Credit Report Agent - Stage 4: AI-Integrated¶

class AIInputValidator:
    """
    ✅ Validate and sanitize data before AI processing
    """
    def prepare_for_model(self, credit_report):
        # Remove direct identifiers
        sanitized = self._anonymize_pii(credit_report)

        # Validate feature ranges
        sanitized = self._validate_features(sanitized)

        # Detect adversarial inputs
        if self._is_adversarial(sanitized):
            raise SecurityError("Adversarial input detected")

        # Apply differential privacy
        sanitized = self._add_noise(sanitized)

        return sanitized

    def _anonymize_pii(self, report):
        """Remove direct identifiers before ML"""
        return {
            # ❌ Remove: SSN, name, address
            # ✅ Keep: Anonymized features
            'credit_utilization': report['utilization'],
            'payment_history_score': self._score_history(report['history']),
            'account_age_months': report['oldest_account_age'],
            'inquiry_count': len(report['inquiries']),
            'account_types': self._encode_types(report['accounts']),
            # No PII in ML input
        }

    def _is_adversarial(self, features):
        """Detect adversarial examples"""
        # Check for unusual feature combinations
        # Check for out-of-distribution inputs
        # Check for gradient-based attacks

        # Example: Credit utilization >100% is impossible
        if features['credit_utilization'] > 1.0:
            return True

        # Check feature correlations
        if self._check_anomalous_correlations(features):
            return True

        return False

class ModelAccessControl:
    """
    ✅ Control which users/processes can access AI models
    """
    def __init__(self):
        self.model_permissions = {
            'fraud_detector': ['analyst', 'system'],
            'risk_assessor': ['analyst', 'underwriter', 'system'],
            'pattern_analyzer': ['data_scientist', 'system']
        }

    def check_model_access(self, user_role, model_name):
        """Verify user can access specific model"""
        if model_name not in self.model_permissions:
            raise ValueError(f"Unknown model: {model_name}")

        allowed_roles = self.model_permissions[model_name]

        if user_role not in allowed_roles:
            audit_log('unauthorized_model_access', {
                'user_role': user_role,
                'model': model_name
            })
            return False

        return True

    def rate_limit_model(self, user_id, model_name):
        """Prevent model abuse"""
        # Different rate limits per model
        limits = {
            'fraud_detector': 100,  # 100 requests/hour
            'risk_assessor': 50,    # 50 requests/hour
            'pattern_analyzer': 10  # 10 requests/hour (expensive)
        }

        return self.check_rate_limit(
            user_id,
            model_name,
            limit=limits[model_name]
        )

# Usage
@app.route('/ai/analyze', methods=['POST'])
@require_auth
def ai_analyze(user_id, user_role):
    model_name = request.json['model']

    # Check permissions
    if not model_access.check_model_access(user_role, model_name):
        return {'error': 'Access denied'}, 403

    # Check rate limit
    if not model_access.rate_limit_model(user_id, model_name):
        return {'error': 'Rate limit exceeded'}, 429

    # Process request
    return run_analysis(model_name, request.json['data'])

import numpy as np

class DifferentialPrivacy:
    """
    ✅ Add calibrated noise for privacy preservation
    """
    def __init__(self, epsilon=1.0, delta=1e-5):
        self.epsilon = epsilon  # Privacy budget
        self.delta = delta      # Privacy parameter

    def add_laplace_noise(self, value, sensitivity):
        """Add Laplace noise for differential privacy"""
        scale = sensitivity / self.epsilon
        noise = np.random.laplace(0, scale)
        return value + noise

    def privatize_features(self, features):
        """Apply differential privacy to features"""
        privatized = {}

        # Add noise to continuous features
        privatized['credit_utilization'] = self.add_laplace_noise(
            features['credit_utilization'],
            sensitivity=0.1  # Max change in utilization
        )

        privatized['account_age_months'] = self.add_laplace_noise(
            features['account_age_months'],
            sensitivity=1.0  # Max 1 month sensitivity
        )

        # Categorical features - randomized response
        privatized['account_types'] = self._randomized_response(
            features['account_types']
        )

        return privatized

    def _randomized_response(self, value):
        """Randomized response for categorical data"""
        # With probability (1-epsilon), flip the value
        if np.random.random() > self.epsilon:
            # Return random category instead
            return self._random_category()
        return value

