Analysis** using Salesforce CodeGen 350M
+- 🔍 **Permission Vulnerability Detection**
+- 👤 **Identity & Access Management Analysis**
+- 🔐 **Sharing Model Security Assessment**
+- 📊 **Risk Scoring & Prioritization**
+- 💡 **Actionable Recommendations**
+
+## API Endpoints
+
+### POST `/api/analyze`
+
+Main analysis endpoint for Salesforce integration.
+
+**Request:**
+```json
+{
+  "org_id": "00Dxx0000001234",
+  "org_name": "Your Organization",
+  "users": [...],
+  "profiles": [...],
+  "permission_sets": [...],
+  "login_history": [...],
+  "sharing_settings": {...}
+}
+```
+
+**Response:**
+```json
+{
+  "success": true,
+  "overall_risk_score": 45,
+  "risk_level": "MEDIUM",
+  "critical_findings": [...],
+  "high_findings": [...],
+  "ai_executive_summary": "...",
+  "ai_recommendations": [...]
+}
+```
+
+## Usage
+
+### From Salesforce Apex:
+
+```apex
+// 1. Collect metadata
+Map<String, Object> metadata = MetadataExtractor.collectOrgMetadata();
+
+// 2. Call Optimax Agent
+HttpRequest req = new HttpRequest();
+req.setEndpoint('callout:Optimax_Agent/api/analyze');
+req.setMethod('POST');
+req.setHeader('Content-Type', 'application/json');
+req.setBody(JSON.serialize(metadata));
+
+Http http = new Http();
+HttpResponse res = http.send(req);
+
+// 3. Parse response
+Map<String, Object> analysis = (Map<String, Object>)JSON.deserializeUntyped(res.getBody());
+```
+
+### Using cURL:
+
+```bash
+curl -X POST https://m8077anya-vishwakarma-optimax-agent.hf.space/api/analyze \
+  -H "Content-Type: application/json" \
+  -d @metadata.json
+```
+
+## Testing
+
+Visit the app in your browser to use the interactive testing interface.
+
+## Security
+
+- ✅ No credential processing
+- ✅ Metadata-only analysis
+- ✅ Stateless operation
+- ✅ Private space (access controlled)
+
+## Technical Details
+
+- **Framework:** Gradio + FastAPI
+- **AI Model:** Salesforce CodeGen 350M (350 million parameters)
+- **Analysis:** Hybrid (AI + rule-based)
+- **Deployment:** Hugging Face Spaces
+
+---
+
+**Version:** 2.0.0  
+**Last Updated:** January 2026

ai_models.py

@@ -0,0 +1,838 @@
+"""
+ai_models.py - Multi-Model AI Security Analyzer for Salesforce
+
+Uses FOUR models for comprehensive analysis:
+1. CodeBERT (125M) - For Apex code analysis
+2. RoBERTa (125M) - For permission classification
+3. SecBERT (110M) - For security policy analysis
+4. Isolation Forest - For anomaly detection (NEW)
+
+Usage:
+    from ai_models import MultiModelAnalyzer
+    
+    analyzer = MultiModelAnalyzer()
+    results = analyzer.analyze_org(metadata)
+"""
+
+import torch
+import logging
+from transformers import (
+    RobertaTokenizer, 
+    RobertaModel,
+    AutoTokenizer,
+    AutoModel
+)
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.ensemble import IsolationForest  # NEW: Anomaly detection
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# Import our anomaly detector
+try:
+    from anomaly_detector import AnomalyDetector
+    ANOMALY_AVAILABLE = True
+except ImportError:
+    logger.warning("⚠️ anomaly_detector.py not found - anomaly detection disabled")
+    ANOMALY_AVAILABLE = False
+
+# ============================================================================
+# MULTI-MODEL ANALYZER
+# ============================================================================
+
+class MultiModelAnalyzer:
+    """
+    Multi-model AI analyzer using CodeBERT, RoBERTa, SecBERT, and Isolation Forest
+    """
+    
+    def __init__(self):
+        """Initialize all four models"""
+        self.available = False
+        
+        try:
+            logger.info("🤖 Loading AI Models...")
+            logger.info("   1/4 Loading CodeBERT for code analysis...")
+            self.codebert = CodeBERTAnalyzer()
+            
+            logger.info("   2/4 Loading RoBERTa for permission classification...")
+            self.roberta = RoBERTaAnalyzer()
+            
+            logger.info("   3/4 Loading SecBERT for security analysis...")
+            self.secbert = SecBERTAnalyzer()
+            
+            logger.info("   4/4 Loading Anomaly Detector...")
+            if ANOMALY_AVAILABLE:
+                self.anomaly = AnomalyDetector()
+                logger.info("   ✅ Anomaly detector loaded!")
+            else:
+                self.anomaly = None
+                logger.warning("   ⚠️ Anomaly detector not available")
+            
+            self.available = True
+            logger.info("✅ All AI models loaded successfully!")
+            
+        except Exception as e:
+            logger.error(f"❌ Failed to load AI models: {e}")
+            self.available = False
+    
+    def analyze_org(self, metadata):
+        """
+        Analyze entire Salesforce org with all models
+        
+        Args:
+            metadata: Dict containing org data (users, profiles, permission_sets, etc.)
+            
+        Returns:
+            Dict with AI-powered insights from all models
+        """
+        if not self.available:
+            return {
+                'available': False,
+                'message': 'AI models not available'
+            }
+        
+        results = {
+            'available': True,
+            'code_analysis': [],
+            'permission_analysis': [],
+            'security_analysis': [],
+            'anomaly_analysis': [],  # NEW
+            'overall_risk_score': 0,
+            'ai_recommendations': []
+        }
+        
+        try:
+            # 1. Analyze Apex code (if available)
+            if 'apex_classes' in metadata and metadata['apex_classes']:
+                logger.info("🔍 Analyzing Apex code with CodeBERT...")
+                results['code_analysis'] = self.codebert.analyze_apex_code(
+                    metadata['apex_classes']
+                )
+            
+            # 2. Analyze permissions with RoBERTa
+            if 'permission_sets' in metadata or 'profiles' in metadata:
+                logger.info("🔍 Analyzing permissions with RoBERTa...")
+                results['permission_analysis'] = self.roberta.analyze_permissions(
+                    permission_sets=metadata.get('permission_sets', []),
+                    profiles=metadata.get('profiles', [])
+                )
+            
+            # 3. Analyze security policies with SecBERT
+            logger.info("🔍 Analyzing security policies with SecBERT...")
+            results['security_analysis'] = self.secbert.analyze_security(
+                metadata=metadata
+            )
+            
+            # 4. Detect anomalies with Isolation Forest (NEW)
+            if self.anomaly:
+                logger.info("🔍 Detecting behavioral anomalies...")
+                
+                # Login anomalies
+                login_anomalies = self.anomaly.detect_login_anomalies(
+                    metadata.get('login_history', [])
+                )
+                
+                # Permission anomalies
+                perm_anomalies = self.anomaly.detect_permission_anomalies(
+                    users=metadata.get('users', []),
+                    permission_sets=metadata.get('permission_sets', [])
+                )
+                
+                # Dormant accounts
+                dormant_anomalies = self.anomaly.detect_dormant_account_risks(
+                    metadata.get('users', [])
+                )
+                
+                results['anomaly_analysis'] = login_anomalies + perm_anomalies + dormant_anomalies
+                logger.info(f"   Found {len(results['anomaly_analysis'])} anomalies")
+            
+            # 5. Calculate overall AI risk score
+            results['overall_risk_score'] = self._calculate_ai_risk_score(results)
+            
+            # 6. Generate AI recommendations
+            results['ai_recommendations'] = self._generate_recommendations(results)
+            
+            logger.info(f"✅ AI Analysis complete - Risk Score: {results['overall_risk_score']}/100")
+            
+        except Exception as e:
+            logger.error(f"❌ AI analysis error: {e}")
+            results['error'] = str(e)
+        
+        return results
+    
+    def _calculate_ai_risk_score(self, results):
+        """Calculate overall risk score from all model outputs"""
+        score = 0
+        
+        # Code vulnerabilities
+        code_issues = len(results.get('code_analysis', []))
+        score += min(25, code_issues * 10)
+        
+        # Permission risks
+        high_risk_perms = len([p for p in results.get('permission_analysis', []) 
+                               if p.get('risk_level') in ['CRITICAL', 'HIGH']])
+        score += min(30, high_risk_perms * 8)
+        
+        # Security policy issues
+        security_issues = len(results.get('security_analysis', []))
+        score += min(25, security_issues * 5)
+        
+        # Anomalies (NEW)
+        anomaly_issues = len(results.get('anomaly_analysis', []))
+        high_anomalies = len([a for a in results.get('anomaly_analysis', [])
+                             if a.get('severity') == 'High'])
+        score += min(20, high_anomalies * 10 + (anomaly_issues - high_anomalies) * 3)
+        
+        return min(100, score)
+    
+    def _generate_recommendations(self, results):
+        """Generate AI-powered recommendations"""
+        recommendations = []
+        
+        if results.get('code_analysis'):
+            recommendations.append(
+                "🔍 Code vulnerabilities detected - Review Apex classes for security issues"
+            )
+        
+        if results.get('permission_analysis'):
+            high_risk = [p for p in results['permission_analysis'] 
+                        if p.get('risk_level') == 'CRITICAL']
+            if high_risk:
+                recommendations.append(
+                    f"⚠️ {len(high_risk)} critical permission risks - Implement least privilege"
+                )
+        
+        if results.get('security_analysis'):
+            recommendations.append(
+                "🛡️ Security policy gaps identified - Strengthen access controls"
+            )
+        
+        # NEW: Anomaly recommendations
+        if results.get('anomaly_analysis'):
+            high_anomalies = [a for a in results['anomaly_analysis']
+                            if a.get('severity') == 'High']
+            if high_anomalies:
+                recommendations.append(
+                    f"🚨 {len(high_anomalies)} high-risk behavioral anomalies - Investigate immediately"
+                )
+            
+            login_anomalies = [a for a in results['anomaly_analysis']
+                              if 'Login' in a.get('type', '')]
+            if login_anomalies:
+                recommendations.append(
+                    f"🔐 {len(login_anomalies)} unusual login patterns - Enable MFA and IP restrictions"
+                )
+        
+        if not recommendations:
+            recommendations.append("✅ No critical AI-detected issues found")
+        
+        return recommendations
+
+
+# ============================================================================
+# CODEBERT ANALYZER (For Apex Code)
+# ============================================================================
+
+class CodeBERTAnalyzer:
+    """
+    CodeBERT model for analyzing Apex code
+    Zero-shot vulnerability detection using embeddings
+    """
+    
+    def __init__(self):
+        self.model_name = "microsoft/codebert-base"
+        self.tokenizer = RobertaTokenizer.from_pretrained(self.model_name)
+        self.model = RobertaModel.from_pretrained(self.model_name)
+        self.model.eval()
+        
+        # Vulnerability patterns (embeddings will be compared against these)
+        self.vulnerability_patterns = {
+            'SOQL_INJECTION': [
+                'dynamic SOQL query with string concatenation',
+                'database query with user input without binding',
+                'SQL injection vulnerability in Salesforce'
+            ],
+            'MISSING_SHARING': [
+                'class without sharing keywords',
+                'missing with sharing declaration',
+                'no sharing enforcement in Apex'
+            ],
+            'HARDCODED_CREDENTIALS': [
+                'hardcoded API key in code',
+                'exposed credentials in source',
+                'plaintext password in class'
+            ],
+            'UNSAFE_DML': [
+                'DML operation without try catch',
+                'unhandled database operation',
+                'unsafe insert update delete'
+            ]
+        }
+        
+        # Pre-compute pattern embeddings
+        self.pattern_embeddings = self._compute_pattern_embeddings()
+    
+    def _compute_pattern_embeddings(self):
+        """Pre-compute embeddings for vulnerability patterns"""
+        embeddings = {}
+        
+        for vuln_type, patterns in self.vulnerability_patterns.items():
+            pattern_embs = []
+            for pattern in patterns:
+                emb = self._get_embedding(pattern)
+                pattern_embs.append(emb)
+            embeddings[vuln_type] = np.mean(pattern_embs, axis=0)
+        
+        return embeddings
+    
+    def _get_embedding(self, text):
+        """Get embedding for text using CodeBERT"""
+        inputs = self.tokenizer(
+            text,
+            return_tensors='pt',
+            max_length=512,
+            truncation=True,
+            padding=True
+        )
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            # Use [CLS] token embedding
+            embedding = outputs.last_hidden_state[:, 0, :].numpy()
+        
+        return embedding[0]
+    
+    def analyze_code_snippet(self, code):
+        """Analyze a single code snippet"""
+        # Get code embedding
+        code_embedding = self._get_embedding(code)
+        
+        # Compare with vulnerability patterns
+        vulnerabilities = []
+        
+        for vuln_type, pattern_emb in self.pattern_embeddings.items():
+            similarity = cosine_similarity(
+                code_embedding.reshape(1, -1),
+                pattern_emb.reshape(1, -1)
+            )[0][0]
+            
+            # Threshold for detection (tune this based on testing)
+            if similarity > 0.7:
+                vulnerabilities.append({
+                    'type': vuln_type,
+                    'confidence': float(similarity),
+                    'severity': 'HIGH' if similarity > 0.85 else 'MEDIUM'
+                })
+        
+        # Also use pattern matching as backup
+        pattern_matches = self._pattern_match(code)
+        vulnerabilities.extend(pattern_matches)
+        
+        return vulnerabilities
+    
+    def _pattern_match(self, code):
+        """Simple pattern matching for common issues"""
+        issues = []
+        
+        if 'Database.query(' in code and '+' in code:
+            issues.append({
+                'type': 'POTENTIAL_SOQL_INJECTION',
+                'confidence': 0.9,
+                'severity': 'CRITICAL',
+                'description': 'Dynamic SOQL with string concatenation detected'
+            })
+        
+        if ('public class' in code and 
+            'with sharing' not in code and 
+            'without sharing' not in code):
+            issues.append({
+                'type': 'MISSING_SHARING',
+                'confidence': 0.95,
+                'severity': 'HIGH',
+                'description': 'Class missing sharing declaration'
+            })
+        
+        # Check for potential hardcoded credentials
+        credential_keywords = ['password', 'apikey', 'api_key', 'secret', 'token']
+        for keyword in credential_keywords:
+            if f'{keyword} =' in code.lower() or f'{keyword}=' in code.lower():
+                issues.append({
+                    'type': 'POTENTIAL_HARDCODED_CREDENTIALS',
+                    'confidence': 0.8,
+                    'severity': 'CRITICAL',
+                    'description': f'Possible hardcoded {keyword} found'
+                })
+                break
+        
+        return issues
+    
+    def analyze_apex_code(self, apex_classes):
+        """Analyze multiple Apex classes"""
+        results = []
+        
+        for apex_class in apex_classes[:10]:  # Limit to avoid timeout
+            class_name = apex_class.get('Name', 'Unknown')
+            code = apex_class.get('Body', '')
+            
+            if not code:
+                continue
+            
+            vulnerabilities = self.analyze_code_snippet(code)
+            
+            if vulnerabilities:
+                results.append({
+                    'class_name': class_name,
+                    'vulnerabilities': vulnerabilities,
+                    'total_issues': len(vulnerabilities)
+                })
+        
+        return results
+
+
+# ============================================================================
+# ROBERTA ANALYZER (For Permission Classification)
+# ============================================================================
+
+class RoBERTaAnalyzer:
+    """
+    RoBERTa model for permission risk classification
+    Zero-shot classification using semantic similarity
+    """
+    
+    def __init__(self):
+        self.model_name = "roberta-base"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModel.from_pretrained(self.model_name)
+        self.model.eval()
+        
+        # Risk level definitions
+        self.risk_definitions = {
+            'CRITICAL': [
+                'full system access with modify all data',
+                'administrative privileges with user management',
+                'unrestricted access to all records',
+                'complete control over organization data'
+            ],
+            'HIGH': [
+                'elevated permissions with data modification',
+                'access to sensitive user information',
+                'ability to change security settings',
+                'export capabilities for all data'
+            ],
+            'MEDIUM': [
+                'limited administrative functions',
+                'read access to multiple objects',
+                'standard user permissions with extras',
+                'moderate data access rights'
+            ],
+            'LOW': [
+                'basic user permissions',
+                'read-only access to owned records',
+                'minimal system privileges',
+                'restricted data access'
+            ]
+        }
+        
+        # Pre-compute risk embeddings
+        self.risk_embeddings = self._compute_risk_embeddings()
+    
+    def _compute_risk_embeddings(self):
+        """Pre-compute embeddings for risk levels"""
+        embeddings = {}
+        
+        for risk_level, descriptions in self.risk_definitions.items():
+            risk_embs = []
+            for desc in descriptions:
+                emb = self._get_embedding(desc)
+                risk_embs.append(emb)
+            embeddings[risk_level] = np.mean(risk_embs, axis=0)
+        
+        return embeddings
+    
+    def _get_embedding(self, text):
+        """Get embedding using RoBERTa"""
+        inputs = self.tokenizer(
+            text,
+            return_tensors='pt',
+            max_length=512,
+            truncation=True,
+            padding=True
+        )
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            embedding = outputs.last_hidden_state[:, 0, :].numpy()
+        
+        return embedding[0]
+    
+    def classify_permission_set(self, permission_set):
+        """Classify a single permission set's risk level"""
+        
+        # Build description from permissions
+        dangerous_perms = []
+        
+        if permission_set.get('PermissionsModifyAllData'):
+            dangerous_perms.append('modify all data')
+        if permission_set.get('PermissionsViewAllData'):
+            dangerous_perms.append('view all data')
+        if permission_set.get('PermissionsManageUsers'):
+            dangerous_perms.append('manage users')
+        if permission_set.get('PermissionsAuthorApex'):
+            dangerous_perms.append('author apex code')
+        
+        if not dangerous_perms:
+            return {
+                'name': permission_set.get('Name', 'Unknown'),
+                'risk_level': 'LOW',
+                'confidence': 0.9,
+                'dangerous_permissions': []
+            }
+        
+        # Create description
+        description = f"permission set with {', '.join(dangerous_perms)}"
+        
+        # Get embedding
+        perm_embedding = self._get_embedding(description)
+        
+        # Compare with risk levels
+        similarities = {}
+        for risk_level, risk_emb in self.risk_embeddings.items():
+            similarity = cosine_similarity(
+                perm_embedding.reshape(1, -1),
+                risk_emb.reshape(1, -1)
+            )[0][0]
+            similarities[risk_level] = similarity
+        
+        # Get highest similarity
+        predicted_risk = max(similarities, key=similarities.get)
+        confidence = similarities[predicted_risk]
+        
+        return {
+            'name': permission_set.get('Name', 'Unknown'),
+            'risk_level': predicted_risk,
+            'confidence': float(confidence),
+            'dangerous_permissions': dangerous_perms
+        }
+    
+    def analyze_permissions(self, permission_sets, profiles):
+        """Analyze all permission sets and profiles"""
+        results = []
+        
+        # Analyze permission sets
+        for perm_set in permission_sets[:20]:  # Limit for performance
+            result = self.classify_permission_set(perm_set)
+            result['type'] = 'Permission Set'
+            results.append(result)
+        
+        # Analyze profiles
+        for profile in profiles[:10]:
+            result = self.classify_permission_set(profile)
+            result['type'] = 'Profile'
+            results.append(result)
+        
+        return results
+
+
+# ============================================================================
+# SECBERT ANALYZER (For Security Policies)
+# ============================================================================
+
+class SecBERTAnalyzer:
+    """
+    SecBERT model for security policy analysis
+    Detects security misconfigurations and policy violations
+    """
+    
+    def __init__(self):
+        # SecBERT might not be available, fallback to RoBERTa
+        try:
+            self.model_name = "jackaduma/SecBERT"
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+            self.model = AutoModel.from_pretrained(self.model_name)
+        except:
+            logger.warning("⚠️ SecBERT not available, using RoBERTa as fallback")
+            self.model_name = "roberta-base"
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+            self.model = AutoModel.from_pretrained(self.model_name)
+        
+        self.model.eval()
+        
+        # Security violation patterns
+        self.security_patterns = {
+            'MFA_NOT_ENFORCED': [
+                'multi-factor authentication not enabled',
+                'no MFA requirement for users',
+                'missing two-factor authentication'
+            ],
+            'EXCESSIVE_ADMINS': [
+                'too many administrative accounts',
+                'excessive system administrator privileges',
+                'multiple users with full access'
+            ],
+            'DORMANT_ACCOUNTS': [
+                'inactive user accounts with permissions',
+                'dormant administrative accounts',
+                'unused accounts with access rights'
+            ],
+            'WEAK_PASSWORD_POLICY': [
+                'insufficient password requirements',
+                'weak password complexity rules',
+                'no password expiration policy'
+            ]
+        }
+        
+        self.pattern_embeddings = self._compute_pattern_embeddings()
+    
+    def _compute_pattern_embeddings(self):
+        """Pre-compute embeddings for security patterns"""
+        embeddings = {}
+        
+        for pattern_type, descriptions in self.security_patterns.items():
+            pattern_embs = []
+            for desc in descriptions:
+                emb = self._get_embedding(desc)
+                pattern_embs.append(emb)
+            embeddings[pattern_type] = np.mean(pattern_embs, axis=0)
+        
+        return embeddings
+    
+    def _get_embedding(self, text):
+        """Get embedding using SecBERT/RoBERTa"""
+        inputs = self.tokenizer(
+            text,
+            return_tensors='pt',
+            max_length=512,
+            truncation=True,
+            padding=True
+        )
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+            embedding = outputs.last_hidden_state[:, 0, :].numpy()
+        
+        return embedding[0]
+    
+    def analyze_security(self, metadata):
+        """Analyze security policies and configurations"""
+        findings = []
+        
+        users = metadata.get('users', [])
+        profiles = metadata.get('profiles', [])
+        
+        # Check MFA compliance
+        mfa_result = self._check_mfa_compliance(users)
+        if mfa_result:
+            findings.append(mfa_result)
+        
+        # Check admin count
+        admin_result = self._check_admin_count(users, profiles)
+        if admin_result:
+            findings.append(admin_result)
+        
+        # Check dormant accounts
+        dormant_result = self._check_dormant_accounts(users)
+        if dormant_result:
+            findings.append(dormant_result)
+        
+        return findings
+    
+    def _check_mfa_compliance(self, users):
+        """Check MFA compliance using AI"""
+        # Simple heuristic for demo
+        total_users = len(users)
+        if total_users == 0:
+            return None
+        
+        # In real scenario, check MfaEnabled field
+        # For demo, create finding
+        description = f"multi-factor authentication compliance check for {total_users} users"
+        emb = self._get_embedding(description)
+        
+        mfa_pattern = self.pattern_embeddings['MFA_NOT_ENFORCED']
+        similarity = cosine_similarity(
+            emb.reshape(1, -1),
+            mfa_pattern.reshape(1, -1)
+        )[0][0]
+        
+        if similarity > 0.6:
+            return {
+                'type': 'MFA_NOT_ENFORCED',
+                'severity': 'HIGH',
+                'confidence': float(similarity),
+                'description': 'MFA may not be enforced for all users',
+                'recommendation': 'Enable MFA for all users, especially admins'
+            }
+        
+        return None
+    
+    def _check_admin_count(self, users, profiles):
+        """Check for excessive administrators"""
+        admin_count = len([u for u in users if 'Admin' in str(u.get('Profile', {}))])
+        
+        if admin_count > 5:
+            description = f"{admin_count} administrative users with full system access"
+            emb = self._get_embedding(description)
+            
+            pattern = self.pattern_embeddings['EXCESSIVE_ADMINS']
+            similarity = cosine_similarity(
+                emb.reshape(1, -1),
+                pattern.reshape(1, -1)
+            )[0][0]
+            
+            return {
+                'type': 'EXCESSIVE_ADMINS',
+                'severity': 'MEDIUM',
+                'confidence': float(similarity),
+                'admin_count': admin_count,
+                'description': f'{admin_count} users have System Administrator profile',
+                'recommendation': 'Reduce admin count, use Permission Sets instead'
+            }
+        
+        return None
+    
+    def _check_dormant_accounts(self, users):
+        """Check for dormant user accounts"""
+        # Simple check - in production, check LastLoginDate
+        inactive_count = len([u for u in users if not u.get('IsActive', True)])
+        
+        if inactive_count > 0:
+            description = f"{inactive_count} inactive user accounts still present"
+            emb = self._get_embedding(description)
+            
+            pattern = self.pattern_embeddings['DORMANT_ACCOUNTS']
+            similarity = cosine_similarity(
+                emb.reshape(1, -1),
+                pattern.reshape(1, -1)
+            )[0][0]
+            
+            return {
+                'type': 'DORMANT_ACCOUNTS',
+                'severity': 'LOW',
+                'confidence': float(similarity),
+                'inactive_count': inactive_count,
+                'description': f'{inactive_count} inactive accounts found',
+                'recommendation': 'Review and remove dormant accounts'
+            }
+        
+        return None
+
+
+# ============================================================================
+# HELPER FUNCTIONS FOR INTEGRATION
+# ============================================================================
+
+def get_ai_summary(analysis_results):
+    """Generate human-readable summary from AI analysis"""
+    
+    if not analysis_results.get('available'):
+        return "AI analysis not available"
+    
+    summary_parts = []
+    
+    # Code analysis summary
+    if analysis_results.get('code_analysis'):
+        code_issues = len(analysis_results['code_analysis'])
+        summary_parts.append(f"Detected {code_issues} code vulnerabilities")
+    
+    # Permission analysis summary
+    if analysis_results.get('permission_analysis'):
+        critical = len([p for p in analysis_results['permission_analysis'] 
+                       if p.get('risk_level') == 'CRITICAL'])
+        if critical > 0:
+            summary_parts.append(f"Found {critical} critical permission risks")
+    
+    # Security analysis summary
+    if analysis_results.get('security_analysis'):
+        security_issues = len(analysis_results['security_analysis'])
+        summary_parts.append(f"Identified {security_issues} security policy gaps")
+    
+    # NEW: Anomaly analysis summary
+    if analysis_results.get('anomaly_analysis'):
+        anomaly_count = len(analysis_results['anomaly_analysis'])
+        high_anomalies = len([a for a in analysis_results['anomaly_analysis']
+                             if a.get('severity') == 'High'])
+        if high_anomalies > 0:
+            summary_parts.append(f"Detected {high_anomalies} high-risk behavioral anomalies")
+        elif anomaly_count > 0:
+            summary_parts.append(f"Detected {anomaly_count} behavioral anomalies")
+    
+    if not summary_parts:
+        return "AI analysis completed - no critical issues detected"
+    
+    return ". ".join(summary_parts) + "."
+
+
+# ============================================================================
+# EXAMPLE USAGE
+# ============================================================================
+
+if __name__ == "__main__":
+    
+    print("=" * 80)
+    print("MULTI-MODEL AI ANALYZER - TEST")
+    print("=" * 80)
+    
+    # Initialize analyzer
+    analyzer = MultiModelAnalyzer()
+    
+    if not analyzer.available:
+        print("❌ AI models failed to load")
+        exit(1)
+    
+    # Test data
+    test_metadata = {
+        'apex_classes': [
+            {
+                'Name': 'UnsafeController',
+                'Body': '''
+                public class UnsafeController {
+                    public void queryData(String input) {
+                        String query = 'SELECT Id FROM User WHERE Name = \\'' + input + '\\'';
+                        Database.query(query);
+                    }
+                }
+                '''
+            }
+        ],
+        'permission_sets': [
+            {
+                'Name': 'AdminPermSet',
+                'PermissionsModifyAllData': True,
+                'PermissionsViewAllData': True,
+                'PermissionsManageUsers': True
+            }
+        ],
+        'profiles': [
+            {
+                'Name': 'System Administrator',
+                'PermissionsModifyAllData': True,
+                'PermissionsViewAllData': True
+            }
+        ],
+        'users': [
+            {'Id': '1', 'IsActive': True},
+            {'Id': '2', 'IsActive': True},
+            {'Id': '3', 'IsActive': False}
+        ],
+        'login_history': []
+    }
+    
+    # Run analysis
+    print("\n🔍 Running AI analysis...")
+    results = analyzer.analyze_org(test_metadata)
+    
+    # Display results
+    print("\n📊 RESULTS:")
+    print(f"   Overall AI Risk Score: {results['overall_risk_score']}/100")
+    print(f"   Code Issues: {len(results.get('code_analysis', []))}")
+    print(f"   Permission Risks: {len(results.get('permission_analysis', []))}")
+    print(f"   Security Findings: {len(results.get('security_analysis', []))}")
+    print(f"   Anomalies Detected: {len(results.get('anomaly_analysis', []))}")
+    
+    print("\n💡 AI Recommendations:")
+    for rec in results.get('ai_recommendations', []):
+        print(f"   • {rec}")
+    
+    print("\n" + "=" * 80)
+    print("✅ Multi-Model AI Analysis Complete!")
+    print("=" * 80)

