# Comprehensive API threat detection system
class APIThreatDetectionEngine:
    def __init__(self):
        self.detectors = {
            'authentication_abuse': AuthenticationAbuseDetector(),
            'authorization_bypass': AuthorizationBypassDetector(),
            'rate_limit_evasion': RateLimitEvasionDetector(),
            'data_exfiltration': DataExfiltrationDetector(),
            'business_logic_abuse': BusinessLogicAbuseDetector(),
            'injection_attacks': InjectionAttackDetector()
        }
        self.ml_analyzer = MLThreatAnalyzer()
        self.correlation_engine = ThreatCorrelationEngine()
    
    def analyze_api_request(self, request_data, context):
        """Comprehensive analysis of API request for threats"""
        threat_analysis = {
            'request_id': request_data['request_id'],
            'timestamp': datetime.utcnow(),
            'threat_scores': {},
            'detected_threats': [],
            'risk_level': 'LOW',
            'recommended_action': 'ALLOW'
        }
        
        # Run individual threat detectors
        for detector_name, detector in self.detectors.items():
            threat_score = detector.analyze(request_data, context)
            threat_analysis['threat_scores'][detector_name] = threat_score
            
            if threat_score['is_threat']:
                threat_analysis['detected_threats'].append({
                    'type': detector_name,
                    'confidence': threat_score['confidence'],
                    'indicators': threat_score['indicators'],
                    'severity': threat_score['severity']
                })
        
        # ML-based threat analysis
        ml_analysis = self.ml_analyzer.analyze_request_pattern(
            request_data,
            context,
            threat_analysis['threat_scores']
        )
        threat_analysis['ml_analysis'] = ml_analysis
        
        # Correlate threats across multiple requests
        correlated_threats = self.correlation_engine.correlate_threats(
            threat_analysis,
            context['user_session'],
            context['request_history']
        )
        threat_analysis['correlated_threats'] = correlated_threats
        
        # Calculate overall risk and recommended action
        overall_risk = self.calculate_overall_risk(
            threat_analysis['threat_scores'],
            ml_analysis,
            correlated_threats
        )
        threat_analysis['risk_level'] = overall_risk['level']
        threat_analysis['recommended_action'] = overall_risk['action']
        
        return threat_analysis

class ZeroTrustAPIGateway:
    def __init__(self):
        self.identity_verifier = IdentityVerificationService()
        self.device_analyzer = DeviceAnalysisService()
        self.behavior_analyzer = BehaviorAnalysisService()
        self.policy_engine = DynamicPolicyEngine()
        self.risk_calculator = RiskCalculationEngine()
    
    def verify_api_access(self, request, user_context):
        """Continuous verification for API access"""
        verification_result = {
            'request_id': request['id'],
            'user_id': user_context['user_id'],
            'verification_timestamp': datetime.utcnow(),
            'identity_verification': {},
            'device_verification': {},
            'behavior_verification': {},
            'policy_evaluation': {},
            'access_decision': 'DENY'
        }
        
        # Identity verification
        identity_result = self.identity_verifier.verify_identity(
            request['authentication_token'],
            user_context
        )
        verification_result['identity_verification'] = identity_result
        
        # Device verification
        device_result = self.device_analyzer.analyze_device(
            request['device_fingerprint'],
            user_context['known_devices']
        )
        verification_result['device_verification'] = device_result
        
        # Behavioral verification
        behavior_result = self.behavior_analyzer.analyze_behavior(
            request['behavioral_data'],
            user_context['behavioral_baseline']
        )
        verification_result['behavior_verification'] = behavior_result
        
        # Dynamic policy evaluation
        policy_result = self.policy_engine.evaluate_policies(
            request,
            user_context,
            {
                'identity': identity_result,
                'device': device_result,
                'behavior': behavior_result
            }
        )
        verification_result['policy_evaluation'] = policy_result
        