# Usage in AI pipeline
dp = DifferentialPrivacy(epsilon=1.0)

def process_with_privacy(credit_report):
    # Extract features
    features = extract_features(credit_report)

    # Apply differential privacy
    private_features = dp.privatize_features(features)

    # Run ML model on private data
    result = model.predict(private_features)

    return result

import shap  # SHapley Additive exPlanations

class ExplainableAI:
    """
    ✅ Provide explanations for AI decisions
    """
    def __init__(self, model):
        self.model = model
        self.explainer = shap.TreeExplainer(model)

    def explain_prediction(self, features, prediction):
        """Generate explanation for model decision"""
        # Calculate SHAP values
        shap_values = self.explainer.shap_values(features)

        # Get feature importance
        feature_importance = self._calculate_importance(
            features,
            shap_values
        )

        # Generate human-readable explanation
        explanation = self._generate_explanation(
            prediction,
            feature_importance
        )

        # Audit the explanation
        self._audit_explanation(features, prediction, explanation)

        return explanation

    def _generate_explanation(self, prediction, importance):
        """Create human-readable explanation"""
        explanation = {
            'decision': prediction['category'],
            'confidence': prediction['confidence'],
            'primary_factors': [],
            'reasoning': ''
        }

        # Get top 3 factors
        top_factors = sorted(
            importance.items(),
            key=lambda x: abs(x[1]),
            reverse=True
        )[:3]

        for feature, impact in top_factors:
            explanation['primary_factors'].append({
                'factor': self._feature_to_english(feature),
                'impact': 'positive' if impact > 0 else 'negative',
                'magnitude': abs(impact)
            })

        # Generate reasoning text
        explanation['reasoning'] = self._create_reasoning_text(
            explanation['primary_factors']
        )

        return explanation

    def _feature_to_english(self, feature_name):
        """Convert feature names to readable descriptions"""
        mappings = {
            'credit_utilization': 'Credit card usage',
            'payment_history_score': 'Payment track record',
            'account_age_months': 'Length of credit history',
            'inquiry_count': 'Recent credit applications'
        }
        return mappings.get(feature_name, feature_name)

    def _audit_explanation(self, features, prediction, explanation):
        """Log explanation for compliance"""
        audit_log('ai_decision_explained', {
            'model_version': self.model.version,
            'prediction': prediction,
            'explanation': explanation,
            'timestamp': datetime.utcnow()
        })

# Usage
explainer = ExplainableAI(fraud_model)

prediction = fraud_model.predict(features)
explanation = explainer.explain_prediction(features, prediction)

# Return both prediction and explanation
return {
    'fraud_detected': prediction['fraud'],
    'confidence': prediction['confidence'],
    'explanation': explanation
}

class ModelMonitor:
    """
    ✅ Monitor ML model for security issues
    """
    def __init__(self):
        self.baseline_performance = None
        self.drift_detector = DriftDetector()

    def monitor_prediction(self, features, prediction):
        """Monitor each prediction for anomalies"""
        # Check for model drift
        if self.drift_detector.detect_drift(features):
            alert('model_drift_detected', {
                'severity': 'high',
                'features': features
            })

        # Check for adversarial patterns
        if self._is_adversarial_pattern(features, prediction):
            alert('adversarial_attack_suspected', {
                'severity': 'critical',
                'features': features
            })

        # Check confidence distribution
        if self._unusual_confidence(prediction):
            alert('unusual_confidence_pattern', {
                'severity': 'medium',
                'confidence': prediction['confidence']
            })

        # Track performance metrics
        self._update_metrics(features, prediction)

    def _is_adversarial_pattern(self, features, prediction):
        """Detect adversarial attack patterns"""
        # Check for high-confidence wrong predictions
        # Check for unusual feature combinations
        # Check for gradient-based perturbations

        # Example: Very high confidence on edge case
        if prediction['confidence'] > 0.99:
            if self._is_edge_case(features):
                return True

        return False

    def detect_model_poisoning(self, training_data):
        """Detect if model was poisoned during training"""
        # Check for data poisoning
        suspicious_samples = []

        for sample in training_data:
            if self._is_poisoned_sample(sample):
                suspicious_samples.append(sample)

        if len(suspicious_samples) > 0:
            alert('model_poisoning_suspected', {
                'severity': 'critical',
                'sample_count': len(suspicious_samples)
            })
            return True

        return False

# Usage - monitor every prediction
monitor = ModelMonitor()

@app.route('/ai/predict', methods=['POST'])
def predict():
    features = request.json['features']

    prediction = model.predict(features)