anomaly_detector.py

@@ -0,0 +1,345 @@
+"""
+anomaly_detector.py - Behavioral Anomaly Detection
+Add this file to detect unusual login patterns and permission usage
+
+Usage:
+    from anomaly_detector import AnomalyDetector
+    
+    detector = AnomalyDetector()
+    anomalies = detector.detect_login_anomalies(login_history)
+"""
+
+import numpy as np
+from sklearn.ensemble import IsolationForest
+from datetime import datetime, timedelta
+import logging
+
+logger = logging.getLogger(__name__)
+
+class AnomalyDetector:
+    """
+    Detects anomalous behavior in Salesforce org using Isolation Forest
+    """
+    
+    def __init__(self):
+        """Initialize anomaly detector"""
+        self.login_detector = IsolationForest(
+            contamination=0.1,  # Expect ~10% anomalies
+            random_state=42
+        )
+        self.available = True
+        logger.info("✅ Anomaly Detector initialized")
+    
+    def detect_login_anomalies(self, login_history: list) -> list:
+        """
+        Detect anomalous login patterns
+        
+        Args:
+            login_history: List of login records
+            
+        Returns:
+            List of anomaly findings
+        """
+        if not login_history or len(login_history) < 10:
+            return []
+        
+        findings = []
+        
+        try:
+            # Extract features from login history
+            features = self._extract_login_features(login_history)
+            
+            if len(features) < 10:
+                return []
+            
+            # Fit and predict anomalies
+            predictions = self.login_detector.fit_predict(features)
+            scores = self.login_detector.score_samples(features)
+            
+            # Find anomalies (prediction = -1)
+            for idx, (pred, score) in enumerate(zip(predictions, scores)):
+                if pred == -1:  # Anomaly detected
+                    login = login_history[idx]
+                    
+                    # Determine anomaly type
+                    anomaly_type = self._classify_login_anomaly(login, login_history)
+                    
+                    findings.append({
+                        'type': f'Anomalous Login - {anomaly_type}',
+                        'severity': 'High' if score < -0.5 else 'Medium',
+                        'user_id': login.get('UserId', 'Unknown'),
+                        'login_time': login.get('LoginTime', 'Unknown'),
+                        'source_ip': login.get('SourceIp', 'Unknown'),
+                        'anomaly_score': float(score),
+                        'description': f'Unusual {anomaly_type.lower()} detected',
+                        'impact': 'Potential account compromise or unauthorized access',
+                        'recommendation': 'Investigate login context, verify with user, consider MFA'
+                    })
+            
+            logger.info(f"🔍 Detected {len(findings)} login anomalies")
+            
+        except Exception as e:
+            logger.error(f"❌ Anomaly detection error: {e}")
+        
+        return findings
+    
+    def _extract_login_features(self, login_history: list) -> np.ndarray:
+        """
+        Extract numerical features from login history
+        
+        Features:
+        - Hour of day (0-23)
+        - Day of week (0-6)
+        - Login success (0/1)
+        - IP address hash (numeric representation)
+        """
+        features = []
+        
+        for login in login_history:
+            try:
+                login_time = login.get('LoginTime', '')
+                if not login_time:
+                    continue
+                
+                # Parse timestamp
+                dt = datetime.fromisoformat(login_time.replace('Z', '+00:00'))
+                
+                # Extract features
+                hour = dt.hour
+                day_of_week = dt.weekday()
+                is_success = 1 if login.get('Status') == 'Success' else 0
+                
+                # Simple IP hash (just use last octet for demo)
+                ip = login.get('SourceIp', '0.0.0.0')
+                ip_hash = hash(ip) % 1000
+                
+                features.append([hour, day_of_week, is_success, ip_hash])
+                
+            except Exception as e:
+                continue
+        
+        return np.array(features) if features else np.array([])
+    
+    def _classify_login_anomaly(self, login: dict, all_logins: list) -> str:
+        """
+        Classify the type of anomaly detected
+        """
+        login_time = login.get('LoginTime', '')
+        source_ip = login.get('SourceIp', '')
+        status = login.get('Status', '')
+        
+        if not login_time:
+            return 'Unknown'
+        
+        try:
+            dt = datetime.fromisoformat(login_time.replace('Z', '+00:00'))
+            hour = dt.hour
+            
+            # Check time-based anomalies
+            if hour < 6 or hour > 22:
+                return 'Off-Hours Access'
+            
+            # Check IP-based anomalies
+            common_ips = [l.get('SourceIp') for l in all_logins]
+            if source_ip not in common_ips[:10]:  # Not in top 10 IPs
+                return 'Unusual Location'
+            
+            # Check status-based anomalies
+            if status == 'Failed':
+                return 'Failed Login Attempt'
+            
+            return 'Unusual Pattern'
+            
+        except Exception:
+            return 'Unknown'
+    
+    def detect_permission_anomalies(self, users: list, permission_sets: list) -> list:
+        """
+        Detect users with anomalous permission combinations
+        
+        Args:
+            users: List of user records
+            permission_sets: List of permission set assignments
+            
+        Returns:
+            List of permission anomaly findings
+        """
+        findings = []
+        
+        try:
+            # Count permissions per user
+            user_perm_counts = {}
+            
+            for ps in permission_sets:
+                user_id = ps.get('AssigneeId')
+                if user_id:
+                    user_perm_counts[user_id] = user_perm_counts.get(user_id, 0) + 1
+            
+            if not user_perm_counts:
+                return []
+            
+            # Convert to array for anomaly detection
+            perm_counts = np.array(list(user_perm_counts.values())).reshape(-1, 1)
+            
+            if len(perm_counts) < 5:
+                return []
+            
+            # Detect anomalies
+            detector = IsolationForest(contamination=0.1, random_state=42)
+            predictions = detector.fit_predict(perm_counts)
+            
+            # Find users with anomalous permission counts
+            for user_id, perm_count in user_perm_counts.items():
+                idx = list(user_perm_counts.keys()).index(user_id)
+                
+                if predictions[idx] == -1:  # Anomaly
+                    # Find user details
+                    user = next((u for u in users if u.get('Id') == user_id), {})
+                    
+                    findings.append({
+                        'type': 'Anomalous Permission Assignment',
+                        'severity': 'Medium',
+                        'user_id': user_id,
+                        'username': user.get('Username', 'Unknown'),
+                        'permission_count': perm_count,
+                        'description': f'User has {perm_count} permission sets (unusual)',
+                        'impact': 'Potential privilege escalation or excessive access',
+                        'recommendation': 'Review permission assignments, ensure least privilege'
+                    })
+            
+            logger.info(f"🔍 Detected {len(findings)} permission anomalies")
+            
+        except Exception as e:
+            logger.error(f"❌ Permission anomaly detection error: {e}")
+        
+        return findings
+    
+    def detect_dormant_account_risks(self, users: list) -> list:
+        """
+        Detect dormant accounts with elevated privileges
+        
+        Args:
+            users: List of user records
+            
+        Returns:
+            List of dormant account findings
+        """
+        findings = []
+        threshold_days = 90
+        threshold_date = datetime.now() - timedelta(days=threshold_days)
+        
+        for user in users:
+            last_login = user.get('LastLoginDate')
+            profile = user.get('Profile', {}).get('Name', '')
+            
+            # Check for dormant admin accounts
+            if 'Admin' in profile or 'System Administrator' in profile:
+                
+                if not last_login:
+                    findings.append({
+                        'type': 'Dormant Admin Account - Never Used',
+                        'severity': 'High',
+                        'user_id': user.get('Id'),
+                        'username': user.get('Username'),
+                        'profile': profile,
+                        'description': 'Admin account never logged in',
+                        'impact': 'Orphaned privileged account - prime target for attackers',
+                        'recommendation': 'Deactivate immediately or remove admin privileges'
+                    })
+                
+                elif isinstance(last_login, str):
+                    try:
+                        last_login_date = datetime.fromisoformat(last_login.replace('Z', '+00:00'))
+                        
+                        if last_login_date < threshold_date:
+                            days_inactive = (datetime.now() - last_login_date).days
+                            
+                            findings.append({
+                                'type': 'Dormant Admin Account',
+                                'severity': 'High',
+                                'user_id': user.get('Id'),
+                                'username': user.get('Username'),
+                                'profile': profile,
+                                'days_inactive': days_inactive,
+                                'last_login': last_login,
+                                'description': f'Admin account inactive for {days_inactive} days',
+                                'impact': 'Orphaned privileged account - security risk',
+                                'recommendation': 'Deactivate or remove admin privileges'
+                            })
+                    except Exception:
+                        pass
+        
+        logger.info(f"🔍 Detected {len(findings)} dormant account risks")
+        return findings
+
+
+# ============================================================================
+# INTEGRATION HELPER
+# ============================================================================
+
+def analyze_with_anomaly_detection(metadata: dict) -> dict:
+    """
+    Run anomaly detection on org metadata
+    
+    Args:
+        metadata: Org metadata from Salesforce
+        
+    Returns:
+        Anomaly analysis results
+    """
+    detector = AnomalyDetector()
+    
+    findings = []
+    
+    # Detect login anomalies
+    login_findings = detector.detect_login_anomalies(
+        metadata.get('login_history', [])
+    )
+    findings.extend(login_findings)
+    
+    # Detect permission anomalies
+    perm_findings = detector.detect_permission_anomalies(
+        users=metadata.get('users', []),
+        permission_sets=metadata.get('permission_sets', [])
+    )
+    findings.extend(perm_findings)
+    
+    # Detect dormant account risks
+    dormant_findings = detector.detect_dormant_account_risks(
+        metadata.get('users', [])
+    )
+    findings.extend(dormant_findings)
+    
+    return {
+        'findings': findings,
+        'total_anomalies': len(findings),
+        'login_anomalies': len(login_findings),
+        'permission_anomalies': len(perm_findings),
+        'dormant_accounts': len(dormant_findings)
+    }
+
+
+if __name__ == '__main__':
+    # Test
+    print("=" * 80)
+    print("ANOMALY DETECTOR - TEST")
+    print("=" * 80)
+    
+    detector = AnomalyDetector()
+    
+    # Test login data
+    test_logins = [
+        {'UserId': '1', 'LoginTime': '2026-01-19T09:00:00Z', 'SourceIp': '192.168.1.1', 'Status': 'Success'},
+        {'UserId': '2', 'LoginTime': '2026-01-19T03:00:00Z', 'SourceIp': '10.0.0.1', 'Status': 'Success'},  # Anomaly: 3 AM
+        {'UserId': '3', 'LoginTime': '2026-01-19T10:00:00Z', 'SourceIp': '192.168.1.1', 'Status': 'Success'},
+    ] * 5  # Repeat to have enough data
+    
+    anomalies = detector.detect_login_anomalies(test_logins)
+    
+    print(f"\n✅ Detected {len(anomalies)} anomalies")
+    for anomaly in anomalies:
+        print(f"   • {anomaly['type']} - {anomaly['description']}")
+    
+    print("\n" + "=" * 80)
+    print("✅ Anomaly Detection Test Complete!")
+    print("=" * 80)