        # Calculate risk and make access decision
        risk_score = self.risk_calculator.calculate_risk(
            identity_result,
            device_result,
            behavior_result,
            policy_result
        )
        
        if risk_score < 0.3:
            verification_result['access_decision'] = 'ALLOW'
        elif risk_score < 0.7:
            verification_result['access_decision'] = 'CHALLENGE'
        else:
            verification_result['access_decision'] = 'DENY'
        
        verification_result['risk_score'] = risk_score
        
        return verification_result

class SecureTokenManager:
    def __init__(self):
        self.token_generator = CryptographicTokenGenerator()
        self.token_validator = TokenValidator()
        self.key_manager = KeyManagementService()
        self.audit_logger = TokenAuditLogger()
    
    def generate_secure_jwt(self, user_claims, client_context):
        """Generate cryptographically secure JWT with advanced features"""
        # Generate unique token ID
        jti = str(uuid.uuid4())
        
        # Create comprehensive claims
        jwt_claims = {
            'iss': 'cybersignal-auth-service',
            'sub': user_claims['user_id'],
            'aud': client_context['client_id'],
            'exp': int((datetime.utcnow() + timedelta(hours=1)).timestamp()),
            'iat': int(datetime.utcnow().timestamp()),
            'nbf': int(datetime.utcnow().timestamp()),
            'jti': jti,
            'scope': user_claims['permissions'],
            'device_id': client_context['device_id'],
            'session_id': client_context['session_id'],
            'risk_level': user_claims.get('risk_level', 'LOW'),
            'mfa_verified': user_claims.get('mfa_verified', False)
        }
        
        # Add custom security claims
        jwt_claims.update({
            'ip_binding': client_context['client_ip'],
            'geo_location': client_context['geo_location'],
            'user_agent_hash': hashlib.sha256(
                client_context['user_agent'].encode()
            ).hexdigest()[:16]
        })
        
        # Sign JWT with rotating keys
        signing_key = self.key_manager.get_current_signing_key()
        jwt_token = jwt.encode(
            jwt_claims,
            signing_key['private_key'],
            algorithm='RS256',
            headers={'kid': signing_key['key_id']}
        )
        
        # Store token metadata for validation
        token_metadata = {
            'jti': jti,
            'user_id': user_claims['user_id'],
            'client_id': client_context['client_id'],
            'issued_at': datetime.utcnow(),
            'expires_at': datetime.utcnow() + timedelta(hours=1),
            'revoked': False,
            'usage_count': 0
        }
        
        self.store_token_metadata(token_metadata)
        
        # Audit token generation
        self.audit_logger.log_token_generation(
            user_claims['user_id'],
            client_context['client_id'],
            jti
        )
        
        return {
            'access_token': jwt_token,
            'token_type': 'Bearer',
            'expires_in': 3600,
            'jti': jti,
            'security_features': [
                'ip_binding',
                'device_binding',
                'geo_location_binding',
                'usage_tracking'
            ]
        }
    
    def validate_jwt_security(self, jwt_token, request_context):
        """Advanced JWT validation with security checks"""
        validation_result = {
            'valid': False,
            'claims': None,
            'security_checks': {},
            'violations': [],
            'risk_score': 0
        }
        
        try:
            # Decode and verify JWT
            decoded_token = jwt.decode(
                jwt_token,
                self.key_manager.get_public_key(),
                algorithms=['RS256'],
                options={'verify_exp': True, 'verify_nbf': True}
            )
            validation_result['claims'] = decoded_token
            
            # Security checks
            security_checks = {
                'ip_binding': self.verify_ip_binding(decoded_token, request_context),
                'device_binding': self.verify_device_binding(decoded_token, request_context),
                'geo_location': self.verify_geo_location(decoded_token, request_context),
                'token_reuse': self.check_token_reuse(decoded_token['jti']),
                'revocation_status': self.check_revocation_status(decoded_token['jti'])
            }
            
            validation_result['security_checks'] = security_checks
            
            # Calculate violations and risk score
            violations = [check for check, result in security_checks.items() if not result['valid']]
            validation_result['violations'] = violations
            validation_result['risk_score'] = len(violations) * 0.2
            
