AI audit

"""
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)