app.json

@@ -0,0 +1,25 @@
+{
+  "name": "Optimax Security Agent",
+  "description": "AI-powered Salesforce security analysis",
+  "keywords": ["salesforce", "security", "ai"],
+  "formation": {
+    "web": {
+      "quantity": 1,
+      "size": "standard-2x"
+    }
+  },
+  "env": {
+    "SF_CLIENT_ID": {
+      "description": "Your Salesforce Client ID",
+      "required": true
+    },
+    "SF_CLIENT_SECRET": {
+      "description": "Your Salesforce Client Secret",
+      "required": true
+    },
+    "SF_REDIRECT_URI": {
+      "description": "OAuth Redirect URI",
+      "value": "https://login.salesforce.com/services/oauth2/success"
+    }
+  }
+}

app.py

@@ -0,0 +1,787 @@
+import gradio as gr
+import json
+import logging
+from datetime import datetime, timezone
+from simple_salesforce import Salesforce
+import requests
+from urllib.parse import urlencode, urlparse, parse_qs, unquote
+import secrets
+import time
+
+# Import AI models
+from ai_models import MultiModelAnalyzer, get_ai_summary
+
+# Import analyzers
+from permission_analyzer import PermissionAnalyzer
+from identity_analyzer import IdentityAnalyzer
+from sharing_analyzer import SharingAnalyzer
+
+# Import report generator
+from optimax_reports import generate_report
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# ============================================================================
+# OAUTH CONFIGURATION
+# ============================================================================
+
+SF_CLIENT_ID = "3MVG9VMBZCsTL9hmy3bhf4_UX7eXivaplob0liijsZucnNjPHD1yTL4J6mxXSN42BHh9wwOhQLbrA_vsl.oqc"
+SF_CLIENT_SECRET = "4AC7C18215B1312D5148278BDC01D327DE8BB282DF9C5B697733DAF0E47A736E"
+SF_REDIRECT_URI = "https://login.salesforce.com/services/oauth2/success"
+
+# Session storage
+OAUTH_SESSIONS = {}
+
+# ============================================================================
+# INITIALIZE COMPONENTS
+# ============================================================================
+
+logger.info("=" * 80)
+logger.info("🚀 INITIALIZING OPTIMAX SECURITY AGENT")
+logger.info("=" * 80)
+
+# Initialize rule-based analyzers
+logger.info("📋 Loading rule-based analyzers...")
+perm_analyzer = PermissionAnalyzer()
+identity_analyzer = IdentityAnalyzer()
+sharing_analyzer = SharingAnalyzer()
+logger.info("✅ Rule-based analyzers loaded")
+
+# Initialize AI models
+logger.info("")
+logger.info("🤖 Loading Multi-Model AI Analyzer...")
+logger.info("   This may take 30-60 seconds on first run...")
+try:
+    ai_analyzer = MultiModelAnalyzer()
+    if ai_analyzer.available:
+        logger.info("✅ AI Models loaded successfully!")
+        logger.info("   • CodeBERT (125M) - Code vulnerability detection")
+        logger.info("   • RoBERTa (125M) - Permission risk classification")
+        logger.info("   • SecBERT (110M) - Security policy analysis")
+        logger.info("   • Isolation Forest - Behavioral anomaly detection")
+    else:
+        logger.warning("⚠️  AI models failed to load - using rule-based analysis only")
+        ai_analyzer = None
+except Exception as e:
+    logger.error(f"⚠️  AI models not available: {e}")
+    logger.warning("   Continuing with rule-based analysis only")
+    ai_analyzer = None
+
+logger.info("")
+logger.info("=" * 80)
+logger.info("✅ ALL COMPONENTS INITIALIZED")
+logger.info("=" * 80)
+
+# ============================================================================
+# OAUTH FUNCTIONS WITH AUTO CODE EXTRACTION
+# ============================================================================
+
+def check_credentials_configured():
+    """Check if OAuth credentials are properly configured"""
+    return "YOUR_" not in SF_CLIENT_ID and "YOUR_" not in SF_CLIENT_SECRET
+
+def generate_oauth_url(org_type: str) -> tuple:
+    """Generate OAuth URL for user authorization with JavaScript auto-extraction"""
+    
+    if not check_credentials_configured():
+        return "", "", "⚠️ OAuth credentials not configured"
+    
+    # Generate unique session ID
+    session_id = secrets.token_urlsafe(32)
+    OAUTH_SESSIONS[session_id] = {
+        "status": "pending",
+        "org_type": org_type,
+        "created_at": datetime.now(timezone.utc),
+        "code": None
+    }
+    
+    # Determine authorization endpoint
+    if org_type == "sandbox":
+        auth_endpoint = "https://test.salesforce.com/services/oauth2/authorize"
+        redirect_uri = "https://test.salesforce.com/services/oauth2/success"
+    else:
+        auth_endpoint = "https://login.salesforce.com/services/oauth2/authorize"
+        redirect_uri = SF_REDIRECT_URI
+    
+    # Build OAuth parameters
+    params = {
+        "response_type": "code",
+        "client_id": SF_CLIENT_ID,
+        "redirect_uri": redirect_uri,
+        "scope": "full refresh_token",
+        "state": session_id,  # Pass session ID for tracking
+        "prompt": "login"  # Force fresh login every time
+    }
+    
+    oauth_url = f"{auth_endpoint}?{urlencode(params)}"
+    
+    logger.info("=" * 80)
+    logger.info(f"🔗 Generated OAuth URL for {org_type.upper()}")
+    logger.info(f"   Session ID: {session_id}")
+    logger.info(f"   Redirect URI: {redirect_uri}")
+    logger.info("=" * 80)
+    
+    # Create instructions with JavaScript auto-extraction
+    instructions = f"""
+## ✅ Step 1: Click Authorization Link Below
+
+Click the **blue button** to open Salesforce authorization.
+
+---
+
+## 🔐 Step 2: Log In & Allow Access
+
+1. **Log into your {org_type.title()} org**
+2. Click **"Allow"** to grant permissions
+3. The authorization code will be **automatically detected!** ✨
+
+---
+
+## ⏳ Step 3: Wait for Automatic Processing
+
+Once you authorize:
+- 🔍 The code will be **automatically extracted** from the URL
+- 🚀 The security scan will **start automatically**
+- 📊 Results will appear below
+
+**No manual copying needed!** 🎉
+
+---
+
+**Session ID:** `{session_id}`
+"""
+    
+    return session_id, oauth_url, instructions
+
+def connect_production_org():
+    """Connect to Production/Developer Org"""
+    return generate_oauth_url("production")
+
+def connect_sandbox_org():
+    """Connect to Sandbox Org"""
+    return generate_oauth_url("sandbox")
+
+def exchange_code_for_token(code: str, org_type: str) -> dict:
+    """Exchange authorization code for access token"""
+    
+    # Determine token endpoint
+    if org_type == "sandbox":
+        token_endpoint = "https://test.salesforce.com/services/oauth2/token"
+        redirect_uri = "https://test.salesforce.com/services/oauth2/success"
+    else:
+        token_endpoint = "https://login.salesforce.com/services/oauth2/token"
+        redirect_uri = SF_REDIRECT_URI
+    
+    # Clean and decode the authorization code
+    clean_code = unquote(code.strip())
+    
+    logger.info(f"🔐 Exchanging code for {org_type} token...")
+    logger.info(f"   Code length: {len(clean_code)} chars")
+    
+    # Exchange code for token
+    response = requests.post(
+        token_endpoint,
+        data={
+            "grant_type": "authorization_code",
+            "client_id": SF_CLIENT_ID,
+            "client_secret": SF_CLIENT_SECRET,
+            "redirect_uri": redirect_uri,
+            "code": clean_code
+        },
+        headers={"Content-Type": "application/x-www-form-urlencoded"}
+    )
+    
+    if response.status_code != 200:
+        try:
+            error_data = response.json()
+            logger.error(f"❌ Token exchange failed: {error_data}")
+            return {
+                "error": error_data.get("error", "unknown_error"),
+                "error_description": error_data.get("error_description", "Token exchange failed")
+            }
+        except:
+            logger.error(f"❌ Token exchange failed with status {response.status_code}")
+            return {
+                "error": "token_exchange_failed",
+                "error_description": f"HTTP {response.status_code}"
+            }
+    
+    token_data = response.json()
+    logger.info("✅ Token exchange successful!")
+    
+    return token_data
+
+# ============================================================================
+# CORE ANALYSIS FUNCTIONS (same as original)
+# ============================================================================
+
+def extract_metadata(sf: Salesforce) -> dict:
+    """Extract metadata from Salesforce org"""
+    
+    logger.info("📡 Extracting org metadata...")
+    metadata = {}
+    
+    try:
+        # Get users
+        logger.info("   • Fetching users...")
+        users_query = """
+        SELECT Id, Username, Name, Email, IsActive, Profile.Name, LastLoginDate, 
+               CreatedDate, UserRole.Name
+        FROM User
+        WHERE IsActive = true
+        LIMIT 1000
+        """
+        users_result = sf.query(users_query)
+        metadata['users'] = users_result['records']
+        logger.info(f"     ✅ Retrieved {len(metadata['users'])} users")
+        
+        # Get permission sets
+        logger.info("   • Fetching permission sets...")
+        ps_query = """
+        SELECT Id, Name, Label, Description, IsOwnedByProfile,
+               PermissionsModifyAllData, PermissionsViewAllData, PermissionsManageUsers,
+               PermissionsCustomizeApplication, PermissionsAuthorApex,
+               (SELECT Id, AssigneeId, Assignee.Username, Assignee.Name, Assignee.Email
+                FROM Assignments)
+        FROM PermissionSet
+        WHERE IsOwnedByProfile = false
+        LIMIT 500
+        """
+        ps_result = sf.query(ps_query)
+        metadata['permission_sets'] = ps_result['records']
+        logger.info(f"     ✅ Retrieved {len(metadata['permission_sets'])} permission sets")
+        
+        # Get profiles  
+        logger.info("   • Fetching profiles...")
+        profiles_query = """
+        SELECT Id, Name, UserLicense.Name, IsCustom,
+               PermissionsModifyAllData, PermissionsViewAllData, PermissionsManageUsers,
+               PermissionsCustomizeApplication, PermissionsAuthorApex
+        FROM Profile
+        LIMIT 100
+        """
+        profiles_result = sf.query(profiles_query)
+        metadata['profiles'] = profiles_result['records']
+        logger.info(f"     ✅ Retrieved {len(metadata['profiles'])} profiles")
+        
+        # Get org info
+        logger.info("   • Fetching org info...")
+        org_query = "SELECT Id, Name, OrganizationType, InstanceName FROM Organization LIMIT 1"
+        org_result = sf.query(org_query)
+        if org_result['records']:
+            metadata['organization'] = org_result['records'][0]
+            logger.info(f"     ✅ Retrieved org info: {metadata['organization'].get('Name')}")
+        
+        # Try to get login history
+        try:
+            logger.info("   • Fetching login history...")
+            login_query = """
+            SELECT Id, UserId, LoginTime, SourceIp, Status, LoginType
+            FROM LoginHistory
+            WHERE LoginTime = LAST_N_DAYS:30
+            LIMIT 1000
+            """
+            login_result = sf.query(login_query)
+            metadata['login_history'] = login_result['records']
+            logger.info(f"     ✅ Retrieved {len(metadata['login_history'])} login records")
+        except Exception as e:
+            logger.warning(f"     ⚠️  Could not fetch login history: {e}")
+            metadata['login_history'] = []
+        
+        logger.info("✅ Metadata extraction complete!")
+        return metadata
+        
+    except Exception as e:
+        logger.error(f"❌ Error extracting metadata: {e}")
+        raise
+
+def calculate_risk_score(findings: list) -> tuple:
+    """Calculate overall risk score based on findings"""
+    
+    score = 0
+    severity_weights = {
+        'Critical': 30,
+        'High': 15,
+        'Medium': 5,
+        'Low': 1
+    }
+    
+    for finding in findings:
+        severity = finding.get('severity', 'Low')
+        score += severity_weights.get(severity, 0)
+    
+    # Normalize to 0-100
+    score = min(100, score)
+    
+    # Determine risk level
+    if score >= 70:
+        risk_level = "CRITICAL"
+    elif score >= 50:
+        risk_level = "HIGH"
+    elif score >= 30:
+        risk_level = "MEDIUM"
+    else:
+        risk_level = "LOW"
+    
+    return score, risk_level
+
+def analyze_org(metadata: dict) -> dict:
+    """Perform comprehensive security analysis"""
+    
+    logger.info("")
+    logger.info("=" * 80)
+    logger.info("🔍 STARTING SECURITY ANALYSIS")
+    logger.info("=" * 80)
+    
+    all_findings = []
+    
+    # Create user lookup for analyzers
+    user_lookup = {u.get('Id'): u for u in metadata.get('users', [])}
+    
+    # 1. Permission Analysis
+    logger.info("1️⃣  Analyzing permissions...")
+    perm_results = perm_analyzer.analyze_all_permissions(
+        metadata.get('permission_sets', []),
+        metadata.get('profiles', []),
+        metadata.get('users', [])
+    )
+    all_findings.extend(perm_results['findings'])
+    logger.info(f"   ✅ Found {len(perm_results['findings'])} permission issues")
+    
+    # 2. Identity Analysis
+    logger.info("2️⃣  Analyzing identity & access...")
+    identity_results = identity_analyzer.analyze_users(
+        metadata.get('users', []),
+        metadata.get('login_history', [])
+    )
+    all_findings.extend(identity_results['findings'])
+    logger.info(f"   ✅ Found {len(identity_results['findings'])} identity issues")
+    
+    # 3. Sharing Analysis
+    logger.info("3️⃣  Analyzing sharing model...")
+    sharing_results = sharing_analyzer.analyze_sharing_settings({
+        'organization_wide_defaults': [],
+        'role_hierarchy': [],
+        'sharing_rules': []
+    })
+    all_findings.extend(sharing_results['findings'])
+    logger.info(f"   ✅ Found {len(sharing_results['findings'])} sharing issues")
+    
+    # 4. AI Analysis
+    ai_summary = ""
+    ai_recommendations = []
+    
+    if ai_analyzer and ai_analyzer.available:
+        logger.info("4️⃣  Running AI analysis...")
+        try:
+            ai_analysis = ai_analyzer.analyze_security(all_findings, metadata)
+            ai_summary = ai_analysis.get('executive_summary', '')
+            ai_recommendations = ai_analysis.get('recommendations', [])
+            
+            for finding in all_findings:
+                if finding.get('type') in ai_analysis.get('ai_insights', {}):
+                    finding['ai_insight'] = ai_analysis['ai_insights'][finding['type']]
+            
+            logger.info(f"   ✅ AI analysis complete")
+        except Exception as e:
+            logger.warning(f"   ⚠️  AI analysis failed: {e}")
+    else:
+        logger.info("4️⃣  Skipping AI analysis (not available)")
+    
+    # Calculate risk score
+    risk_score, risk_level = calculate_risk_score(all_findings)
+    
+    # Organize findings by severity
+    findings_by_severity = {
+        'Critical': [f for f in all_findings if f.get('severity') == 'Critical'],
+        'High': [f for f in all_findings if f.get('severity') == 'High'],
+        'Medium': [f for f in all_findings if f.get('severity') == 'Medium'],
+        'Low': [f for f in all_findings if f.get('severity') == 'Low']
+    }
+    
+    logger.info("")
+    logger.info("=" * 80)
+    logger.info("✅ ANALYSIS COMPLETE")
+    logger.info(f"   Risk Score: {risk_score}/100 ({risk_level})")
+    logger.info(f"   Total Findings: {len(all_findings)}")
+    logger.info("=" * 80)
+    
+    return {
+        'success': True,
+        'overall_risk_score': risk_score,
+        'risk_level': risk_level,
+        'total_findings': len(all_findings),
+        'findings_by_severity': findings_by_severity,
+        'all_findings': all_findings,
+        'ai_executive_summary': ai_summary,
+        'ai_recommendations': ai_recommendations,
+        'metadata_summary': {
+            'total_users': len(metadata.get('users', [])),
+            'active_users': len([u for u in metadata.get('users', []) if u.get('IsActive')]),
+            'permission_sets': len(metadata.get('permission_sets', [])),
+            'profiles': len(metadata.get('profiles', [])),
+            'org_name': metadata.get('organization', {}).get('Name', 'Unknown')
+        }
+    }
+
+def scan_salesforce_org(session_id: str, auth_code_or_url: str):
+    """
+    Scan Salesforce org using authorization code
+    Accepts either the full URL or just the code
+    """
+    
+    logger.info("=" * 80)
+    logger.info(f"🚀 SCAN STARTED - Session: {session_id}")
+    logger.info("=" * 80)
+    
+    # Validate session
+    if session_id not in OAUTH_SESSIONS:
+        error_msg = "❌ Invalid session ID. Please click 'Connect' again."
+        logger.error(error_msg)
+        return f"<div style='color: red; padding: 20px;'>{error_msg}</div>", json.dumps({"error": error_msg}, indent=2), None
+    
+    session = OAUTH_SESSIONS[session_id]
+    org_type = session.get('org_type', 'production')
+    
+    # Extract code from URL if full URL was provided
+    auth_code = auth_code_or_url.strip()
+    
+    if 'login.salesforce.com' in auth_code or 'test.salesforce.com' in auth_code:
+        # Full URL provided - extract code
+        try:
+            parsed_url = urlparse(auth_code)
+            query_params = parse_qs(parsed_url.query)
+            
+            if 'code' in query_params:
+                auth_code = query_params['code'][0]
+                logger.info("✅ Extracted code from URL")
+            else:
+                error_msg = "❌ No authorization code found in URL"
+                logger.error(error_msg)
+                return f"<div style='color: red; padding: 20px;'>{error_msg}</div>", json.dumps({"error": error_msg}, indent=2), None
+        except Exception as e:
+            error_msg = f"❌ Failed to parse URL: {str(e)}"
+            logger.error(error_msg)
+            return f"<div style='color: red; padding: 20px;'>{error_msg}</div>", json.dumps({"error": error_msg}, indent=2), None
+    
+    if not auth_code:
+        error_msg = "❌ No authorization code provided"
+        logger.error(error_msg)
+        return f"<div style='color: red; padding: 20px;'>{error_msg}</div>", json.dumps({"error": error_msg}, indent=2), None
+    
+    try:
+        # Exchange code for token
+        token_data = exchange_code_for_token(auth_code, org_type)
+        
+        if 'error' in token_data:
+            error_msg = f"❌ {token_data.get('error_description', 'Authentication failed')}"
+            logger.error(error_msg)
+            return f"<div style='color: red; padding: 20px;'>{error_msg}</div>", json.dumps(token_data, indent=2), None
+        
+        # Connect to Salesforce
+        sf = Salesforce(
+            instance_url=token_data['instance_url'],
+            session_id=token_data['access_token']
+        )
+        
+        # Extract metadata
+        metadata = extract_metadata(sf)
+        
+        # Run analysis
+        analysis_results = analyze_org(metadata)
+        
+        # Generate HTML report
+        report_path = generate_report(analysis_results, metadata)
+        
+        # Read the report
+        with open(report_path, 'r') as f:
+            report_html = f.read()
+        
+        # Clean up session
+        if session_id in OAUTH_SESSIONS:
+            del OAUTH_SESSIONS[session_id]
+        
+        logger.info("✅ Scan complete!")
+        
+        return report_html, json.dumps(analysis_results, indent=2, default=str), report_path
+        
+    except Exception as e:
+        error_msg = f"❌ Analysis failed: {str(e)}"
+        logger.error(error_msg)
+        logger.exception("Full error:")
+        return f"<div style='color: red; padding: 20px;'>{error_msg}</div>", json.dumps({"error": str(e)}, indent=2), None
+
+# ============================================================================
+# JAVASCRIPT AUTO CODE EXTRACTION
+# ============================================================================
+
+AUTO_EXTRACT_JS = """
+<script>
+// Auto-extract authorization code from URL
+function autoExtractCode() {
+    // Check if we're on the success page
+    const currentUrl = window.location.href;
+    
+    // Look for code in URL parameters
+    const urlParams = new URLSearchParams(window.location.search);
+    const code = urlParams.get('code');
+    const state = urlParams.get('state');
+    
+    if (code && state) {
+        // Send code back to parent window
+        if (window.opener) {
+            window.opener.postMessage({
+                type: 'SALESFORCE_AUTH_CODE',
+                code: code,
+                state: state
+            }, '*');
+            
+            // Close this window
+            setTimeout(() => {
+                window.close();
+            }, 1000);
+        }
+    }
+}
+
+// Listen for auth codes from popup windows
+window.addEventListener('message', function(event) {
+    // Verify message is from expected source
+    if (event.data.type === 'SALESFORCE_AUTH_CODE') {
+        const code = event.data.code;
+        const state = event.data.state;
+        
+        // Find the session ID input and auth code input
+        const sessionInput = document.querySelector('input[label="📋 Session ID (Auto-filled)"]');
+        const codeInput = document.querySelector('textarea[label="🔑 Authorization Code"]');
+        
+        if (codeInput && code) {
+            // Auto-fill the code
+            codeInput.value = code;
+            
+            // Trigger input event so Gradio detects the change
+            const inputEvent = new Event('input', { bubbles: true });
+            codeInput.dispatchEvent(inputEvent);
+            
+            // Auto-click the scan button after a short delay
+            setTimeout(() => {
+                const scanButton = document.querySelector('button:contains("Start AI-Powered Security Scan")');
+                if (scanButton) {
+                    scanButton.click();
+                }
+            }, 500);
+            
+            console.log('✅ Authorization code auto-extracted and scan started!');
+        }
+    }
+});
+
+// Run on page load
+autoExtractCode();
+</script>
+"""
+
+# ============================================================================
+# GRADIO INTERFACE WITH AUTO EXTRACTION
+# ============================================================================
+
+ai_status = "✅ Active" if (ai_analyzer and ai_analyzer.available) else "⚠️ Not Available"
+
+with gr.Blocks(
+    title="Optimax Security Agent",
+    theme=gr.themes.Soft(primary_hue="purple"),
+    head=AUTO_EXTRACT_JS  # Inject JavaScript for auto-extraction
+) as demo:
+    
+    gr.Markdown("""
+    # 🛡️ Optimax Security Agent
+    
+    ## Multi-Model AI-Powered Security Scanner
+    
+    **Enhanced with Automatic Code Detection! 🎉**
+    """)
+    
+    with gr.Tab("🔍 Scan Your Org"):
+        gr.Markdown("""
+        ### Connect Your Salesforce Org
+        
+        Advanced AI-powered security analysis using 4 specialized models.
+        
+        🔒 **Your credentials stay in Salesforce · We only read metadata · AI + Rule-based analysis**
+        
+        ✨ **NEW: Authorization code is automatically detected from the URL!**
+        """)
+        
+        with gr.Row():
+            prod_button = gr.Button(
+                "🏢 Production / Developer Org",
+                variant="primary",
+                size="lg",
+                scale=1
+            )
+            sandbox_button = gr.Button(
+                "🧪 Sandbox Org",
+                variant="secondary",
+                size="lg",
+                scale=1
+            )
+        
+        instructions = gr.Markdown("")
+        oauth_url_hidden = gr.Textbox(visible=False)
+        
+        # Authorization link - opens in popup for auto-extraction
+        auth_link = gr.HTML("")
+        
+        gr.Markdown("---")
+        
+        with gr.Row():
+            session_id = gr.Textbox(
+                label="📋 Session ID (Auto-filled)",
+                interactive=False,
+                scale=1
+            )
+            auth_code = gr.Textbox(
+                label="🔑 Authorization Code (Auto-detected)",
+                placeholder="Will be automatically filled from the authorization popup...",
+                lines=2,
+                scale=2
+            )
+        
+        scan_button = gr.Button(
+            "🚀 Start AI-Powered Security Scan",
+            variant="primary",
+            size="lg"
+        )
+        
+        gr.Markdown("""
+        💡 **How it works:**
+        1. Click "Connect" → Opens authorization in new window
+        2. Authorize in Salesforce
+        3. Code is **automatically extracted** and scan starts! ✨
+        
+        If automatic detection fails, you can still paste the code manually.
+        """)
+        
+        gr.Markdown("---\n### 📊 Security Analysis Results")
+        
+        # Download Button
+        download_button = gr.File(
+            label="📥 Download Complete HTML Report",
+            visible=True,
+            interactive=False
+        )
+        
+        # HTML Report Display
+        report_html = gr.HTML(
+            label="Security Report",
+            value="<div style='text-align: center; padding: 40px; color: #6b7280;'>Your AI-powered security analysis will appear here...</div>"
+        )
+        
+        # Raw JSON
+        with gr.Accordion("🔍 View Raw JSON Data", open=False):
+            results = gr.Textbox(
+                label="Analysis Report (JSON)",
+                lines=15,
+                placeholder="Raw JSON data..."
+            )
+        
+        # Event handlers
+        def show_oauth_link(sid, url, inst):
+            if url:
+                # Open in popup window for auto-extraction
+                link_html = f'''
+                <div style="text-align: center; margin: 30px 0;">
+                    <a href="{url}" target="_blank" onclick="window.open(this.href, 'SalesforceAuth', 'width=600,height=700'); return false;" style="display: inline-block; padding: 15px 40px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; text-decoration: none; border-radius: 10px; font-size: 18px; font-weight: bold; box-shadow: 0 4px 6px rgba(0,0,0,0.2);">
+                        🚀 Open Salesforce to Authorize
+                    </a>
+                    <p style="margin-top: 15px; color: #666; font-size: 14px;">
+                        ✨ Authorization code will be automatically detected!
+                    </p>
+                </div>
+                '''
+                return link_html
+            return ""
+        
+        prod_button.click(
+            fn=connect_production_org,
+            outputs=[session_id, oauth_url_hidden, instructions]
+        ).then(
+            fn=show_oauth_link,
+            inputs=[session_id, oauth_url_hidden, instructions],
+            outputs=[auth_link]
+        )
+        
+        sandbox_button.click(
+            fn=connect_sandbox_org,
+            outputs=[session_id, oauth_url_hidden, instructions]
+        ).then(
+            fn=show_oauth_link,
+            inputs=[session_id, oauth_url_hidden, instructions],
+            outputs=[auth_link]
+        )
+        
+        scan_button.click(
+            fn=scan_salesforce_org,
+            inputs=[session_id, auth_code],
+            outputs=[report_html, results, download_button]
+        )
+    
+    with gr.Tab("ℹ️ About"):
+        ai_models = ["CodeBERT (125M)", "RoBERTa (125M)", "SecBERT (110M)", "Isolation Forest"] if (ai_analyzer and ai_analyzer.available) else []
+        
+        gr.Markdown(f"""
+        ## Optimax Security Agent
+        
+        ### 🤖 AI Models
+        **Status:** {ai_status}
+        
+        {'**Active Models:**' if ai_models else '**AI Models:** Not loaded'}
+        {chr(10).join(['- ' + model for model in ai_models]) if ai_models else ''}
+        
+        ### 🎯 Features
+        - 🤖 Multi-Model AI Analysis (4 specialized models)
+        - ✨ **Automatic Code Detection** (JavaScript-based)
+        - 📋 Rule-Based Vulnerability Detection
+        - 🔍 Permission & Access Analysis
+        - 👤 Identity Management Review
+        - 🔐 Sharing Model Assessment
+        - 🚨 Behavioral Anomaly Detection
+        - 📊 Hybrid Risk Scoring (AI + Rules)
+        - 💡 Actionable Recommendations
+        
+        ### 🔒 Security & Privacy
+        - ✅ OAuth 2.0 authentication
+        - ✅ Automatic code extraction (browser-side)
+        - ✅ Metadata-only scanning
+        - ✅ Zero credential storage
+        - ✅ No data persistence
+        
+        ### 📖 How It Works
+        1. Click "Connect" → Opens popup
+        2. Authorize in Salesforce
+        3. JavaScript auto-detects code from URL
+        4. Code auto-fills and scan starts
+        5. View comprehensive results
+        
+        **Version:** 3.3.0 (Auto Code Detection)  
+        **Last Updated:** January 2026
+        """)
+
+if __name__ == "__main__":
+    logger.info("")
+    logger.info("=" * 80)
+    logger.info("🌐 STARTING GRADIO INTERFACE")
+    logger.info("=" * 80)
+    logger.info(f"   AI Models: {ai_status}")
+    logger.info(f"   OAuth: ✅ Configured")
+    logger.info(f"   Mode: Auto code detection (JavaScript)")
+    logger.info("=" * 80)
+    logger.info("")
+    
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True
+    )

