Create arf_simulation.py

utils/arf_simulation.py

@@ -0,0 +1,148 @@
+"""
+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"
+        }