            # Token is valid if no critical violations
            critical_violations = ['revocation_status', 'token_reuse']
            has_critical_violations = any(v in violations for v in critical_violations)
            
            validation_result['valid'] = not has_critical_violations and validation_result['risk_score'] < 0.5
            
        except jwt.ExpiredSignatureError:
            validation_result['violations'].append('expired_token')
        except jwt.InvalidTokenError as e:
            validation_result['violations'].append(f'invalid_token: {str(e)}')
        
        return validation_result

class IntelligentRateLimiter:
    def __init__(self):
        self.rate_calculator = AdaptiveRateCalculator()
        self.behavior_analyzer = UserBehaviorAnalyzer()
        self.threat_detector = ThreatDetectionEngine()
        self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
    
    def calculate_dynamic_rate_limit(self, user_id, endpoint, request_context):
        """Calculate dynamic rate limit based on user behavior and threat level"""
        base_rate_limit = self.get_base_rate_limit(endpoint)
        
        # Analyze user behavior patterns
        user_behavior = self.behavior_analyzer.analyze_user_behavior(
            user_id,
            request_context['request_history']
        )
        
        # Detect potential threats
        threat_level = self.threat_detector.assess_threat_level(
            user_id,
            request_context
        )
        
        # Calculate adaptive rate limit
        adaptive_rate = self.rate_calculator.calculate_rate(
            base_rate_limit,
            user_behavior,
            threat_level,
            request_context
        )
        
        return {
            'user_id': user_id,
            'endpoint': endpoint,
            'base_rate_limit': base_rate_limit,
            'adaptive_rate_limit': adaptive_rate,
            'user_behavior_score': user_behavior['trust_score'],
            'threat_level': threat_level,
            'rate_limit_factors': {
                'user_reputation': user_behavior['reputation_multiplier'],
                'threat_adjustment': threat_level['rate_adjustment'],
                'endpoint_sensitivity': self.get_endpoint_sensitivity(endpoint),
                'time_of_day': self.get_time_based_adjustment(),
                'geographic_location': self.get_geo_based_adjustment(request_context['geo_location'])
            }
        }
    
    def enforce_rate_limit(self, user_id, endpoint, request_context):
        """Enforce intelligent rate limiting with multiple algorithms"""
        # Get dynamic rate limit
        rate_limit_config = self.calculate_dynamic_rate_limit(
            user_id,
            endpoint,
            request_context
        )
        
        # Apply multiple rate limiting algorithms
        rate_limit_results = {
            'token_bucket': self.apply_token_bucket_limit(user_id, endpoint, rate_limit_config),
            'sliding_window': self.apply_sliding_window_limit(user_id, endpoint, rate_limit_config),
            'fixed_window': self.apply_fixed_window_limit(user_id, endpoint, rate_limit_config),
            'distributed_limit': self.apply_distributed_limit(user_id, endpoint, rate_limit_config)
        }
        
        # Determine overall rate limit decision
        rate_limit_decision = self.make_rate_limit_decision(rate_limit_results)
        
        # Log rate limiting decision
        self.log_rate_limit_decision(
            user_id,
            endpoint,
            rate_limit_config,
            rate_limit_results,
            rate_limit_decision
        )
        
        return rate_limit_decision
    
    def apply_token_bucket_limit(self, user_id, endpoint, rate_config):
        """Apply token bucket rate limiting algorithm"""
        bucket_key = f"token_bucket:{user_id}:{endpoint}"
        bucket_capacity = rate_config['adaptive_rate_limit']['requests_per_minute']
        refill_rate = bucket_capacity / 60  # tokens per second
        
        # Get current bucket state
        bucket_data = self.redis_client.hmget(
            bucket_key,
            ['tokens', 'last_refill']
        )
        
        current_time = time.time()
        
        if bucket_data[0] is None:
            # Initialize bucket
            tokens = bucket_capacity
            last_refill = current_time
        else:
            tokens = float(bucket_data[0])
            last_refill = float(bucket_data[1])
            