identity_analyzer.py

@@ -0,0 +1,216 @@
+# analyzers/identity_analyzer.py
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional
+
+class IdentityAnalyzer:
+    """
+    Analyzes identity and access management for security issues
+    """
+    
+    def analyze_users(self, users: List[Dict], login_history: List[Dict]) -> Dict:
+        """
+        Analyze user accounts for security issues
+        
+        Args:
+            users: List of user records
+            login_history: Login history records
+            
+        Returns:
+            Analysis results with findings
+        """
+        findings = []
+        
+        # Analyze dormant users
+        dormant_findings = self._find_dormant_users(users)
+        findings.extend(dormant_findings)
+        
+        # Analyze admin users
+        admin_findings = self._analyze_admin_users(users)
+        findings.extend(admin_findings)
+        
+        # Analyze MFA compliance
+        mfa_findings = self._analyze_mfa_compliance(users)
+        findings.extend(mfa_findings)
+        
+        # Analyze login anomalies
+        login_findings = self._analyze_login_history(login_history)
+        findings.extend(login_findings)
+        
+        return {
+            'findings': findings,
+            'total_users': len(users),
+            'active_users': len([u for u in users if u.get('IsActive')]),
+            'admin_users': len([u for u in users if 'Admin' in u.get('Profile', {}).get('Name', '')])
+        }
+    
+    def _find_dormant_users(self, users: List[Dict]) -> List[Dict]:
+        """
+        Find users who haven't logged in recently
+        """
+        findings = []
+        threshold_days = 90
+        threshold_date = datetime.now() - timedelta(days=threshold_days)
+        
+        for user in users:
+            last_login = user.get('LastLoginDate')
+            
+            if not last_login:
+                findings.append({
+                    'type': 'Dormant User - Never Logged In',
+                    'severity': 'Medium',
+                    'username': user.get('Username'),
+                    'user_id': user.get('Id'),
+                    'name': user.get('Name', 'Unknown'),  # ADDED
+                    'email': user.get('Email', 'N/A'),    # ADDED
+                    'status': 'Active' if user.get('IsActive') else 'Inactive',  # ADDED
+                    'profile': user.get('Profile', {}).get('Name', 'Unknown'),
+                    'description': f"User {user.get('Username')} has never logged in",
+                    'recommendation': 'Deactivate or review necessity of this account'
+                })
+            elif isinstance(last_login, str):
+                try:
+                    last_login_date = datetime.fromisoformat(last_login.replace('Z', '+00:00'))
+                    if last_login_date < threshold_date:
+                        days_inactive = (datetime.now() - last_login_date).days
+                        findings.append({
+                            'type': 'Dormant User - Inactive',
+                            'severity': 'Medium',
+                            'username': user.get('Username'),
+                            'user_id': user.get('Id'),
+                            'name': user.get('Name', 'Unknown'),  # ADDED
+                            'email': user.get('Email', 'N/A'),    # ADDED
+                            'status': 'Active' if user.get('IsActive') else 'Inactive',  # ADDED
+                            'profile': user.get('Profile', {}).get('Name', 'Unknown'),
+                            'days_inactive': days_inactive,
+                            'last_login': last_login,
+                            'description': f"User inactive for {days_inactive} days",
+                            'recommendation': 'Consider deactivating inactive accounts'
+                        })
+                except:
+                    pass
+        
+        return findings
+    
+    def _analyze_admin_users(self, users: List[Dict]) -> List[Dict]:
+        """
+        Analyze administrative users for security risks
+        """
+        findings = []
+        
+        admin_users = [
+            u for u in users 
+            if 'Admin' in u.get('Profile', {}).get('Name', '') or
+               'System Administrator' in u.get('Profile', {}).get('Name', '')
+        ]
+        
+        if len(admin_users) > 5:
+            # Create detailed user list with name, email, status
+            admin_details = []
+            for u in admin_users:
+                admin_details.append({
+                    'username': u.get('Username'),
+                    'name': u.get('Name', 'Unknown'),
+                    'email': u.get('Email', 'N/A'),
+                    'status': 'Active' if u.get('IsActive') else 'Inactive'
+                })
+            
+            findings.append({
+                'type': 'Excessive Admin Users',
+                'severity': 'High',
+                'admin_count': len(admin_users),
+                'description': f"{len(admin_users)} users have System Administrator profile",
+                'impact': 'Too many users with full org access increases security risk',
+                'recommendation': 'Reduce admin users, use Permission Sets for specific needs',
+                'admin_usernames': [u.get('Username') for u in admin_users],
+                'admin_details': admin_details  # ADDED: Detailed user info
+            })
+        
+        return findings
+    
+    def _analyze_mfa_compliance(self, users: List[Dict]) -> List[Dict]:
+        """
+        Analyze Multi-Factor Authentication compliance
+        """
+        findings = []
+        
+        # Check for users without MFA (if field exists)
+        non_mfa_users = []
+        non_mfa_details = []  # ADDED
+        
+        for user in users:
+            # Note: MfaEnabled__c might be a custom field
+            if user.get('MfaEnabled__c') == False:
+                non_mfa_users.append(user.get('Username'))
+                # ADDED: Collect detailed user info
+                non_mfa_details.append({
+                    'username': user.get('Username'),
+                    'name': user.get('Name', 'Unknown'),
+                    'email': user.get('Email', 'N/A'),
+                    'status': 'Active' if user.get('IsActive') else 'Inactive',
+                    'profile': user.get('Profile', {}).get('Name', 'Unknown')
+                })
+        
+        if non_mfa_users:
+            findings.append({
+                'type': 'MFA Not Enabled',
+                'severity': 'High',
+                'users_without_mfa': len(non_mfa_users),
+                'description': f"{len(non_mfa_users)} users don't have MFA enabled",
+                'impact': 'Accounts vulnerable to credential compromise',
+                'recommendation': 'Enable MFA for all users, especially admins',
+                'affected_users': non_mfa_users[:10],  # Show first 10 usernames
+                'user_details': non_mfa_details[:10]   # ADDED: First 10 with full details
+            })
+        
+        return findings
+    
+    def _analyze_login_history(self, login_history: List[Dict]) -> List[Dict]:
+        """
+        Analyze login history for suspicious patterns
+        """
+        findings = []
+        
+        # Count failed login attempts
+        failed_logins = [l for l in login_history if l.get('Status') == 'Failed']
+        
+        if len(failed_logins) > 100:
+            findings.append({
+                'type': 'High Failed Login Attempts',
+                'severity': 'High',
+                'failed_count': len(failed_logins),
+                'description': f"{len(failed_logins)} failed login attempts detected",
+                'impact': 'Possible brute force attack or credential stuffing',
+                'recommendation': 'Review failed attempts, consider IP restrictions'
+            })
+        
+        # Check for logins from unusual locations (simplified)
+        unique_ips = set(l.get('SourceIp', '') for l in login_history if l.get('SourceIp'))
+        
+        if len(unique_ips) > 100:
+            findings.append({
+                'type': 'Many Unique Login IPs',
+                'severity': 'Medium',
+                'unique_ip_count': len(unique_ips),
+                'description': f"Logins from {len(unique_ips)} different IP addresses",
+                'recommendation': 'Review for unauthorized access, implement IP restrictions'
+            })
+        
+        return findings
+    
+    def check_mfa_compliance(self, users: List[Dict]) -> Dict:
+        """
+        Check organization-wide MFA compliance
+        
+        Returns:
+            MFA compliance statistics
+        """
+        total_users = len(users)
+        mfa_enabled = sum(1 for u in users if u.get('MfaEnabled__c') == True)
+        
+        return {
+            'total_users': total_users,
+            'mfa_enabled': mfa_enabled,
+            'mfa_disabled': total_users - mfa_enabled,
+            'compliance_percentage': (mfa_enabled / total_users * 100) if total_users > 0 else 0,
+            'is_compliant': (mfa_enabled / total_users) >= 0.95 if total_users > 0 else False
+        }

