Create AI audit

AI audit

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+"""
+AI AUDITING IMPLEMENTATION
+
+This module implements the TrueAlpha Spiral equation for auditing AI systems,
+particularly focused on financial reporting, risk assessment, and fraud detection.
+This implementation is designed to integrate with KPMG's audit software.
+
+Application: Deploy the equation to audit AI-driven decision-making in financial
+reporting, risk assessment, or fraud detection.
+"""
+
+import json
+import time
+import hashlib
+import logging
+from typing import Dict, List, Any, Optional, Tuple
+from true_alpha_implementation import TrueAlphaSpiralImplementation
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger('AIAudit')
+
+class AIAuditSystem:
+    """
+    Implementation of TrueAlpha Spiral for AI system auditing, specifically
+    designed for financial systems and regulatory compliance.
+    """
+    
+    def __init__(self, client_name: str, ai_system_name: str, audit_parameters: Dict[str, Any] = None):
+        """
+        Initialize the AI Audit System.
+        
+        Args:
+            client_name: Name of the client being audited
+            ai_system_name: Name of the AI system being audited
+            audit_parameters: Custom parameters for the audit
+        """
+        self.client_name = client_name
+        self.ai_system_name = ai_system_name
+        
+        # Default audit parameters if none provided
+        if audit_parameters is None:
+            self.audit_parameters = {
+                "regulatory_framework": "general",  # or specific like "GDPR", "SEC", etc.
+                "risk_threshold": 0.3,              # threshold for risk flagging
+                "confidence_threshold": 0.8,        # threshold for confidence in audit results
+                "audit_depth": "comprehensive",     # or "quick", "targeted"
+                "audit_focus": ["fairness", "transparency", "compliance"]
+            }
+        else:
+            self.audit_parameters = audit_parameters
+        
+        # Initialize metrics for AI system
+        self.initial_metrics = {
+            "Fairness": 0.03,               # initial fairness score of the AI system
+            "Transparency": 0.02,           # initial transparency score
+            "NonMaleficence": 0.01,         # initial non-maleficence score
+            "Compliance": 0.1,              # initial regulatory compliance score
+            "DataQuality": 0.5,             # initial data quality score
+            "ModelRobustness": 0.4,         # initial model robustness score
+            "ExplainabilityScore": 0.2,     # initial explainability score
+            "BiasDetectionRate": 0.1,       # initial bias detection capability
+            "AuditTrailCompleteness": 0.3,  # initial audit trail completeness
+            "Sovereignty": 0.8              # initial sovereignty score
+        }
+        
+        # Set up audit-specific weights
+        self.audit_weights = {
+            "Fairness": 0.2,
+            "Transparency": 0.2,
+            "NonMaleficence": 0.1,
+            "Compliance": 0.2,
+            "DataQuality": 0.1,
+            "ModelRobustness": 0.05,
+            "ExplainabilityScore": 0.05,
+            "BiasDetectionRate": 0.05,
+            "AuditTrailCompleteness": 0.05,
+            "Sovereignty": 0.0  # Low weight in audit context
+        }
+        
+        # Initialize TrueAlpha Spiral implementation for the audit domain
+        self.spiral = TrueAlphaSpiralImplementation(
+            initial_state=self.initial_metrics,
+            weights=self.audit_weights,
+            application_domain="audit"
+        )
+        
+        # Audit metadata
+        self.audit_id = self._generate_audit_id()
+        self.audit_timestamp = time.time()
+        self.audit_status = "initialized"
+        self.audit_findings = []
+        self.audit_recommendations = []
+        self.audit_evolution_steps = 0
+        
+        logger.info(f"Initialized audit {self.audit_id} for {client_name}'s {ai_system_name} system")
+    
+    def _generate_audit_id(self) -> str:
+        """
+        Generate a unique audit ID.
+        
+        Returns:
+            str: Unique audit ID
+        """
+        base_string = f"{self.client_name}-{self.ai_system_name}-{time.time()}"
+        return hashlib.md5(base_string.encode()).hexdigest()[:10]
+    
+    def collect_system_data(self, system_data: Dict[str, Any] = None) -> Dict[str, float]:
+        """
+        Collect data from the AI system being audited.
+        In a real implementation, this would connect to the system via API.