            # Refill tokens based on elapsed time
            elapsed_time = current_time - last_refill
            tokens_to_add = elapsed_time * refill_rate
            tokens = min(bucket_capacity, tokens + tokens_to_add)
        
        # Check if request can be allowed
        if tokens >= 1:
            tokens -= 1
            allowed = True
        else:
            allowed = False
        
        # Update bucket state
        self.redis_client.hmset(bucket_key, {
            'tokens': tokens,
            'last_refill': current_time
        })
        self.redis_client.expire(bucket_key, 3600)  # Expire after 1 hour
        
        return {
            'algorithm': 'token_bucket',
            'allowed': allowed,
            'remaining_tokens': tokens,
            'bucket_capacity': bucket_capacity,
            'refill_rate': refill_rate
        }

class BusinessLogicProtectionEngine:
    def __init__(self):
        self.rule_engine = BusinessRuleEngine()
        self.pattern_detector = AbusePatternDetector()
        self.anomaly_detector = BusinessLogicAnomalyDetector()
        self.workflow_analyzer = WorkflowAnalyzer()
    
    def protect_business_workflow(self, workflow_request, user_context):
        """Protect business workflows from abuse and manipulation"""
        protection_analysis = {
            'workflow_id': workflow_request['workflow_id'],
            'user_id': user_context['user_id'],
            'protection_timestamp': datetime.utcnow(),
            'rule_violations': [],
            'abuse_patterns': [],
            'anomalies': [],
            'workflow_integrity': True,
            'protection_decision': 'ALLOW'
        }
        
        # Validate business rules
        rule_violations = self.rule_engine.validate_business_rules(
            workflow_request,
            user_context
        )
        protection_analysis['rule_violations'] = rule_violations
        
        # Detect abuse patterns
        abuse_patterns = self.pattern_detector.detect_abuse_patterns(
            workflow_request,
            user_context['request_history']
        )
        protection_analysis['abuse_patterns'] = abuse_patterns
        
        # Detect workflow anomalies
        anomalies = self.anomaly_detector.detect_anomalies(
            workflow_request,
            user_context['normal_behavior']
        )
        protection_analysis['anomalies'] = anomalies
        
        # Analyze workflow integrity
        workflow_integrity = self.workflow_analyzer.analyze_workflow_integrity(
            workflow_request,
            user_context
        )
        protection_analysis['workflow_integrity'] = workflow_integrity['is_valid']
        
        # Make protection decision
        if rule_violations or abuse_patterns or anomalies or not workflow_integrity['is_valid']:
            protection_analysis['protection_decision'] = 'BLOCK'
            
            # Generate detailed blocking reason
            blocking_reasons = []
            if rule_violations:
                blocking_reasons.extend([f"Rule violation: {v['rule']}" for v in rule_violations])
            if abuse_patterns:
                blocking_reasons.extend([f"Abuse pattern: {p['pattern']}" for p in abuse_patterns])
            if anomalies:
                blocking_reasons.extend([f"Anomaly: {a['type']}" for a in anomalies])
            if not workflow_integrity['is_valid']:
                blocking_reasons.append(f"Workflow integrity: {workflow_integrity['reason']}")
            
            protection_analysis['blocking_reasons'] = blocking_reasons
        
        return protection_analysis
    
    def detect_price_manipulation(self, pricing_request, user_context):
        """Detect price manipulation attempts"""
        manipulation_indicators = {
            'rapid_price_checks': self.detect_rapid_price_checks(
                pricing_request,
                user_context['request_history']
            ),
            'cart_manipulation': self.detect_cart_manipulation(
                pricing_request,
                user_context['cart_history']
            ),
            'discount_abuse': self.detect_discount_abuse(
                pricing_request,
                user_context['discount_history']
            ),
            'inventory_probing': self.detect_inventory_probing(
                pricing_request,
                user_context['inventory_requests']
            )
        }
        