optimax_reports.py

@@ -0,0 +1,734 @@
+"""
+Optimax Report Generator - Category-Based Security Analysis
+Organizes findings into 6 security categories WITHOUT mentioning AI models:
+
+1. Profiles & Permissions
+2. Sharing Model
+3. API & Integration
+4. Guest/Public Exposure
+5. Custom Code
+6. Identity & Sessions
+
+Usage:
+    from optimax_reports import generate_report
+    html_report = generate_report(analysis_result)
+"""
+
+def generate_report(analysis_result: dict) -> str:
+    """Generate comprehensive HTML report organized by security categories"""
+    
+    if not analysis_result.get('success', False):
+        return _generate_error_html(analysis_result.get('error', 'Unknown error'))
+    
+    # Extract data
+    org_name = analysis_result.get('org_name', 'Unknown Organization')
+    org_id = analysis_result.get('org_id', 'N/A')
+    timestamp = analysis_result.get('timestamp', '')
+    risk_score = analysis_result.get('overall_risk_score', 0)
+    risk_level = analysis_result.get('risk_level', 'UNKNOWN')
+    
+    # Get all findings
+    all_findings = (
+        analysis_result.get('critical_findings', []) +
+        analysis_result.get('high_findings', []) +
+        analysis_result.get('medium_findings', []) +
+        analysis_result.get('low_findings', [])
+    )
+    
+    ai_summary = analysis_result.get('ai_executive_summary', '')
+    ai_recommendations = analysis_result.get('ai_recommendations', [])
+    ai_detailed = analysis_result.get('ai_detailed_results', {})
+    statistics = analysis_result.get('statistics', {})
+    
+    # Categorize all findings
+    categorized_findings = _categorize_findings(all_findings, ai_detailed)
+    
+    risk_colors = {
+        'CRITICAL': '#dc2626',
+        'HIGH': '#ea580c',
+        'MEDIUM': '#f59e0b',
+        'LOW': '#10b981'
+    }
+    risk_color = risk_colors.get(risk_level, '#6b7280')
+    
+    # Build comprehensive HTML with FIXED download button
+    html = f"""
+<style>
+    .optimax-report * {{ margin: 0; padding: 0; box-sizing: border-box; }}
+    .optimax-report {{ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Arial, sans-serif; line-height: 1.6; color: #000000 !important; background: #ffffff !important; border-radius: 12px; overflow: hidden; }}
+    .optimax-header {{ background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: #ffffff !important; padding: 40px; text-align: center; }}
+    .optimax-header h1 {{ font-size: 2.5em; margin-bottom: 10px; font-weight: 700; color: #ffffff !important; }}
+    .optimax-header .subtitle {{ font-size: 1.2em; opacity: 1; color: #ffffff !important; margin-top: 10px; }}
+    .optimax-content {{ padding: 40px; background: #ffffff !important; }}
+    .optimax-section {{ margin-bottom: 40px; }}
+    .optimax-section-title {{ font-size: 1.8em; font-weight: 700; margin-bottom: 20px; color: #000000 !important; border-bottom: 3px solid #667eea; padding-bottom: 10px; }}
+    .optimax-category-title {{ font-size: 1.4em; font-weight: 700; margin: 30px 0 15px 0; color: #000000 !important; display: flex; align-items: center; gap: 10px; }}
+    .optimax-risk-score-box {{ text-align: center; padding: 30px; background: #f9fafb !important; border-radius: 10px; margin-bottom: 30px; border: 1px solid #e5e7eb; }}
+    .optimax-risk-score {{ font-size: 4em; font-weight: 700; color: {risk_color} !important; margin: 10px 0; }}
+    .optimax-risk-level {{ font-size: 1.3em; font-weight: 700; color: {risk_color} !important; text-transform: uppercase; letter-spacing: 0.1em; }}
+    .optimax-risk-cards {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 20px; margin-bottom: 40px; }}
+    .optimax-risk-card {{ background: #ffffff !important; border-radius: 10px; padding: 25px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); border-left: 5px solid; }}
+    .optimax-risk-card.critical {{ border-left-color: #dc2626; }}
+    .optimax-risk-card.high {{ border-left-color: #ea580c; }}
+    .optimax-risk-card.medium {{ border-left-color: #f59e0b; }}
+    .optimax-risk-card.low {{ border-left-color: #10b981; }}
+    .optimax-risk-card-title {{ font-size: 0.9em; text-transform: uppercase; color: #4b5563 !important; margin-bottom: 10px; font-weight: 700; }}
+    .optimax-risk-card-value {{ font-size: 2.5em; font-weight: 700; color: #000000 !important; text-shadow: 0 1px 2px rgba(0,0,0,0.1); }}
+    .optimax-stats-grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(180px, 1fr)); gap: 15px; margin-bottom: 30px; }}
+    .optimax-stat-item {{ background: #f9fafb !important; padding: 20px; border-radius: 8px; border-left: 4px solid #667eea; }}
+    .optimax-stat-label {{ font-size: 0.85em; color: #4b5563 !important; text-transform: uppercase; font-weight: 700; margin-bottom: 5px; }}
+    .optimax-stat-value {{ font-size: 1.6em; font-weight: 700; color: #000000 !important; }}
+    .optimax-finding-card {{ background: #f8fafc !important; border: 2px solid #e2e8f0; border-radius: 8px; padding: 20px; margin: 15px 0; }}
+    .optimax-finding-header {{ display: flex; align-items: center; gap: 12px; margin-bottom: 12px; flex-wrap: wrap; }}
+    .optimax-finding-title {{ font-weight: 700; color: #000000 !important; font-size: 1.05em; flex: 1; min-width: 200px; }}
+    .optimax-finding-body {{ color: #000000 !important; margin: 10px 0; line-height: 1.7; }}
+    .optimax-finding-body strong {{ color: #000000 !important; font-weight: 700; }}
+    .optimax-severity-badge {{ display: inline-block !important; padding: 6px 14px !important; border-radius: 20px !important; font-size: 0.85em !important; font-weight: 700 !important; text-transform: uppercase !important; letter-spacing: 0.05em !important; white-space: nowrap !important; }}
+    .optimax-severity-critical {{ background: #fee2e2 !important; color: #991b1b !important; border: 2px solid #991b1b !important; }}
+    .optimax-severity-high {{ background: #ffedd5 !important; color: #9a3412 !important; border: 2px solid #9a3412 !important; }}
+    .optimax-severity-medium {{ background: #fef3c7 !important; color: #92400e !important; border: 2px solid #92400e !important; }}
+    .optimax-severity-low {{ background: #d1fae5 !important; color: #065f46 !important; border: 2px solid #065f46 !important; }}
+    .optimax-summary-box {{ background: #e0f2fe; padding: 25px; border-radius: 10px; margin-bottom: 30px; border-left: 5px solid #0ea5e9; }}
+    .optimax-summary-title {{ font-size: 1.3em; font-weight: 700; color: #0c4a6e; margin-bottom: 15px; }}
+    .optimax-summary-box p {{ color: #000000 !important; line-height: 1.7; }}
+    .optimax-rec-box {{ background: #f0fdf4; padding: 25px; border-radius: 10px; border-left: 5px solid #10b981; }}
+    .optimax-rec-title {{ font-size: 1.3em; font-weight: 700; color: #065f46; margin-bottom: 15px; }}
+    .optimax-rec-list {{ list-style: none; padding-left: 0; }}
+    .optimax-rec-list li {{ padding: 12px 0; padding-left: 30px; position: relative; border-bottom: 1px solid #d1fae5; color: #000000 !important; line-height: 1.6; }}
+    .optimax-rec-list li:last-child {{ border-bottom: none; }}
+    .optimax-rec-list li::before {{ content: "✓"; position: absolute; left: 0; color: #10b981; font-weight: 700; font-size: 1.2em; }}
+    .optimax-no-findings {{ text-align: center; padding: 40px; color: #4b5563 !important; font-style: italic; }}
+    .optimax-category-icon {{ font-size: 1.3em; }}
+    .optimax-metric-grid {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(140px, 1fr)); gap: 10px; margin: 10px 0; }}
+    .optimax-metric-item {{ background: #f9fafb !important; padding: 10px 15px; border-radius: 6px; border-left: 3px solid #667eea; }}
+    .optimax-metric-label {{ font-size: 0.75em; color: #4b5563 !important; text-transform: uppercase; font-weight: 700; }}
+    .optimax-metric-value {{ font-size: 1.1em; font-weight: 700; color: #000000 !important; margin-top: 3px; }}
+    .optimax-recommendation {{ background: #dcfce7; border-left: 4px solid #10b981; padding: 12px 15px; border-radius: 6px; margin: 10px 0; }}
+    .optimax-recommendation-title {{ font-weight: 700; color: #166534; font-size: 0.95em; }}
+    .optimax-recommendation-text {{ color: #166534; margin-top: 5px; font-size: 0.9em; line-height: 1.5; }}
+    .optimax-affected-users {{ background: #fef2f2; padding: 10px 15px; border-radius: 6px; margin: 10px 0; border-left: 3px solid #dc2626; }}
+    .optimax-affected-users-title {{ font-weight: 700; color: #991b1b; font-size: 0.9em; }}
+    .optimax-affected-users-list {{ color: #991b1b; font-size: 0.85em; margin-top: 5px; }}
+    .optimax-category-summary {{ background: #f9fafb !important; padding: 15px 20px; border-radius: 8px; margin: 15px 0 25px 0; border-left: 4px solid #667eea; }}
+    .optimax-category-summary-text {{ color: #000000 !important; font-size: 0.95em; line-height: 1.6; }}
+    .optimax-download-btn {{ display: inline-block; padding: 12px 30px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: #ffffff !important; text-decoration: none; border-radius: 8px; font-weight: 700; font-size: 1em; cursor: pointer; border: none; box-shadow: 0 4px 6px rgba(0,0,0,0.1); transition: all 0.3s ease; margin: 10px 0; }}
+    .optimax-download-btn:hover {{ transform: translateY(-2px); box-shadow: 0 6px 12px rgba(0,0,0,0.15); }}
+    .optimax-download-btn:active {{ transform: translateY(0); }}
+</style>
+
+<script>
+function downloadReport() {{
+    try {{
+        // Wait a moment for Gradio to fully render
+        setTimeout(() => {{
+            // Find the report container
+            let reportContainer = document.querySelector('.optimax-report');
+            
+            // If not found in current context, try looking in parent frames
+            if (!reportContainer && window.parent) {{
+                reportContainer = window.parent.document.querySelector('.optimax-report');
+            }}
+            
+            if (!reportContainer) {{
+                // Last resort: get the entire HTML content
+                const htmlContent = document.documentElement.outerHTML;
+                const blob = new Blob([htmlContent], {{ type: 'text/html' }});
+                const url = URL.createObjectURL(blob);
+                const link = document.createElement('a');
+                const timestamp = new Date().toISOString().slice(0, 19).replace(/:/g, '-');
+                link.href = url;
+                link.download = `Optimax_Report_${{timestamp}}.html`;
+                link.click();
+                URL.revokeObjectURL(url);
+                alert('✅ Report downloaded!');
+                return;
+            }}
+            
+            // Clone and prepare report
+            const clone = reportContainer.cloneNode(true);
+            const downloadBtn = clone.querySelector('.optimax-download-btn');
+            if (downloadBtn) downloadBtn.remove();
+            
+            // Get styles
+            const styles = Array.from(document.querySelectorAll('style'))
+                .map(s => s.innerHTML).join('\\n');
+            
+            // Create complete HTML
+            const timestamp = new Date().toISOString().slice(0, 19).replace(/:/g, '-');
+            const fullHTML = `<!DOCTYPE html>
+<html>
+<head>
+    <meta charset="UTF-8">
+    <title>Optimax Security Report</title>
+    <style>${{styles}}</style>
+</head>
+<body style="margin:0;padding:20px;background:#f9fafb">
+    ${{clone.outerHTML}}
+</body>
+</html>`;
+            
+            // Download
+            const blob = new Blob([fullHTML], {{ type: 'text/html' }});
+            const url = URL.createObjectURL(blob);
+            const link = document.createElement('a');
+            link.href = url;
+            link.download = `Optimax_Security_Report_${{timestamp}}.html`;
+            document.body.appendChild(link);
+            link.click();
+            document.body.removeChild(link);
+            URL.revokeObjectURL(url);
+            
+            // Visual feedback
+            const btn = document.querySelector('.optimax-download-btn');
+            if (btn) {{
+                const orig = btn.innerHTML;
+                btn.innerHTML = '✅ Downloaded!';
+                btn.style.background = '#10b981';
+                setTimeout(() => {{
+                    btn.innerHTML = orig;
+                    btn.style.background = 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)';
+                }}, 2000);
+            }}
+        }}, 100);
+    }} catch (error) {{
+        console.error('Download error:', error);
+        alert('Download failed: ' + error.message + '\\n\\nTry right-clicking and Save As instead.');
+    }}
+}}
+</script>
+
+<div class="optimax-report">
+    <div class="optimax-header">
+        <h1>🛡️ Optimax Security Report</h1>
+        <div class="subtitle">Comprehensive Security Analysis</div>
+        <button class="optimax-download-btn" onclick="downloadReport()" type="button">
+            📥 Download Report
+        </button>
+    </div>
+    
+    <div class="optimax-content">
+        <!-- Organization Info -->
+        <div class="optimax-section">
+            <div class="optimax-stats-grid">
+                <div class="optimax-stat-item">
+                    <div class="optimax-stat-label">Organization</div>
+                    <div class="optimax-stat-value" style="font-size: 1.2em;">{org_name}</div>
+                </div>
+                <div class="optimax-stat-item">
+                    <div class="optimax-stat-label">Org ID</div>
+                    <div class="optimax-stat-value" style="font-size: 0.9em;">{org_id[:15]}...</div>
+                </div>
+                <div class="optimax-stat-item">
+                    <div class="optimax-stat-label">Scan Date</div>
+                    <div class="optimax-stat-value" style="font-size: 0.9em;">{timestamp[:10] if timestamp else 'N/A'}</div>
+                </div>
+                <div class="optimax-stat-item">
+                    <div class="optimax-stat-label">Scan Time</div>
+                    <div class="optimax-stat-value" style="font-size: 0.9em;">{timestamp[11:19] if timestamp else 'N/A'}</div>
+                </div>
+            </div>
+        </div>
+        
+        <!-- Risk Assessment -->
+        <div class="optimax-section">
+            <h2 class="optimax-section-title">Overall Risk Assessment</h2>
+            <div class="optimax-risk-score-box">
+                <div style="color: #4b5563 !important; font-weight: 700; text-transform: uppercase; font-size: 0.9em;">Security Risk Score</div>
+                <div class="optimax-risk-score">{risk_score}/100</div>
+                <div class="optimax-risk-level">{risk_level} RISK</div>
+            </div>
+            
+            <div class="optimax-risk-cards">
+                <div class="optimax-risk-card critical">
+                    <div class="optimax-risk-card-title">Critical</div>
+                    <div class="optimax-risk-card-value">{len(analysis_result.get('critical_findings', []))}</div>
+                </div>
+                <div class="optimax-risk-card high">
+                    <div class="optimax-risk-card-title">High</div>
+                    <div class="optimax-risk-card-value">{len(analysis_result.get('high_findings', []))}</div>
+                </div>
+                <div class="optimax-risk-card medium">
+                    <div class="optimax-risk-card-title">Medium</div>
+                    <div class="optimax-risk-card-value">{len(analysis_result.get('medium_findings', []))}</div>
+                </div>
+                <div class="optimax-risk-card low">
+                    <div class="optimax-risk-card-title">Low</div>
+                    <div class="optimax-risk-card-value">{len(analysis_result.get('low_findings', []))}</div>
+                </div>
+            </div>
+        </div>
+        
+        <!-- Executive Summary -->
+        {_build_summary_section(ai_summary, ai_recommendations)}
+        
+        <!-- Scan Statistics -->
+        <div class="optimax-section">
+            <h2 class="optimax-section-title">Scan Statistics</h2>
+            <div class="optimax-stats-grid">
+                {_build_stats(statistics)}
+            </div>
+        </div>
+        
+        <!-- CATEGORY-BASED FINDINGS -->
+        <div class="optimax-section">
+            <h2 class="optimax-section-title">Security Findings by Category</h2>
+            
+            {_build_category_section('Profiles & Permissions', categorized_findings['profiles_permissions'], '👤', 
+                'User profiles, permission sets, system permissions, and access controls')}
+            
+            {_build_category_section('Sharing Model', categorized_findings['sharing_model'], '🔓',
+                'Organization-wide defaults, sharing rules, role hierarchy, and record-level access')}
+            
+            {_build_category_section('API & Integration', categorized_findings['api_integration'], '🔌',
+                'API usage, connected apps, OAuth tokens, and external integrations')}
+            
+            {_build_category_section('Guest/Public Exposure', categorized_findings['guest_public'], '🌐',
+                'Guest user access, public pages, unauthenticated access, and site security')}
+            
+            {_build_category_section('Custom Code', categorized_findings['custom_code'], '💻',
+                'Apex classes, triggers, SOQL queries, and code security vulnerabilities')}
+            
+            {_build_category_section('Identity & Sessions', categorized_findings['identity_sessions'], '🔐',
+                'Login activity, MFA compliance, session security, and user authentication')}
+        </div>
+        
+    </div>
+</div>
+"""
+    
+    return html
+
+
+def _categorize_findings(all_findings: list, ai_detailed: dict) -> dict:
+    """Categorize findings into 6 security categories"""
+    
+    categories = {
+        'profiles_permissions': [],
+        'sharing_model': [],
+        'api_integration': [],
+        'guest_public': [],
+        'custom_code': [],
+        'identity_sessions': []
+    }
+    
+    # Categorize rule-based findings
+    for finding in all_findings:
+        finding_type = finding.get('type', '').lower()
+        
+        # Profiles & Permissions
+        if any(keyword in finding_type for keyword in ['permission', 'profile', 'admin', 'privilege', 'escalation']):
+            categories['profiles_permissions'].append(finding)
+        
+        # Sharing Model
+        elif any(keyword in finding_type for keyword in ['sharing', 'owd', 'role', 'hierarchy']):
+            categories['sharing_model'].append(finding)
+        
+        # API & Integration
+        elif any(keyword in finding_type for keyword in ['api', 'integration', 'oauth', 'connected']):
+            categories['api_integration'].append(finding)
+        
+        # Guest/Public Exposure
+        elif any(keyword in finding_type for keyword in ['guest', 'public', 'unauthenticated', 'site']):
+            categories['guest_public'].append(finding)
+        
+        # Custom Code
+        elif any(keyword in finding_type for keyword in ['code', 'apex', 'trigger', 'soql', 'class']):
+            categories['custom_code'].append(finding)
+        
+        # Identity & Sessions
+        elif any(keyword in finding_type for keyword in ['login', 'mfa', 'dormant', 'session', 'password', 'authentication']):
+            categories['identity_sessions'].append(finding)
+        
+        # Default to Profiles & Permissions if unclear
+        else:
+            categories['profiles_permissions'].append(finding)
+    
+    # Add AI-detected findings if available
+    if ai_detailed and ai_detailed.get('available'):
+        
+        # Code analysis findings -> Custom Code
+        for code_item in ai_detailed.get('code_analysis', []):
+            for vuln in code_item.get('vulnerabilities', []):
+                categories['custom_code'].append({
+                    'type': vuln.get('type', 'Code Vulnerability'),
+                    'severity': vuln.get('severity', 'Medium'),
+                    'description': vuln.get('description', 'Code security issue detected'),
+                    'class_name': code_item.get('class_name'),
+                    'confidence': vuln.get('confidence', 0),
+                    'recommendation': _get_code_recommendation(vuln.get('type', ''))
+                })
+        
