utils/arf_simulation.py

"""
ARF Simulation - Fallback when real ARF is not available
"""

import random
from datetime import datetime
from typing import Dict, Any, Optional

class RiskEngine:
    def assess(self, action: str, context: Dict) -> Dict:
        """Simulate risk assessment"""
        action_lower = action.lower()
        risk = 0.25
        
        if "drop" in action_lower and "database" in action_lower:
            risk = 0.85
            factors = ["Destructive operation", "Data loss", "Production impact"]
        elif "delete" in action_lower:
            risk = 0.65
            factors = ["Data deletion", "Write operation"]
        elif "update" in action_lower and "where" not in action_lower:
            risk = 0.75
            factors = ["Mass update", "No WHERE clause"]
        elif "grant" in action_lower:
            risk = 0.55
            factors = ["Privilege escalation", "Security implications"]
        else:
            risk = 0.35 + random.random() * 0.2
            factors = ["Standard operation"]
        
        # Adjust based on context
        if "production" in str(context).lower():
            risk *= 1.3
            factors.append("Production environment")
        
        risk = min(0.95, max(0.25, risk))
        
        return {
            "risk_score": risk,
            "confidence": 0.8 + random.random() * 0.15,
            "risk_factors": factors,
            "timestamp": datetime.now().isoformat()
        }

class PolicyEngine:
    def evaluate(self, action: Any, risk_score: float, context: Dict) -> str:
        """Simulate policy evaluation"""
        if risk_score > 0.7:
            return "HIGH_RISK"
        elif risk_score > 0.4:
            return "MODERATE_RISK"
        return "LOW_RISK"

class ActionValidator:
    def parse_action(self, action: str) -> Dict:
        """Parse action into structured format"""
        return {
            "raw": action,
            "type": self._classify_action(action),
            "tokens": action.split(),
            "parsed_at": datetime.now().isoformat()
        }
    
    def _classify_action(self, action: str) -> str:
        """Classify action type"""
        action_lower = action.lower()
        if "drop" in action_lower:
            return "DESTRUCTIVE"
        elif "delete" in action_lower:
            return "DELETE"
        elif "update" in action_lower:
            return "UPDATE"
        elif "grant" in action_lower:
            return "PRIVILEGE"
        else:
            return "QUERY"

class LicenseManager:
    def validate(self, license_key: Optional[str] = None) -> Dict:
        """Validate license key"""
        if not license_key:
            return {"tier": "oss", "name": "OSS Edition", "features": []}
        
        key_upper = license_key.upper()
        
        if "ARF-TRIAL" in key_upper:
            return {
                "tier": "trial",
                "name": "Trial Edition",
                "features": ["mechanical_gates", "email_support"],
                "expires": (datetime.now().timestamp() + 14 * 86400)
            }
        elif "ARF-PRO" in key_upper:
            return {
                "tier": "professional",
                "name": "Professional Edition",
                "features": ["mechanical_gates", "24_7_support", "advanced_gates"],
                "price": "$5,000/month"
            }
        elif "ARF-ENTERPRISE" in key_upper:
            return {
                "tier": "enterprise",
                "name": "Enterprise Edition",
                "features": ["full_mechanical_gates", "dedicated_support", "custom_gates", "soc2_compliance"],
                "price": "$15,000/month"
            }
        
        return {"tier": "oss", "name": "OSS Edition", "features": []}

class BayesianRiskScorer:
    def assess(self, action: Dict, context: Dict) -> Dict:
        """Simulate Bayesian risk assessment"""
        # Simplified Bayesian scoring
        action_type = action.get("type", "QUERY")
        
        # Priors based on action type
        priors = {
            "DESTRUCTIVE": 0.7,
            "DELETE": 0.6,
            "UPDATE": 0.5,
            "PRIVILEGE": 0.4,
            "QUERY": 0.2
        }
        
        prior = priors.get(action_type, 0.5)
        
        # Likelihood adjustments
        context_str = str(context).lower()
        likelihood = 1.0
        
        if "production" in context_str:
            likelihood *= 1.3
        if "junior" in context_str or "intern" in context_str:
            likelihood *= 1.2
        
        # Posterior (simplified)
        posterior = (prior * likelihood) / (prior * likelihood + (1 - prior))
        
        # Add some variance
        posterior += random.uniform(-0.05, 0.05)
        posterior = max(0.25, min(0.95, posterior))
        
        return {
            "risk_score": posterior,
            "confidence": 0.85,
            "risk_factors": [f"{action_type} operation"],
            "method": "bayesian_simulation"
        }