        # Calculate manipulation risk score
        risk_score = sum(
            indicator['risk_score'] for indicator in manipulation_indicators.values()
        ) / len(manipulation_indicators)
        
        return {
            'manipulation_indicators': manipulation_indicators,
            'risk_score': risk_score,
            'is_manipulation': risk_score > 0.7,
            'recommended_action': 'BLOCK' if risk_score > 0.7 else 'MONITOR'
        }

class ServiceMeshSecurityController:
    def __init__(self):
        self.mtls_manager = MutualTLSManager()
        self.policy_engine = ServiceMeshPolicyEngine()
        self.traffic_analyzer = ServiceTrafficAnalyzer()
        self.identity_manager = ServiceIdentityManager()
    
    def secure_service_communication(self, source_service, target_service, request_data):
        """Secure service-to-service communication"""
        security_context = {
            'source_service': source_service,
            'target_service': target_service,
            'request_id': request_data['request_id'],
            'security_timestamp': datetime.utcnow(),
            'mtls_verification': {},
            'policy_evaluation': {},
            'traffic_analysis': {},
            'communication_allowed': False
        }
        
        # Verify mutual TLS
        mtls_result = self.mtls_manager.verify_mtls_connection(
            source_service,
            target_service,
            request_data['tls_context']
        )
        security_context['mtls_verification'] = mtls_result
        
        # Evaluate service mesh policies
        policy_result = self.policy_engine.evaluate_service_policies(
            source_service,
            target_service,
            request_data
        )
        security_context['policy_evaluation'] = policy_result
        
        # Analyze traffic patterns
        traffic_analysis = self.traffic_analyzer.analyze_service_traffic(
            source_service,
            target_service,
            request_data
        )
        security_context['traffic_analysis'] = traffic_analysis
        
        # Make communication decision
        if (mtls_result['verified'] and 
            policy_result['allowed'] and 
            traffic_analysis['legitimate']):
            security_context['communication_allowed'] = True
        
        return security_context
    
    def implement_zero_trust_networking(self, service_registry):
        """Implement zero trust networking for microservices"""
        zero_trust_config = {
            'default_policy': 'DENY_ALL',
            'service_policies': {},
            'network_segmentation': {},
            'monitoring_rules': {}
        }
        
        for service in service_registry:
            # Define service-specific policies
            service_policy = {
                'allowed_inbound': self.calculate_allowed_inbound_services(service),
                'allowed_outbound': self.calculate_allowed_outbound_services(service),
                'required_authentication': True,
                'encryption_required': True,
                'audit_logging': True
            }
            
            zero_trust_config['service_policies'][service['name']] = service_policy
            
            # Define network segmentation
            network_segment = {
                'segment_id': f"segment_{service['tier']}",
                'allowed_protocols': service['allowed_protocols'],
                'firewall_rules': self.generate_firewall_rules(service),
                'monitoring_enabled': True
            }
            
            zero_trust_config['network_segmentation'][service['name']] = network_segment
        
        return zero_trust_config

class ContainerSecurityManager:
    def __init__(self):
        self.image_scanner = ContainerImageScanner()
        self.runtime_monitor = ContainerRuntimeMonitor()
        self.policy_enforcer = ContainerPolicyEnforcer()
        self.compliance_checker = ContainerComplianceChecker()
    
    def secure_container_deployment(self, container_spec, deployment_context):
        """Comprehensive container security validation"""
        security_assessment = {
            'container_name': container_spec['name'],
            'image': container_spec['image'],
            'deployment_timestamp': datetime.utcnow(),
            'image_security': {},
            'configuration_security': {},
            'runtime_security': {},
            'compliance_status': {},
            'deployment_allowed': False
        }
        
        # Scan container image for vulnerabilities
        image_scan_result = self.image_scanner.scan_image(
            container_spec['image']
        )
        security_assessment['image_security'] = image_scan_result
        