+        # Permission analysis -> Profiles & Permissions
+        for perm in ai_detailed.get('permission_analysis', []):
+            if perm.get('risk_level') in ['CRITICAL', 'HIGH']:
+                categories['profiles_permissions'].append({
+                    'type': f"{perm.get('type', 'Permission Set')} - {perm.get('name', 'Unknown')}",
+                    'severity': perm.get('risk_level', 'Medium'),
+                    'description': f"Risk level: {perm.get('risk_level')}",
+                    'dangerous_permissions': perm.get('dangerous_permissions', []),
+                    'confidence': perm.get('confidence', 0),
+                    'recommendation': 'Review and restrict dangerous permissions'
+                })
+        
+        # Security analysis -> Identity & Sessions
+        for sec in ai_detailed.get('security_analysis', []):
+            categories['identity_sessions'].append({
+                'type': sec.get('type', 'Security Policy Issue'),
+                'severity': sec.get('severity', 'Medium'),
+                'description': sec.get('description', 'Security policy violation detected'),
+                'confidence': sec.get('confidence', 0),
+                'recommendation': sec.get('recommendation', 'Review and remediate')
+            })
+        
+        # Anomaly detection -> Identity & Sessions
+        for anomaly in ai_detailed.get('anomaly_analysis', []):
+            categories['identity_sessions'].append({
+                'type': anomaly.get('type', 'Behavioral Anomaly'),
+                'severity': anomaly.get('severity', 'Medium'),
+                'description': anomaly.get('description', 'Unusual behavior detected'),
+                'impact': anomaly.get('impact', ''),
+                'recommendation': anomaly.get('recommendation', 'Investigate immediately'),
+                'anomaly_score': anomaly.get('anomaly_score'),
+                'username': anomaly.get('username'),
+                'login_time': anomaly.get('login_time'),
+                'source_ip': anomaly.get('source_ip')
+            })
+    
+    return categories
+
+
+def _get_code_recommendation(vuln_type: str) -> str:
+    """Get recommendation for code vulnerability"""
+    recommendations = {
+        'SOQL_INJECTION': 'Use bind variables instead of string concatenation',
+        'POTENTIAL_SOQL_INJECTION': 'Replace dynamic query with parameterized query',
+        'MISSING_SHARING': 'Add with sharing keyword to class declaration',
+        'POTENTIAL_HARDCODED_CREDENTIALS': 'Move credentials to Custom Metadata or Named Credentials',
+        'UNSAFE_DML': 'Wrap DML operations in try-catch with proper error handling'
+    }
+    return recommendations.get(vuln_type, 'Review and remediate this security issue')
+
+
+def _build_summary_section(ai_summary: str, ai_recommendations: list) -> str:
+    """Build executive summary section"""
+    if not ai_summary and not ai_recommendations:
+        return ''
+    
+    html = '<div class="optimax-section">'
+    
+    if ai_summary:
+        # Remove AI model mentions from summary
+        clean_summary = ai_summary
+        for model in ['CodeBERT', 'RoBERTa', 'SecBERT', 'Isolation Forest', 'AI-powered', 'AI analysis', 'AI models']:
+            clean_summary = clean_summary.replace(model, 'Security analysis')
+        
+        html += f'''
+            <div class="optimax-summary-box">
+                <div class="optimax-summary-title" style="color: #0c4a6e !important;">📋 Executive Summary</div>
+                <p style="color: #000000 !important;">{clean_summary}</p>
+            </div>
+        '''
+    
+    if ai_recommendations:
+        # Clean recommendations from AI model mentions
+        clean_recs = []
+        for rec in ai_recommendations:
+            clean_rec = rec
+            for model in ['CodeBERT', 'RoBERTa', 'SecBERT', 'Isolation Forest', 'AI-detected', 'AI analysis']:
+                clean_rec = clean_rec.replace(model, 'Analysis')
+            clean_recs.append(clean_rec)
+        
+        html += '<div class="optimax-rec-box"><div class="optimax-rec-title" style="color: #065f46 !important;">💡 Key Recommendations</div><ul class="optimax-rec-list">'
+        for rec in clean_recs:
+            html += f'<li style="color: #000000 !important;">{rec}</li>'
+        html += '</ul></div>'
+    
+    html += '</div>'
+    return html
+
+
+def _build_stats(statistics: dict) -> str:
+    """Build statistics section"""
+    stats_items = [
+        ('Total Findings', statistics.get('total_findings', 0)),
+        ('Users Analyzed', statistics.get('users_analyzed', 0)),
+        ('Profiles Analyzed', statistics.get('profiles_analyzed', 0)),
+        ('Permission Sets', statistics.get('permission_sets_analyzed', 0)),
+        ('Login Records', statistics.get('login_records_analyzed', 0)),
+    ]
+    
+    html = ''
+    for label, value in stats_items:
+        display_value = f'{value:,}' if isinstance(value, int) else str(value)
+        html += f'''
+            <div class="optimax-stat-item">
+                <div class="optimax-stat-label" style="color: #4b5563 !important;">{label}</div>
+                <div class="optimax-stat-value" style="color: #000000 !important;">{display_value}</div>
+            </div>
+        '''
+    
+    return html
+
+
+def _build_category_section(category_name: str, findings: list, icon: str, description: str) -> str:
+    """Build a category section with all findings"""
+    
+    if not findings:
+        return f'''
+            <div class="optimax-category-title">
+                <span class="optimax-category-icon">{icon}</span>
+                <span style="color: #000000 !important;">{category_name}</span>
+            </div>
+            <div class="optimax-category-summary">
+                <div class="optimax-category-summary-text" style="color: #000000 !important;">
+                    <em style="color: #000000 !important;">{description}</em>
+                </div>
+            </div>
+            <div class="optimax-no-findings" style="color: #4b5563 !important;">✅ No issues detected in this category</div>
+        '''
+    
+    # Count by severity
+    critical = [f for f in findings if f.get('severity', '').upper() in ['CRITICAL']]
+    high = [f for f in findings if f.get('severity', '').upper() in ['HIGH']]
+    medium = [f for f in findings if f.get('severity', '').upper() in ['MEDIUM']]
+    low = [f for f in findings if f.get('severity', '').upper() in ['LOW']]
+    
+    html = f'''
+        <div class="optimax-category-title">
+            <span class="optimax-category-icon">{icon}</span>
+            <span style="color: #000000 !important;">{category_name}</span>
+        </div>
+        <div class="optimax-category-summary">
+            <div class="optimax-category-summary-text" style="color: #000000 !important;">
+                <em style="color: #000000 !important;">{description}</em>
+            </div>
+            <div class="optimax-metric-grid" style="margin-top: 15px;">
+                <div class="optimax-metric-item">
+                    <div class="optimax-metric-label" style="color: #4b5563 !important;">Critical</div>
+                    <div class="optimax-metric-value" style="color: #dc2626 !important;">{len(critical)}</div>
+                </div>
+                <div class="optimax-metric-item">
+                    <div class="optimax-metric-label" style="color: #4b5563 !important;">High</div>
+                    <div class="optimax-metric-value" style="color: #ea580c !important;">{len(high)}</div>
+                </div>
+                <div class="optimax-metric-item">
+                    <div class="optimax-metric-label" style="color: #4b5563 !important;">Medium</div>
+                    <div class="optimax-metric-value" style="color: #f59e0b !important;">{len(medium)}</div>
+                </div>
+                <div class="optimax-metric-item">
+                    <div class="optimax-metric-label" style="color: #4b5563 !important;">Low</div>
+                    <div class="optimax-metric-value" style="color: #10b981 !important;">{len(low)}</div>
+                </div>
+                <div class="optimax-metric-item">
+                    <div class="optimax-metric-label" style="color: #4b5563 !important;">Total</div>
+                    <div class="optimax-metric-value" style="color: #000000 !important;">{len(findings)}</div>
+                </div>
+            </div>
+        </div>
+    '''
+    
+    # Display findings
+    for finding in findings:
+        finding_type = finding.get('type', 'Unknown Issue')
+        severity = finding.get('severity', 'Medium').upper()
+        description = finding.get('description', 'No description available')
+        recommendation = finding.get('recommendation', 'Review and remediate')
+        impact = finding.get('impact', '')
+        
+        # Build additional details
+        details_html = ''
+        
+        if finding.get('class_name'):
+            details_html += f'<div style="color: #000000 !important;"><strong style="color: #000000 !important;">Apex Class:</strong> <span style="color: #000000 !important;">{finding["class_name"]}</span></div>'
+        
+        if finding.get('dangerous_permissions'):
+            perms = finding['dangerous_permissions']
+            details_html += f'<div style="color: #000000 !important;"><strong style="color: #000000 !important;">Dangerous Permissions:</strong> <span style="color: #000000 !important;">{", ".join(perms)}</span></div>'
+        
+        if finding.get('confidence'):
+            confidence_pct = int(finding['confidence'] * 100)
+            details_html += f'<div style="color: #000000 !important;"><strong style="color: #000000 !important;">Detection Confidence:</strong> <span style="color: #000000 !important;">{confidence_pct}%</span></div>'
+        
+        if finding.get('username'):
+            details_html += f'<div style="color: #000000 !important;"><strong style="color: #000000 !important;">User:</strong> <span style="color: #000000 !important;">{finding["username"]}</span></div>'
+        
+        if finding.get('login_time'):
+            details_html += f'<div style="color: #000000 !important;"><strong style="color: #000000 !important;">Time:</strong> <span style="color: #000000 !important;">{finding["login_time"]}</span></div>'
+        
+        if finding.get('source_ip'):
+            details_html += f'<div style="color: #000000 !important;"><strong style="color: #000000 !important;">IP Address:</strong> <span style="color: #000000 !important;">{finding["source_ip"]}</span></div>'
+        
+        if finding.get('days_inactive'):
+            details_html += f'<div style="color: #000000 !important;"><strong style="color: #000000 !important;">Inactive Days:</strong> <span style="color: #000000 !important;">{finding["days_inactive"]}</span></div>'
+        
+        # Affected users with detailed information
+        affected_html = ''
+        
+        # Check for detailed user information first (priority)
+        if finding.get('user_details'):
+            user_details = finding['user_details']
+            user_count = len(user_details)
+            
+            # Build table for detailed user info
+            affected_html = f'''
+                <div class="optimax-affected-users">
+                    <div class="optimax-affected-users-title" style="color: #991b1b !important;">👥 Affected Users ({user_count})</div>
+                    <table class="optimax-user-table" style="width: 100%; margin-top: 10px; border-collapse: collapse;">
+                        <thead>
+                            <tr style="background: #fee2e2; border-bottom: 2px solid #991b1b;">
+                                <th style="padding: 8px; text-align: left; color: #991b1b !important; font-weight: 700; font-size: 0.85em;">Name</th>
+                                <th style="padding: 8px; text-align: left; color: #991b1b !important; font-weight: 700; font-size: 0.85em;">Email</th>
+                                <th style="padding: 8px; text-align: left; color: #991b1b !important; font-weight: 700; font-size: 0.85em;">Username</th>
+                                <th style="padding: 8px; text-align: center; color: #991b1b !important; font-weight: 700; font-size: 0.85em;">Status</th>
+                            </tr>
+                        </thead>
+                        <tbody>
+            '''
+            
+            # Show first 10 users to avoid redundancy
+            for i, user in enumerate(user_details[:10]):
+                row_bg = '#fef2f2' if i % 2 == 0 else '#ffffff'
+                status_color = '#10b981' if user.get('status') == 'Active' else '#dc2626'
+                
+                affected_html += f'''
+                    <tr style="background: {row_bg}; border-bottom: 1px solid #fca5a5;">
+                        <td style="padding: 8px; color: #991b1b !important; font-size: 0.85em;">{user.get('name', 'Unknown')}</td>
+                        <td style="padding: 8px; color: #991b1b !important; font-size: 0.85em;">{user.get('email', 'N/A')}</td>
+                        <td style="padding: 8px; color: #991b1b !important; font-size: 0.85em; font-family: monospace;">{user.get('username', 'Unknown')}</td>
+                        <td style="padding: 8px; text-align: center;">
+                            <span style="display: inline-block; padding: 3px 8px; background: {row_bg}; color: {status_color} !important; font-weight: 700; font-size: 0.75em; border: 1px solid {status_color}; border-radius: 4px;">
+                                {user.get('status', 'Unknown')}
+                            </span>
+                        </td>
+                    </tr>
+                '''
+            
+            affected_html += '</tbody></table>'
+            
+            if user_count > 10:
+                affected_html += f'''
+                    <div style="margin-top: 8px; color: #991b1b !important; font-size: 0.85em; font-style: italic;">
+                        + {user_count - 10} more users not shown
+                    </div>
+                '''
+            
+            affected_html += '</div>'
+            
+        # Fallback to simple username list if no detailed info
+        elif finding.get('affected_users'):
+            affected_users = finding['affected_users']
+            if len(affected_users) > 0:
+                user_count = len(affected_users)
+                # Remove duplicates while preserving order
+                unique_users = list(dict.fromkeys(affected_users))
+                users_display = ', '.join(unique_users[:5])
+                if len(unique_users) > 5:
+                    users_display += f' (+{len(unique_users) - 5} more)'
+                
+                affected_html = f'''
+                    <div class="optimax-affected-users">
+                        <div class="optimax-affected-users-title" style="color: #991b1b !important;">👥 Affected Users ({len(unique_users)})</div>
+                        <div class="optimax-affected-users-list" style="color: #991b1b !important;">{users_display}</div>
+                    </div>
+                '''
+        
+        # Single user details (for individual findings)
+        elif finding.get('username'):
+            affected_html = f'''
+                <div class="optimax-affected-users" style="background: #fef2f2; padding: 12px 15px; border-radius: 6px; margin: 10px 0; border-left: 3px solid #dc2626;">
+                    <table style="width: 100%; border-collapse: collapse;">
+                        <tr>
+                            <td style="padding: 4px 8px; color: #991b1b !important; font-weight: 700; font-size: 0.85em; width: 100px;">Name:</td>
+                            <td style="padding: 4px 8px; color: #991b1b !important; font-size: 0.85em;">{finding.get('name', 'Unknown')}</td>
+                        </tr>
+                        <tr>
+                            <td style="padding: 4px 8px; color: #991b1b !important; font-weight: 700; font-size: 0.85em;">Email:</td>
+                            <td style="padding: 4px 8px; color: #991b1b !important; font-size: 0.85em;">{finding.get('email', 'N/A')}</td>
+                        </tr>
+                        <tr>
+                            <td style="padding: 4px 8px; color: #991b1b !important; font-weight: 700; font-size: 0.85em;">Username:</td>
+                            <td style="padding: 4px 8px; color: #991b1b !important; font-size: 0.85em; font-family: monospace;">{finding.get('username', 'Unknown')}</td>
+                        </tr>
+                        <tr>
+                            <td style="padding: 4px 8px; color: #991b1b !important; font-weight: 700; font-size: 0.85em;">Status:</td>
+                            <td style="padding: 4px 8px;">
+                                <span style="display: inline-block; padding: 2px 8px; background: #fee2e2; color: {'#10b981' if finding.get('status') == 'Active' else '#dc2626'} !important; font-weight: 700; font-size: 0.75em; border: 1px solid {'#10b981' if finding.get('status') == 'Active' else '#dc2626'}; border-radius: 4px;">
+                                    {finding.get('status', 'Unknown')}
+                                </span>
+                            </td>
+                        </tr>
+                    </table>
+                </div>
+            '''
+        
+        html += f'''
+            <div class="optimax-finding-card">
+                <div class="optimax-finding-header">
+                    <div class="optimax-finding-title">{finding_type}</div>
+                    <span class="optimax-severity-badge optimax-severity-{severity.lower()}">{severity}</span>
+                </div>
+                <div class="optimax-finding-body">
+                    <div style="margin-bottom: 8px; color: #000000 !important;"><strong style="color: #000000 !important;">Description:</strong> <span style="color: #000000 !important;">{description}</span></div>
+                    {f'<div style="margin-bottom: 8px; color: #000000 !important;"><strong style="color: #000000 !important;">Impact:</strong> <span style="color: #000000 !important;">{impact}</span></div>' if impact else ''}
+                    {details_html}
+                </div>
+                {affected_html}
+                <div class="optimax-recommendation">
+                    <div class="optimax-recommendation-title" style="color: #166534 !important;">💡 Recommendation</div>
+                    <div class="optimax-recommendation-text" style="color: #166534 !important;">{recommendation}</div>
+                </div>
+            </div>
+        '''
+    
+    return html
+
+
+def _generate_error_html(error_msg: str) -> str:
+    """Generate HTML for error display"""
+    return f"""
+<div style="padding: 30px; background: #fee2e2; border-radius: 10px; border-left: 5px solid #dc2626;">
+    <h2 style="color: #991b1b; margin-bottom: 10px; font-size: 1.5em;">❌ Error</h2>
+    <p style="color: #991b1b; font-size: 1.1em;">{error_msg}</p>
+</div>
+"""
+
+
+# Save report function
+def save_report(analysis_result: dict, filename: str = None) -> str:
+    """Save complete HTML report to file"""
+    from datetime import datetime
+    
+    html_content = generate_report(analysis_result)
+    
+    full_html = f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Optimax Security Report - Category-Based Analysis</title>
+</head>
+<body style="margin: 0; padding: 20px; background: #f9fafb !important;">
+{html_content}
+</body>
+</html>"""
+    
+    if not filename:
+        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+        org_name = analysis_result.get('org_name', 'Unknown').replace(' ', '_')
+        filename = f"optimax_report_{org_name}_{timestamp}.html"
+    
+    with open(filename, 'w', encoding='utf-8') as f:
+        f.write(full_html)
+    
+    return filename
+
+
+if __name__ == '__main__':
+    print("✅ Category-Based Optimax Report Generator loaded!")
+    print("   📁 Categories:")
+    print("      1. 👤 Profiles & Permissions")
+    print("      2. 🔓 Sharing Model")
+    print("      3. 🔌 API & Integration")
+    print("      4. 🌐 Guest/Public Exposure")
+    print("      5. 💻 Custom Code")
+    print("      6. 🔐 Identity & Sessions")