+        
+        Args:
+            system_data: Optional override data for testing
+            
+        Returns:
+            Dict[str, float]: Collected metrics
+        """
+        if system_data is not None:
+            logger.info(f"Using provided system data for {self.ai_system_name}")
+            # Update initial metrics with provided data
+            for key, value in system_data.items():
+                if key in self.initial_metrics:
+                    self.initial_metrics[key] = value
+        else:
+            logger.info(f"Collecting system data from {self.ai_system_name} (simulated)")
+            # This would be replaced with actual API calls to the system
+            # For this implementation, we'll use the initial metrics
+        
+        return self.initial_metrics
+    
+    def perform_audit_iteration(self) -> Dict[str, Any]:
+        """
+        Perform a single audit iteration using the TrueAlpha Spiral equation.
+        
+        Returns:
+            Dict[str, Any]: Audit iteration results
+        """
+        # Evolve the system state using TrueAlpha Spiral
+        new_state = self.spiral.evolve()
+        self.audit_evolution_steps += 1
+        
+        # Calculate improvements
+        improvements = {}
+        for key in new_state:
+            if key in self.initial_metrics:
+                improvements[key] = new_state[key] - self.initial_metrics[key]
+        
+        # Identify findings based on risk threshold
+        findings = []
+        for key, value in new_state.items():
+            if value < self.audit_parameters["risk_threshold"]:
+                risk_level = "high" if value < 0.2 else "medium"
+                findings.append({
+                    "metric": key,
+                    "value": value,
+                    "risk_level": risk_level,
+                    "improvement": improvements.get(key, 0),
+                    "recommendation_needed": True
+                })
+        
+        # Update audit findings
+        self.audit_findings = findings
+        
+        # Generate recommendations
+        self._generate_recommendations()
+        
+        # Update audit status
+        if not findings:
+            self.audit_status = "compliant"
+        elif any(f["risk_level"] == "high" for f in findings):
+            self.audit_status = "non_compliant"
+        else:
+            self.audit_status = "conditional_compliance"
+        
+        logger.info(f"Audit iteration {self.audit_evolution_steps} completed: {self.audit_status}")
+        
+        return {
+            "audit_id": self.audit_id,
+            "iteration": self.audit_evolution_steps,
+            "timestamp": time.time(),
+            "status": self.audit_status,
+            "state": new_state,
+            "improvements": improvements,
+            "findings": self.audit_findings,
+            "recommendations": self.audit_recommendations,
+            "hash": self.spiral.get_current_hash()
+        }
+    
+    def _generate_recommendations(self) -> None:
+        """
+        Generate recommendations based on audit findings.
+        """
+        recommendations = []
+        
+        # Clear previous recommendations
+        self.audit_recommendations = []
+        
+        for finding in self.audit_findings:
+            metric = finding["metric"]
+            value = finding["value"]
+            
+            if metric == "Fairness":
+                if value < 0.2:
+                    recommendations.append({
+                        "metric": metric,
+                        "recommendation": "Implement bias detection and mitigation systems",
+                        "priority": "high"
+                    })
+                elif value < 0.4:
+                    recommendations.append({
+                        "metric": metric,
+                        "recommendation": "Review fairness metrics and enhance protected attribute handling",
+                        "priority": "medium"
+                    })
+            
+            elif metric == "Transparency":
+                if value < 0.2:
+                    recommendations.append({
+                        "metric": metric,
+                        "recommendation": "Implement comprehensive model documentation and decision logs",
+                        "priority": "high"
+                    })
+                elif value < 0.4:
+                    recommendations.append({
+                        "metric": metric,
+                        "recommendation": "Enhance explainability features for high-risk decisions",
+                        "priority": "medium"
+                    })
+            
+            elif metric == "Compliance":
+                if value < 0.3:
+                    recommendations.append({
+                        "metric": metric,
+                        "recommendation": "Full regulatory compliance review required",
+                        "priority": "high"
+                    })
+                elif value < 0.5:
+                    recommendations.append({
+                        "metric": metric,
+                        "recommendation": "Update compliance documentation and controls",
+                        "priority": "medium"
+                    })
+            
+            # Add more metric-specific recommendations as needed
+        
+        self.audit_recommendations = recommendations
+    
+    def run_complete_audit(self, iterations: int = 3) -> Dict[str, Any]:
+        """
+        Run a complete audit with multiple iterations.