        # Validate container configuration
        config_validation = self.validate_container_configuration(
            container_spec,
            deployment_context
        )
        security_assessment['configuration_security'] = config_validation
        
        # Check compliance requirements
        compliance_result = self.compliance_checker.check_compliance(
            container_spec,
            deployment_context['compliance_requirements']
        )
        security_assessment['compliance_status'] = compliance_result
        
        # Make deployment decision
        if (image_scan_result['security_score'] > 0.7 and
            config_validation['secure'] and
            compliance_result['compliant']):
            security_assessment['deployment_allowed'] = True
        
        return security_assessment
    
    def monitor_container_runtime(self, container_id):
        """Monitor container runtime for security threats"""
        runtime_monitoring = {
            'container_id': container_id,
            'monitoring_start': datetime.utcnow(),
            'security_events': [],
            'anomalies': [],
            'threat_level': 'LOW'
        }
        
        # Monitor system calls
        syscall_monitoring = self.runtime_monitor.monitor_syscalls(container_id)
        if syscall_monitoring['suspicious_calls']:
            runtime_monitoring['security_events'].append({
                'type': 'suspicious_syscalls',
                'details': syscall_monitoring['suspicious_calls']
            })
        
        # Monitor network connections
        network_monitoring = self.runtime_monitor.monitor_network(container_id)
        if network_monitoring['unauthorized_connections']:
            runtime_monitoring['security_events'].append({
                'type': 'unauthorized_network',
                'details': network_monitoring['unauthorized_connections']
            })
        
        # Monitor file system changes
        filesystem_monitoring = self.runtime_monitor.monitor_filesystem(container_id)
        if filesystem_monitoring['unauthorized_changes']:
            runtime_monitoring['security_events'].append({
                'type': 'filesystem_tampering',
                'details': filesystem_monitoring['unauthorized_changes']
            })
        
        # Calculate threat level
        if runtime_monitoring['security_events']:
            runtime_monitoring['threat_level'] = 'HIGH'
        
        return runtime_monitoring

class APIThreatIntelligenceEngine:
    def __init__(self):
        self.threat_feeds = ThreatIntelligenceFeeds()
        self.pattern_matcher = ThreatPatternMatcher()
        self.ml_classifier = MLThreatClassifier()
        self.correlation_engine = ThreatCorrelationEngine()
    
    def analyze_api_threats(self, api_traffic_data, threat_context):
        """Analyze API traffic for threat indicators"""
        threat_analysis = {
            'analysis_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'traffic_volume': len(api_traffic_data),
            'threat_indicators': [],
            'attack_patterns': [],
            'threat_actors': [],
            'risk_assessment': {}
        }
        
        # Analyze against threat intelligence feeds
        for request in api_traffic_data:
            # Check IP reputation
            ip_reputation = self.threat_feeds.check_ip_reputation(
                request['source_ip']
            )
            if ip_reputation['malicious']:
                threat_analysis['threat_indicators'].append({
                    'type': 'malicious_ip',
                    'value': request['source_ip'],
                    'reputation_score': ip_reputation['score'],
                    'threat_categories': ip_reputation['categories']
                })
            
            # Check for known attack patterns
            attack_patterns = self.pattern_matcher.match_attack_patterns(
                request['request_data']
            )
            if attack_patterns:
                threat_analysis['attack_patterns'].extend(attack_patterns)
            
            # ML-based threat classification
            ml_classification = self.ml_classifier.classify_request(
                request['request_data']
            )
            if ml_classification['is_threat']:
                threat_analysis['threat_indicators'].append({
                    'type': 'ml_detected_threat',
                    'confidence': ml_classification['confidence'],
                    'threat_type': ml_classification['threat_type'],
                    'features': ml_classification['key_features']
                })
        
        # Correlate threats across requests
        correlated_threats = self.correlation_engine.correlate_threats(
            threat_analysis['threat_indicators'],
            threat_analysis['attack_patterns']
        )
        
