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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"
} |