    # Monitor for security issues
    monitor.monitor_prediction(features, prediction)

    return prediction

class SecureModelVersioning:
    """
    ✅ Secure model update and rollback
    """
    def __init__(self):
        self.current_version = None
        self.version_history = []

    def deploy_model(self, model_file, version, signature):
        """Deploy new model version securely"""
        # Verify model signature
        if not self._verify_signature(model_file, signature):
            raise SecurityError("Invalid model signature")

        # Validate model
        if not self._validate_model(model_file):
            raise SecurityError("Model validation failed")

        # Test model performance
        if not self._performance_test(model_file):
            raise ValueError("Model performance below threshold")

        # Create backup of current model
        self._backup_current_model()

        # Deploy new model
        self._load_model(model_file, version)

        # Monitor for issues
        self._enable_canary_deployment(version)

        # Audit deployment
        audit_log('model_deployed', {
            'version': version,
            'previous_version': self.current_version
        })

        self.current_version = version

    def rollback_model(self, to_version):
        """Rollback to previous model version"""
        if to_version not in self.version_history:
            raise ValueError(f"Version {to_version} not found")

        # Load previous version
        model_file = self._get_version(to_version)
        self._load_model(model_file, to_version)

        # Audit rollback
        audit_log('model_rollback', {
            'from_version': self.current_version,
            'to_version': to_version,
            'reason': 'security_issue'
        })

        self.current_version = to_version

    def _verify_signature(self, model_file, signature):
        """Verify model cryptographic signature"""
        # Use RSA or similar to verify model hasn't been tampered
        public_key = load_public_key()

        model_hash = hashlib.sha256(model_file).hexdigest()

        return verify_signature(public_key, model_hash, signature)

def detect_fraud(credit_report):
    """
    AI-powered fraud detection
    """
    # Prepare features (with privacy)
    features = prepare_secure_features(credit_report)

    # Run fraud model
    prediction = fraud_model.predict(features)

    # Generate explanation
    explanation = explainer.explain_prediction(features, prediction)

    # Audit decision
    audit_ai_decision('fraud_detection', prediction, explanation)

    return {
        'fraud_risk': prediction['score'],
        'confidence': prediction['confidence'],
        'explanation': explanation,
        'recommended_action': get_recommendation(prediction)
    }

def assess_credit_risk(credit_report):
    """
    AI-powered risk assessment
    """
    # Apply differential privacy
    private_features = privacy_layer.privatize(
        extract_features(credit_report)
    )

    # Run risk model
    risk_score = risk_model.predict(private_features)

    # Explain decision (required for FCRA)
    explanation = explainer.explain_risk_score(
        private_features,
        risk_score
    )

    return {
        'risk_category': categorize_risk(risk_score),
        'explanation': explanation,
        'factors': explanation['primary_factors']
    }

def analyze_patterns(credit_report):
    """
    AI-powered pattern detection
    """
    # Anonymize before analysis
    anonymized = anonymize_for_ml(credit_report)

    # Detect unusual patterns
    patterns = pattern_model.analyze(anonymized)

    # Filter sensitive patterns
    filtered_patterns = filter_pii_from_results(patterns)

    return filtered_patterns

class PrivacyBudget:
    """
    ✅ Manage cumulative privacy budget
    """
    def __init__(self, total_epsilon=10.0):
        self.total_epsilon = total_epsilon
        self.used_epsilon = 0.0
        self.queries = []

    def check_budget(self, query_epsilon):
        """Check if query within budget"""
        if self.used_epsilon + query_epsilon > self.total_epsilon:
            raise PrivacyBudgetExceeded(
                f"Budget exceeded: {self.used_epsilon + query_epsilon} > {self.total_epsilon}"
            )

        return True

    def consume_budget(self, query_epsilon, query_desc):
        """Consume privacy budget for query"""
        self.check_budget(query_epsilon)

        self.used_epsilon += query_epsilon
        self.queries.append({
            'epsilon': query_epsilon,
            'description': query_desc,
            'timestamp': datetime.utcnow()
        })

        # Alert if budget running low
        if self.used_epsilon > 0.8 * self.total_epsilon:
            alert('privacy_budget_low', {
                'used': self.used_epsilon,
                'total': self.total_epsilon
            })