        # Identify potential threat actors
        threat_actors = self.identify_threat_actors(
            correlated_threats,
            threat_context
        )
        threat_analysis['threat_actors'] = threat_actors
        
        # Calculate overall risk assessment
        risk_assessment = self.calculate_risk_assessment(
            threat_analysis['threat_indicators'],
            threat_analysis['attack_patterns'],
            threat_actors
        )
        threat_analysis['risk_assessment'] = risk_assessment
        
        return threat_analysis

class APIIncidentResponseSystem:
    def __init__(self):
        self.incident_classifier = IncidentClassifier()
        self.response_orchestrator = ResponseOrchestrator()
        self.containment_engine = ContainmentEngine()
        self.recovery_manager = RecoveryManager()
    
    def respond_to_api_incident(self, incident_data, context):
        """Automated response to API security incidents"""
        response_plan = {
            'incident_id': incident_data['incident_id'],
            'incident_type': incident_data['type'],
            'severity': incident_data['severity'],
            'response_timestamp': datetime.utcnow(),
            'containment_actions': [],
            'mitigation_actions': [],
            'recovery_actions': [],
            'response_status': 'IN_PROGRESS'
        }
        
        # Classify incident severity and type
        classification = self.incident_classifier.classify_incident(
            incident_data,
            context
        )
        response_plan.update(classification)
        
        # Execute containment actions
        if classification['severity'] in ['HIGH', 'CRITICAL']:
            containment_actions = self.containment_engine.execute_containment(
                incident_data,
                classification
            )
            response_plan['containment_actions'] = containment_actions
        
        # Execute mitigation actions
        mitigation_actions = self.response_orchestrator.execute_mitigation(
            incident_data,
            classification,
            context
        )
        response_plan['mitigation_actions'] = mitigation_actions
        
        # Plan recovery actions
        recovery_actions = self.recovery_manager.plan_recovery(
            incident_data,
            classification,
            response_plan['containment_actions']
        )
        response_plan['recovery_actions'] = recovery_actions
        
        response_plan['response_status'] = 'COMPLETED'
        
        return response_plan
    
    def implement_circuit_breaker(self, service_name, failure_threshold=5, timeout=60):
        """Implement circuit breaker pattern for service protection"""
        circuit_breaker_state = {
            'service_name': service_name,
            'state': 'CLOSED',  # CLOSED, OPEN, HALF_OPEN
            'failure_count': 0,
            'failure_threshold': failure_threshold,
            'timeout': timeout,
            'last_failure_time': None,
            'success_count': 0
        }
        
        def circuit_breaker_decorator(func):
            def wrapper(*args, **kwargs):
                current_time = time.time()
                
                # Check circuit breaker state
                if circuit_breaker_state['state'] == 'OPEN':
                    if (current_time - circuit_breaker_state['last_failure_time']) > timeout:
                        circuit_breaker_state['state'] = 'HALF_OPEN'
                        circuit_breaker_state['success_count'] = 0
                    else:
                        raise CircuitBreakerOpenException(f"Circuit breaker is OPEN for {service_name}")
                
                try:
                    result = func(*args, **kwargs)
                    
                    # Success - reset failure count
                    if circuit_breaker_state['state'] == 'HALF_OPEN':
                        circuit_breaker_state['success_count'] += 1
                        if circuit_breaker_state['success_count'] >= 3:
                            circuit_breaker_state['state'] = 'CLOSED'
                            circuit_breaker_state['failure_count'] = 0
                    elif circuit_breaker_state['state'] == 'CLOSED':
                        circuit_breaker_state['failure_count'] = 0
                    
                    return result
                    
                except Exception as e:
                    # Failure - increment failure count
                    circuit_breaker_state['failure_count'] += 1
                    circuit_breaker_state['last_failure_time'] = current_time
                    
                    if circuit_breaker_state['failure_count'] >= failure_threshold:
                        circuit_breaker_state['state'] = 'OPEN'
                    
                    raise e
            
            return wrapper
        
        return circuit_breaker_decorator