permission_analyzer.py

@@ -0,0 +1,352 @@
+from typing import Dict, List, Optional
+
+class PermissionAnalyzer:
+    """
+    Analyzes Salesforce permissions for security vulnerabilities
+    """
+    
+    # Dangerous permissions that grant broad access
+    DANGEROUS_PERMISSIONS = {
+        'PermissionsModifyAllData': {
+            'severity': 'Critical',
+            'description': 'Modify All Data - Full read/write access to all records',
+            'impact': 'User can view, edit, and delete ALL records in the org'
+        },
+        'PermissionsViewAllData': {
+            'severity': 'Critical',
+            'description': 'View All Data - Read access to all records',
+            'impact': 'User can view ALL records, bypassing sharing rules'
+        },
+        'PermissionsManageUsers': {
+            'severity': 'Critical',
+            'description': 'Manage Users - Can create/modify users',
+            'impact': 'Can create admin users, escalate privileges'
+        },
+        'PermissionsCustomizeApplication': {
+            'severity': 'High',
+            'description': 'Customize Application - Modify org configuration',
+            'impact': 'Can change org settings, create fields, objects'
+        },
+        'PermissionsAuthorApex': {
+            'severity': 'High',
+            'description': 'Author Apex - Write and deploy code',
+            'impact': 'Can deploy malicious code, bypass security'
+        },
+        'PermissionsExportReport': {
+            'severity': 'Medium',
+            'description': 'Export Reports - Export data to external files',
+            'impact': 'Can export sensitive data in bulk'
+        },
+        'PermissionsManageRoles': {
+            'severity': 'High',
+            'description': 'Manage Roles - Modify role hierarchy',
+            'impact': 'Can manipulate data access through role changes'
+        },
+        'PermissionsModifyMetadata': {
+            'severity': 'High',
+            'description': 'Modify Metadata - Change org configuration',
+            'impact': 'Can alter security settings and configurations'
+        }
+    }
+    
+    def analyze_all_permissions(
+        self, 
+        permission_sets: List[Dict], 
+        profiles: List[Dict],
+        users: List[Dict] = None  # ADDED: Pass users to get details
+    ) -> Dict:
+        """
+        Analyze all permission sets and profiles
+        
+        Returns:
+            Analysis results with findings
+        """
+        findings = []
+        
+        # Create user lookup dictionary for faster access
+        user_lookup = {}
+        if users:
+            user_lookup = {u.get('Id'): u for u in users}
+        
+        # Analyze permission sets
+        for ps in permission_sets:
+            ps_findings = self.analyze_permission_set(ps, user_lookup)
+            findings.extend(ps_findings)
+        
+        # Analyze profiles
+        for profile in profiles:
+            profile_findings = self._analyze_profile_permissions(profile)
+            findings.extend(profile_findings)
+        
+        return {
+            'findings': findings,
+            'permission_sets_analyzed': len(permission_sets),
+            'profiles_analyzed': len(profiles),
+            'dangerous_assignments': self._count_dangerous_assignments(findings)
+        }
+    
+    def analyze_permission_set(self, permission_set: Dict, user_lookup: Dict = None) -> List[Dict]:
+        """
+        Analyze a single permission set for security issues
+        
+        Args:
+            permission_set: Permission set data from Salesforce
+            user_lookup: Dictionary mapping user IDs to user records
+            
+        Returns:
+            List of security findings
+        """
+        findings = []
+        ps_name = permission_set.get('Name', 'Unknown')
+        ps_id = permission_set.get('Id', 'Unknown')
+        assignments = permission_set.get('Assignments', [])
+        
+        # Build detailed user list with name, email, status
+        user_details = []
+        affected_usernames = []
+        
+        if user_lookup:
+            for assignment in assignments:
+                assignee = assignment.get('Assignee', {})
+                user_id = assignee.get('Id') or assignment.get('AssigneeId')
+                
+                if user_id and user_id in user_lookup:
+                    user = user_lookup[user_id]
+                    user_details.append({
+                        'username': user.get('Username'),
+                        'name': user.get('Name', 'Unknown'),
+                        'email': user.get('Email', 'N/A'),
+                        'status': 'Active' if user.get('IsActive') else 'Inactive'
+                    })
+                    affected_usernames.append(user.get('Username', 'Unknown'))
+                else:
+                    # Fallback to assignee data if available
+                    user_details.append({
+                        'username': assignee.get('Username', 'Unknown'),
+                        'name': assignee.get('Name', 'Unknown'),
+                        'email': assignee.get('Email', 'N/A'),
+                        'status': 'Unknown'
+                    })
+                    affected_usernames.append(assignee.get('Username', 'Unknown'))
+        else:
+            # Fallback when user_lookup is not available
+            for assignment in assignments:
+                assignee = assignment.get('Assignee', {})
+                user_details.append({
+                    'username': assignee.get('Username', 'Unknown'),
+                    'name': assignee.get('Name', 'Unknown'),
+                    'email': assignee.get('Email', 'N/A'),
+                    'status': 'Unknown'
+                })
+                affected_usernames.append(assignee.get('Username', 'Unknown'))
+        
+        # Check for dangerous permissions
+        dangerous_perms = []
+        for perm_field, perm_info in self.DANGEROUS_PERMISSIONS.items():
+            if permission_set.get(perm_field) == True:
+                dangerous_perms.append(perm_info)
+                
+                # Create finding for each dangerous permission
+                findings.append({
+                    'type': 'Dangerous Permission in Permission Set',
+                    'severity': perm_info['severity'],
+                    'permission_set': ps_name,
+                    'permission_set_id': ps_id,
+                    'permission': perm_info['description'],
+                    'impact': perm_info['impact'],
+                    'assigned_users': len(assignments),
+                    'description': f"Permission Set '{ps_name}' grants {perm_info['description']} to {len(assignments)} user(s)",
+                    'recommendation': f"Review necessity of this permission. Consider removing or restricting to fewer users.",
+                    'affected_users': affected_usernames,  # List of usernames
+                    'user_details': user_details  # ADDED: Detailed user info with name, email, status
+                })
+        
+        # Check for permission escalation risk
+        if len(dangerous_perms) >= 2:
+            findings.append({
+                'type': 'Permission Escalation Risk',
+                'severity': 'Critical',
+                'permission_set': ps_name,
+                'description': f"Permission Set '{ps_name}' combines multiple dangerous permissions",
+                'impact': 'High risk of privilege escalation and data breach',
+                'recommendation': 'Split into separate permission sets with single responsibilities',
+                'dangerous_permissions': [p['description'] for p in dangerous_perms],
+                'affected_users': affected_usernames,
+                'user_details': user_details  # ADDED
+            })
+        
+        return findings
+    
+    def analyze_profile(self, profile_data: Dict) -> Dict:
+        """
+        Analyze a specific profile for security issues
+        
+        Args:
+            profile_data: Profile information from Salesforce
+            
+        Returns:
+            Profile analysis with risk assessment
+        """
+        profile_name = profile_data.get('Name', 'Unknown')
+        assigned_users = profile_data.get('AssignedUsers', [])
+        
+        analysis = {
+            'profile_name': profile_name,
+            'profile_id': profile_data.get('Id'),
+            'assigned_users_count': len(assigned_users),
+            'dangerous_permissions': [],
+            'critical_issues': [],
+            'warnings': [],
+            'recommendations': [],
+            'risk_score': 0
+        }
+        
+        # Check for dangerous permissions
+        for perm_field, perm_info in self.DANGEROUS_PERMISSIONS.items():
+            if profile_data.get(perm_field) == True:
+                analysis['dangerous_permissions'].append(perm_info['description'])
+                
+                if perm_info['severity'] == 'Critical':
+                    analysis['critical_issues'].append({
+                        'permission': perm_info['description'],
+                        'impact': perm_info['impact'],
+                        'users_affected': len(assigned_users)
+                    })
+                elif perm_info['severity'] == 'High':
+                    analysis['warnings'].append({
+                        'permission': perm_info['description'],
+                        'impact': perm_info['impact']
+                    })
+        
+        # Calculate risk score
+        analysis['risk_score'] = self._calculate_profile_risk_score(analysis)
+        
+        # Generate recommendations
+        analysis['recommendations'] = self._generate_profile_recommendations(analysis, profile_data)
+        
+        return analysis
+    
+    def _analyze_profile_permissions(self, profile: Dict) -> List[Dict]:
+        """
+        Internal method to analyze profile permissions
+        """
+        findings = []
+        profile_name = profile.get('Name', 'Unknown')
+        
+        # Check if it's a standard profile
+        is_standard = not profile.get('IsCustom', False)
+        
+        for perm_field, perm_info in self.DANGEROUS_PERMISSIONS.items():
+            if profile.get(perm_field) == True:
+                severity = perm_info['severity']
+                
+                # Elevate severity if standard profile is modified
+                if is_standard and severity == 'High':
+                    severity = 'Critical'
+                
+                findings.append({
+                    'type': 'Dangerous Permission in Profile',
+                    'severity': severity,
+                    'profile': profile_name,
+                    'profile_id': profile.get('Id'),
+                    'permission': perm_info['description'],
+                    'impact': perm_info['impact'],
+                    'description': f"Profile '{profile_name}' has {perm_info['description']}",
+                    'recommendation': 'Review and restrict this permission or move to Permission Set'
+                })
+        
+        return findings
+    
+    def _calculate_profile_risk_score(self, analysis: Dict) -> int:
+        """
+        Calculate risk score for a profile (0-100)
+        """
+        score = 0
+        
+        # Critical issues add 30 points each
+        score += len(analysis['critical_issues']) * 30
+        
+        # High risk warnings add 15 points each
+        score += len(analysis['warnings']) * 15
+        
+        # More users = higher risk
+        user_count = analysis['assigned_users_count']
+        if user_count > 50:
+            score += 20
+        elif user_count > 20:
+            score += 10
+        
+        return min(100, score)
+    
+    def _generate_profile_recommendations(self, analysis: Dict, profile_data: Dict) -> List[str]:
+        """
+        Generate specific recommendations for profile security
+        """
+        recommendations = []
+        
+        if analysis['critical_issues']:
+            recommendations.append(
+                f"🚨 URGENT: Remove {len(analysis['critical_issues'])} critical permission(s) "
+                f"or migrate to Permission Sets for granular control"
+            )
+        
+        if analysis['assigned_users_count'] > 20:
+            recommendations.append(
+                f"⚠️ {analysis['assigned_users_count']} users assigned to this profile. "
+                "Consider splitting into multiple profiles based on job functions"
+            )
+        
+        if len(analysis['dangerous_permissions']) >= 3:
+            recommendations.append(
+                "⚠️ Profile has multiple dangerous permissions. "
+                "Implement least privilege principle"
+            )
+        
+        if not recommendations:
+            recommendations.append("✅ Profile follows security best practices")
+        
+        return recommendations
+    
+    def _count_dangerous_assignments(self, findings: List[Dict]) -> int:
+        """
+        Count total dangerous permission assignments
+        """
+        count = 0
+        for finding in findings:
+            if finding.get('type') == 'Dangerous Permission in Permission Set':
+                count += finding.get('assigned_users', 0)
+        return count
+    
+    def check_permission_escalation_risk(
+        self,
+        user_permissions: List[str]
+    ) -> Dict:
+        """
+        Check if combination of permissions creates escalation risk
+        
+        Args:
+            user_permissions: List of permission names user has
+            
+        Returns:
+            Risk assessment
+        """
+        risky_combinations = [
+            (['PermissionsAuthorApex', 'PermissionsModifyAllData'], 'Can deploy malicious code with full data access'),
+            (['PermissionsManageUsers', 'PermissionsViewAllData'], 'Can create admin users and access all data'),
+            (['PermissionsCustomizeApplication', 'PermissionsManageRoles'], 'Can manipulate security architecture')
+        ]
+        
+        risks_found = []
+        
+        for combo, impact in risky_combinations:
+            if all(perm in user_permissions for perm in combo):
+                risks_found.append({
+                    'combination': combo,
+                    'impact': impact,
+                    'severity': 'Critical'
+                })
+        
+        return {
+            'has_escalation_risk': len(risks_found) > 0,
+            'risks': risks_found
+        }

prompts.py

@@ -0,0 +1,113 @@
+FULL_SCAN_PROMPT = """You are a Salesforce security expert analyzing an organization for security vulnerabilities.
+
+Organization ID: {org_id}
+
+Analysis Data:
+{analysis_data}
+
+Perform a comprehensive security analysis covering:
+
+1. **Identity & Access Management**
+   - Dormant admin accounts
+   - Excessive admin privileges
+   - Login anomalies
+
+2. **Authorization & Permissions**
+   - Dangerous permission sets (Modify All Data, View All Data)
+   - Permission escalation risks
+   - Least privilege violations
+
+3. **Record-Level Security**
+   - Public Read/Write OWD settings
+   - Overly permissive sharing rules
+   - Data exposure risks
+
+For each vulnerability found:
+- **Severity**: Critical, High, Medium, or Low
+- **Category**: Identity, Permissions, or Sharing
+- **Description**: Clear explanation of the issue
+- **Impact**: Business and security implications
+- **Affected Items**: Users, permission sets, or objects
+- **Recommendation**: Specific remediation steps
+- **Priority**: Immediate, Short-term, or Long-term
+
+Structure your response as follows:
+
+## EXECUTIVE SUMMARY
+[Brief overview of security posture, total vulnerabilities by severity]
+
+## CRITICAL FINDINGS
+[List all critical vulnerabilities]
+
+## HIGH PRIORITY FINDINGS
+[List all high priority vulnerabilities]
+
+## MEDIUM PRIORITY FINDINGS
+[List all medium priority vulnerabilities]
+
+## LOW PRIORITY FINDINGS
+[List all low priority vulnerabilities]
+
+## RECOMMENDED ACTIONS
+[Prioritized list of remediation steps]
+
+## COMPLIANCE NOTES
+[Any compliance implications - SOC 2, GDPR, etc.]
+
+Be specific, actionable, and use clear, non-technical language where possible."""
+
+PROFILE_SCAN_PROMPT = """You are a Salesforce security expert analyzing a specific profile for security vulnerabilities.
+
+Profile ID: {profile_id}
+Profile Name: {profile_name}
+
+Analysis Data:
+{analysis_data}
+
+Analyze this profile for:
+
+1. **System Permissions**
+   - Administrative privileges
+   - Dangerous permissions (View All, Modify All)
+   - Setup access
+
+2. **Object Permissions**
+   - Excessive CRUD permissions
+   - ViewAllRecords and ModifyAllRecords
+   - Unnecessary object access
+
+3. **Field Permissions**
+   - Access to sensitive fields
+   - Edit permissions on critical data
+
+For each vulnerability:
+- **Severity**: Critical, High, Medium, or Low
+- **Type**: System Permission, Object Permission, or Field Permission
+- **Description**: What is the issue
+- **Risk**: What could go wrong
+- **Recommendation**: How to fix it
+
+Structure your response as:
+
+## PROFILE OVERVIEW
+[Summary of profile and its purpose]
+
+## SECURITY ASSESSMENT
+[Overall security rating and key concerns]
+
+## CRITICAL ISSUES
+[Critical vulnerabilities requiring immediate attention]
+
+## HIGH PRIORITY ISSUES
+[High priority issues]
+
+## MEDIUM PRIORITY ISSUES
+[Medium priority issues]
+
+## RECOMMENDATIONS
+[Specific steps to improve security]
+
+## LEAST PRIVILEGE BASELINE
+[What permissions this profile should have based on its apparent purpose]
+
+Be specific and actionable in your recommendations."""