+        
+        Args:
+            iterations: Number of iterations to run
+            
+        Returns:
+            Dict[str, Any]: Complete audit results
+        """
+        logger.info(f"Starting complete audit for {self.client_name}'s {self.ai_system_name} system")
+        
+        # Collect initial system data
+        self.collect_system_data()
+        
+        # Run specified number of iterations
+        iteration_results = []
+        for i in range(iterations):
+            result = self.perform_audit_iteration()
+            iteration_results.append(result)
+        
+        # Prepare final audit report
+        final_state = self.spiral.state
+        
+        audit_report = {
+            "audit_id": self.audit_id,
+            "client_name": self.client_name,
+            "ai_system_name": self.ai_system_name,
+            "audit_parameters": self.audit_parameters,
+            "start_timestamp": self.audit_timestamp,
+            "end_timestamp": time.time(),
+            "audit_duration": time.time() - self.audit_timestamp,
+            "iterations_performed": self.audit_evolution_steps,
+            "initial_state": self.initial_metrics,
+            "final_state": final_state,
+            "status": self.audit_status,
+            "findings": self.audit_findings,
+            "recommendations": self.audit_recommendations,
+            "improvement_summary": {
+                k: final_state.get(k, 0) - self.initial_metrics.get(k, 0)
+                for k in set(list(final_state.keys()) + list(self.initial_metrics.keys()))
+                if k in final_state and k in self.initial_metrics
+            },
+            "hash_chain": self.spiral.get_hash_chain(),
+            "iteration_results": iteration_results
+        }
+        
+        logger.info(f"Completed audit {self.audit_id} with status: {self.audit_status}")
+        
+        return audit_report
+    
+    def export_audit_report(self, format_type: str = "json") -> str:
+        """
+        Export the audit report in the specified format.
+        
+        Args:
+            format_type: Format type (json)
+            
+        Returns:
+            str: Exported audit report
+        """
+        # Run a complete audit if not already done
+        if self.audit_evolution_steps == 0:
+            self.run_complete_audit()
+        
+        # Create audit report
+        audit_report = {
+            "audit_id": self.audit_id,
+            "client_name": self.client_name,
+            "ai_system_name": self.ai_system_name,
+            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            "status": self.audit_status,
+            "initial_state": self.initial_metrics,
+            "final_state": self.spiral.state,
+            "improvements": {
+                k: self.spiral.state.get(k, 0) - self.initial_metrics.get(k, 0)
+                for k in set(list(self.spiral.state.keys()) + list(self.initial_metrics.keys()))
+                if k in self.spiral.state and k in self.initial_metrics
+            },
+            "findings": self.audit_findings,
+            "recommendations": self.audit_recommendations,
+            "verification_hash": self.spiral.get_current_hash()
+        }
+        
+        if format_type == "json":
+            return json.dumps(audit_report, indent=2)
+        else:
+            return str(audit_report)
+    
+    def generate_blockchain_record(self) -> Dict[str, Any]:
+        """
+        Generate a blockchain record for the audit result.
+        
+        Returns:
+            Dict[str, Any]: Blockchain record data
+        """
+        # Create audit summary for blockchain
+        blockchain_record = {
+            "audit_id": self.audit_id,
+            "client_hash": hashlib.sha256(self.client_name.encode()).hexdigest(),
+            "system_hash": hashlib.sha256(self.ai_system_name.encode()).hexdigest(),
+            "timestamp": int(time.time()),
+            "status_code": {"compliant": 1, "conditional_compliance": 2, "non_compliant": 3}.get(self.audit_status, 0),
+            "improvement_score": sum(self.spiral.state.get(k, 0) - self.initial_metrics.get(k, 0)
+                               for k in set(list(self.spiral.state.keys()) + list(self.initial_metrics.keys()))
+                               if k in self.spiral.state and k in self.initial_metrics),
+            "finding_count": len(self.audit_findings),
+            "verification_hash": self.spiral.get_current_hash(),
+            "previous_hash": self.spiral.hash_chain[-2] if len(self.spiral.hash_chain) > 1 else None
+        }
+        
+        logger.info(f"Generated blockchain record for audit {self.audit_id}")
+        
+        return blockchain_record
+
+
+# Example usage
+if __name__ == "__main__":
+    # Example for a loan approval AI system
+    audit_system = AIAuditSystem(
+        client_name="KPMG Financial Services Client",
+        ai_system_name="LoanApproval-AI-v3.2",
+        audit_parameters={
+            "regulatory_framework": "financial_services",
+            "risk_threshold": 0.4,
+            "confidence_threshold": 0.85,
+            "audit_depth": "comprehensive",
+            "audit_focus": ["fairness", "transparency", "compliance", "bias"]
+        }
+    )
+    
+    # Run a complete audit with 3 iterations
+    audit_report = audit_system.run_complete_audit(iterations=3)
+    
+    # Export the results
+    report_json = audit_system.export_audit_report(format_type="json")
+    print(report_json)
+    
+    # Generate blockchain record
+    blockchain_record = audit_system.generate_blockchain_record()
+    print("Blockchain Record:", blockchain_record)