requirements.txt

@@ -0,0 +1,50 @@
+# ============================================================================
+# OPTIMAX SECURITY AGENT - REQUIREMENTS
+# Version: 3.1.0 (Multi-Model AI + Anomaly Detection)
+# ============================================================================
+
+# Core Framework - Gradio 6.3.0 Compatible
+gradio==6.3.0
+fastapi==0.115.5
+uvicorn[standard]==0.32.1
+pydantic==2.10.5
+
+# AI/ML Dependencies - Transformers & PyTorch
+transformers==4.46.0
+torch==2.5.1
+numpy<2.0.0
+sentencepiece==0.1.99
+accelerate==0.34.2
+
+# Machine Learning Libraries - Includes Isolation Forest for Anomaly Detection
+scikit-learn==1.5.2  # ✅ Provides IsolationForest (no additional install needed!)
+scipy==1.14.1
+
+# Salesforce Integration
+simple-salesforce==1.12.4
+requests==2.31.0
+
+# Utilities
+python-dotenv==1.0.0
+
+# ============================================================================
+# NOTES:
+# ============================================================================
+# 
+# ✅ No changes needed from previous version!
+# 
+# scikit-learn==1.5.2 already includes:
+#   - IsolationForest (anomaly detection)
+#   - cosine_similarity (used by AI models)
+#   - All necessary ML utilities
+#
+# Python Version: 3.10, 3.11, or 3.13
+# Tested on: Hugging Face Spaces (Python 3.11)
+#
+# Total Models:
+#   1. CodeBERT (125M) - from transformers
+#   2. RoBERTa (125M) - from transformers
+#   3. SecBERT (110M) - from transformers
+#   4. Isolation Forest - from scikit-learn ✅ NEW!
+#
+# ============================================================================

sharing_analyzer.py

@@ -0,0 +1,129 @@
+
+# analyzers/sharing_analyzer.py
+from typing import Dict, List, Optional
+from datetime import datetime
+
+class SharingAnalyzer:
+    """
+    Analyzes Salesforce sharing model for security vulnerabilities
+    """
+    
+    RISKY_OWD_SETTINGS = {
+        'Public Read/Write': 'Critical',
+        'Public Read/Write/Transfer': 'Critical',
+        'Public Full Access': 'Critical',
+        'Public Read Only': 'High'
+    }
+    
+    def analyze_sharing_settings(self, sharing_settings: Dict) -> Dict:
+        """
+        Analyze organization-wide sharing settings
+        
+        Args:
+            sharing_settings: OWD settings, sharing rules, role hierarchy
+            
+        Returns:
+            Analysis results with findings
+        """
+        findings = []
+        
+        # Analyze OWD settings
+        owd_findings = self._analyze_owd(sharing_settings.get('organization_wide_defaults', []))
+        findings.extend(owd_findings)
+        
+        # Analyze role hierarchy
+        role_findings = self._analyze_role_hierarchy(sharing_settings.get('role_hierarchy', []))
+        findings.extend(role_findings)
+        
+        # Analyze sharing rules
+        rule_findings = self._analyze_sharing_rules(sharing_settings.get('sharing_rules', []))
+        findings.extend(rule_findings)
+        
+        return {
+            'findings': findings,
+            'owd_objects_analyzed': len(sharing_settings.get('organization_wide_defaults', [])),
+            'roles_analyzed': len(sharing_settings.get('role_hierarchy', [])),
+            'sharing_rules_analyzed': len(sharing_settings.get('sharing_rules', []))
+        }
+    
+    def _analyze_owd(self, owd_settings: List[Dict]) -> List[Dict]:
+        """
+        Analyze Organization-Wide Default settings
+        """
+        findings = []
+        
+        sensitive_objects = ['Account', 'Contact', 'Opportunity', 'Contract', 'Case']
+        
+        for setting in owd_settings:
+            obj_name = setting.get('ObjectName', 'Unknown')
+            default_access = setting.get('DefaultAccess', 'Private')
+            
+            # Check if sensitive object has public access
+            if obj_name in sensitive_objects and default_access in self.RISKY_OWD_SETTINGS:
+                severity = self.RISKY_OWD_SETTINGS[default_access]
+                
+                findings.append({
+                    'type': 'Insecure Organization-Wide Default',
+                    'severity': severity,
+                    'object': obj_name,
+                    'current_setting': default_access,
+                    'description': f"{obj_name} has {default_access} OWD setting",
+                    'impact': f"All users can access {obj_name} records by default",
+                    'recommendation': f"Change {obj_name} OWD to Private and use sharing rules for controlled access"
+                })
+        
+        return findings
+    
+    def _analyze_role_hierarchy(self, roles: List[Dict]) -> List[Dict]:
+        """
+        Analyze role hierarchy for security issues
+        """
+        findings = []
+        
+        # Check for flat hierarchy (security issue)
+        roles_without_parent = [r for r in roles if not r.get('ParentRoleId')]
+        
+        if len(roles_without_parent) > len(roles) * 0.5:
+            findings.append({
+                'type': 'Flat Role Hierarchy',
+                'severity': 'Medium',
+                'description': 'Role hierarchy is too flat',
+                'impact': 'Users may have unintended access through role hierarchy',
+                'recommendation': 'Design role hierarchy based on data access needs, not org chart',
+                'roles_without_parent': len(roles_without_parent)
+            })
+        
+        return findings
+    
+    def _analyze_sharing_rules(self, sharing_rules: List[Dict]) -> List[Dict]:
+        """
+        Analyze sharing rules for over-broad access
+        """
+        findings = []
+        
+        for rule in sharing_rules:
+            access_level = rule.get('AccessLevel', 'Read')
+            
+            if access_level in ['Edit', 'All']:
+                findings.append({
+                    'type': 'Broad Sharing Rule',
+                    'severity': 'Medium',
+                    'rule_name': rule.get('DeveloperName', 'Unknown'),
+                    'object': rule.get('SobjectType', 'Unknown'),
+                    'access_level': access_level,
+                    'description': f"Sharing rule grants {access_level} access",
+                    'recommendation': 'Review necessity of Edit/All access in sharing rules'
+                })
+        
+        return findings
+    
+    def check_sharing_bypass_permissions(self, user_permissions: List[str]) -> bool:
+        """
+        Check if user has permissions that bypass sharing rules
+        """
+        bypass_permissions = [
+            'PermissionsViewAllData',
+            'PermissionsModifyAllData'
+        ]
+        
+        return any(perm in user_permissions for perm in bypass_permissions)

vulenerabilities_db.py

@@ -0,0 +1,272 @@
+"""
+Vulnerability Database
+Knowledge base of known Salesforce security vulnerabilities and patterns
+"""
+
+class VulnerabilityDatabase:
+    """Database of known Salesforce security vulnerabilities"""
+    
+    # Dangerous system permissions
+    DANGEROUS_PERMISSIONS = {
+        'ModifyAllData': {
+            'severity': 'critical',
+            'category': 'Data Access',
+            'description': 'User can create, edit, and delete all records regardless of sharing settings',
+            'risk': 'Complete data manipulation capability - can alter financial records, delete data, bypass all security',
+            'recommendation': 'Remove immediately. Grant object-specific permissions instead.',
+            'compliance_impact': 'SOC 2, GDPR, HIPAA violation risk',
+            'cve_reference': None,
+            'owasp_category': 'A01:2021 - Broken Access Control'
+        },
+        'ViewAllData': {
+            'severity': 'critical',
+            'category': 'Data Access',
+            'description': 'User can view all records regardless of sharing settings',
+            'risk': 'Complete visibility to sensitive data including PII, financial data, trade secrets',
+            'recommendation': 'Remove and grant object-specific read permissions only where needed.',
+            'compliance_impact': 'GDPR, HIPAA, PCI-DSS violation risk',
+            'cve_reference': None,
+            'owasp_category': 'A01:2021 - Broken Access Control'
+        },
+        'ManageUsers': {
+            'severity': 'high',
+            'category': 'User Management',
+            'description': 'User can create, edit, and deactivate users',
+            'risk': 'Can create admin accounts, lock out legitimate users, disable security controls',
+            'recommendation': 'Restrict to designated HR and IT administrators only.',
+            'compliance_impact': 'SOC 2 - Access control violation',
+            'cve_reference': None,
+            'owasp_category': 'A01:2021 - Broken Access Control'
+        },
+        'CustomizeApplication': {
+            'severity': 'high',
+            'category': 'Configuration',
+            'description': 'User can modify org configuration including security settings',
+            'risk': 'Can weaken security controls, modify validation rules, change workflows',
+            'recommendation': 'Restrict to administrators and approved developers only.',
+            'compliance_impact': 'SOC 2 - Change management violation',
+            'cve_reference': None,
+            'owasp_category': 'A05:2021 - Security Misconfiguration'
+        },
+        'AuthorApex': {
+            'severity': 'high',
+            'category': 'Development',
+            'description': 'User can write and deploy Apex code',
+            'risk': 'Can introduce backdoors, data exfiltration code, malicious logic',
+            'recommendation': 'Restrict to vetted developers only. Implement code review process.',
+            'compliance_impact': 'SOC 2 - Development security violation',
+            'cve_reference': None,
+            'owasp_category': 'A04:2021 - Insecure Design'
+        },
+        'ViewSetup': {
+            'severity': 'medium',
+            'category': 'Information Disclosure',
+            'description': 'User can view setup and configuration information',
+            'risk': 'Can map out security architecture for targeted attacks',
+            'recommendation': 'Limit to administrators and necessary support staff.',
+            'compliance_impact': 'Information disclosure',
+            'cve_reference': None,
+            'owasp_category': 'A01:2021 - Broken Access Control'
+        },
+        'ManageInternalUsers': {
+            'severity': 'high',
+            'category': 'User Management',
+            'description': 'Can manage internal users including password resets',
+            'risk': 'Account takeover, unauthorized access escalation',
+            'recommendation': 'Restrict to IT security team only.',
+            'compliance_impact': 'SOC 2 - Access control',
+            'cve_reference': None,
+            'owasp_category': 'A01:2021 - Broken Access Control'
+        }
+    }
+    
+    # Sharing model vulnerabilities
+    SHARING_VULNERABILITIES = {
+        'PublicReadWrite': {
+            'severity': 'critical',
+            'objects': ['Account', 'Contact', 'Opportunity', 'Lead', 'Case', 'Quote', 'Contract'],
+            'description': 'All users can read and write all records',
+            'risk': 'Data integrity compromise, unauthorized modifications, compliance violations',
+            'recommendation': 'Change to Private. Use sharing rules for controlled access.',
+            'compliance_impact': 'GDPR, SOC 2, PCI-DSS violations'
+        },
+        'PublicRead': {
+            'severity': 'high',
+            'objects': ['Account', 'Contact', 'Lead', 'Case', 'Opportunity'],
+            'description': 'All users can view all records',
+            'risk': 'Sensitive data exposure, privacy violations',
+            'recommendation': 'Change to Private. Implement role hierarchy or sharing rules.',
+            'compliance_impact': 'GDPR, HIPAA privacy violations'
+        },
+        'ControlledByParent': {
+            'severity': 'medium',
+            'objects': ['Contact', 'Opportunity', 'Case'],
+            'description': 'Inherits sharing from parent record',
+            'risk': 'Unintended access if parent sharing is too permissive',
+            'recommendation': 'Verify parent object sharing is appropriate.',
+            'compliance_impact': 'Potential data leakage'
+        }
+    }
+    
+    # Identity and access vulnerabilities
+    IDENTITY_VULNERABILITIES = {
+        'DormantAdminAccount': {
+            'severity': 'high',
+            'threshold_days': 90,
+            'description': 'Administrator account inactive for 90+ days',
+            'risk': 'Orphaned privileged accounts are prime targets for attackers',
+            'recommendation': 'Deactivate or remove admin privileges immediately.',
+            'compliance_impact': 'SOC 2, PCI-DSS - Access review violation'
+        },
+        'NoMFA': {
+            'severity': 'high',
+            'description': 'User does not have MFA enabled',
+            'risk': 'Account vulnerable to credential theft and phishing',
+            'recommendation': 'Enable MFA for all users, especially administrators.',
+            'compliance_impact': 'SOC 2, PCI-DSS - Authentication requirement'
+        },
+        'FailedLoginAttempts': {
+            'severity': 'medium',
+            'threshold': 5,
+            'description': 'Multiple failed login attempts detected',
+            'risk': 'Possible brute force attack or credential stuffing',
+            'recommendation': 'Investigate source IPs, consider account lockout policies.',
+            'compliance_impact': 'Security monitoring requirement'
+        },
+        'UnusualLoginTime': {
+            'severity': 'medium',
+            'description': 'Login outside normal business hours',
+            'risk': 'Compromised credentials or unauthorized access',
+            'recommendation': 'Implement time-based login restrictions, investigate anomalies.',
+            'compliance_impact': 'Anomaly detection'
+        }
+    }
+    
+    # Known attack patterns
+    ATTACK_PATTERNS = {
+        'PrivilegeEscalation': {
+            'indicators': [
+                'Multiple permission sets assigned to non-admin user',
+                'Permission set with admin-level permissions',
+                'Profile modification to add dangerous permissions'
+            ],
+            'severity': 'critical',
+            'description': 'User gaining elevated privileges through permission accumulation',
+            'mitigation': 'Regular permission audits, least privilege enforcement'
+        },
+        'DataExfiltration': {
+            'indicators': [
+                'Excessive report exports',
+                'API usage spikes',
+                'Large data exports',
+                'Access to View All Data'
+            ],
+            'severity': 'critical',
+            'description': 'Suspicious data access patterns indicating potential theft',
+            'mitigation': 'Monitor API usage, implement data loss prevention'
+        },
+        'AccountTakeover': {
+            'indicators': [
+                'Login from new geographic location',
+                'Multiple failed logins followed by success',
+                'Password reset without MFA',
+                'Unusual activity after login'
+            ],
+            'severity': 'critical',
+            'description': 'Compromised user account being accessed by attacker',
+            'mitigation': 'Enforce MFA, monitor login anomalies, implement IP restrictions'
+        }
+    }
+    
+    # Compliance requirements
+    COMPLIANCE_MAPPINGS = {
+        'SOC2': {
+            'CC6.1': 'Logical and physical access controls',
+            'CC6.2': 'Prior to issuing system credentials',
+            'CC6.3': 'Removes access when terminated',
+            'CC6.6': 'Manages system-to-system communications',
+            'CC7.2': 'Monitors system components'
+        },
+        'GDPR': {
+            'Article_5': 'Principles relating to processing',
+            'Article_25': 'Data protection by design and default',
+            'Article_32': 'Security of processing'
+        },
+        'HIPAA': {
+            '164.308': 'Administrative safeguards',
+            '164.312': 'Technical safeguards'
+        },
+        'PCI_DSS': {
+            'Req_7': 'Restrict access to cardholder data',
+            'Req_8': 'Identify and authenticate access',
+            'Req_10': 'Track and monitor all access'
+        }
+    }
+    
+    @classmethod
+    def get_permission_info(cls, permission_name: str) -> dict:
+        """Get information about a specific permission"""
+        return cls.DANGEROUS_PERMISSIONS.get(permission_name, {
+            'severity': 'low',
+            'description': f'Permission: {permission_name}',
+            'recommendation': 'Review if this permission is necessary.'
+        })
+    
+    @classmethod
+    def get_sharing_vulnerability(cls, sharing_model: str) -> dict:
+        """Get vulnerability info for sharing model"""
+        for vuln_type, info in cls.SHARING_VULNERABILITIES.items():
+            if vuln_type in sharing_model:
+                return info
+        return {}
+    
+    @classmethod
+    def get_identity_vulnerability(cls, vuln_type: str) -> dict:
+        """Get identity vulnerability information"""
+        return cls.IDENTITY_VULNERABILITIES.get(vuln_type, {})
+    
+    @classmethod
+    def check_compliance_impact(cls, finding: dict) -> list:
+        """Check which compliance frameworks are impacted"""
+        impacts = []
+        
+        if finding.get('severity') in ['critical', 'high']:
+            impacts.extend(['SOC2', 'GDPR'])
+            
+        if 'data' in finding.get('category', '').lower():
+            impacts.extend(['GDPR', 'HIPAA'])
+            
+        if 'access' in finding.get('type', '').lower():
+            impacts.extend(['SOC2', 'PCI_DSS'])
+        
+        return list(set(impacts))
+    
+    @classmethod
+    def get_remediation_priority(cls, severity: str, compliance_impact: list) -> int:
+        """Calculate remediation priority (1=highest, 5=lowest)"""
+        base_priority = {
+            'critical': 1,
+            'high': 2,
+            'medium': 3,
+            'low': 4,
+            'info': 5
+        }.get(severity, 5)
+        
+        # Increase priority if compliance is impacted
+        if compliance_impact and base_priority > 1:
+            base_priority -= 1
+        
+        return base_priority
+    
+    @classmethod
+    def get_similar_vulnerabilities(cls, vulnerability_type: str) -> list:
+        """Get similar vulnerabilities to watch for"""
+        
+        similarity_map = {
+            'ModifyAllData': ['ViewAllData', 'ManageUsers'],
+            'ViewAllData': ['ModifyAllData', 'ViewSetup'],
+            'PublicReadWrite': ['PublicRead', 'ControlledByParent'],
+            'DormantAdminAccount': ['NoMFA', 'UnusualLoginTime']
+        }
+        
+        return similarity_map.get(vulnerability_type, [])