from dataclasses import dataclass
from typing import List, Optional
from enum import Enum
 
class AIRiskLevel(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"
 
@dataclass
class AIGovernanceStructure:
    """Enterprise AI governance organizational structure."""
 
    # Board Level
    board_ai_committee: bool = True
    board_reporting_frequency: str = "quarterly"
 
    # Executive Level
    chief_ai_officer: bool = True
    ai_ethics_officer: bool = True
    ai_security_lead: bool = True
 
    # Operational Level
    ai_review_board: dict = None
    ai_security_team: dict = None
    business_unit_ai_leads: List[str] = None
 
    def __post_init__(self):
        if self.ai_review_board is None:
            self.ai_review_board = {
                'members': [
                    'Chief AI Officer',
                    'AI Ethics Officer',
                    'AI Security Lead',
                    'Legal Counsel',
                    'Privacy Officer',
                    'Business Unit Representatives'
                ],
                'meeting_frequency': 'monthly',
                'responsibilities': [
                    'Review high-risk AI deployments',
                    'Approve AI security policies',
                    'Oversee AI incident response',
                    'Monitor AI risk posture'
                ]
            }
 
 
@dataclass
class AIPolicy:
    """Enterprise AI security policy."""
 
    policy_id: str
    title: str
    version: str
    effective_date: str
    owner: str
    scope: str
    requirements: List[dict]
    exceptions_process: str
    review_frequency: str = "annual"
 
    def generate_policy_document(self) -> str:
        """Generate formatted policy document."""
        pass
 
 
class AIGovernanceFramework:
    """Manage enterprise AI governance."""
 
    def __init__(self, config: dict):
        self.structure = AIGovernanceStructure(**config.get('structure', {}))
        self.policies = self._load_policies(config.get('policies', []))
        self.risk_appetite = config.get('risk_appetite', {})
 
    def assess_deployment_governance(self, ai_system: dict) -> dict:
        """Assess if AI deployment meets governance requirements."""
 
        assessment = {
            'system_id': ai_system['id'],
            'assessment_date': datetime.utcnow().isoformat(),
            'checks': []
        }
 
        # Check 1: Policy compliance
        policy_check = self._check_policy_compliance(ai_system)
        assessment['checks'].append(policy_check)
 
        # Check 2: Risk level within appetite
        risk_check = self._check_risk_appetite(ai_system)
        assessment['checks'].append(risk_check)
 
        # Check 3: Required approvals obtained
        approval_check = self._check_approvals(ai_system)
        assessment['checks'].append(approval_check)
 
        # Check 4: Documentation complete
        doc_check = self._check_documentation(ai_system)
        assessment['checks'].append(doc_check)
 
        # Overall assessment
        assessment['compliant'] = all(c['passed'] for c in assessment['checks'])
        assessment['required_actions'] = [
            c['remediation'] for c in assessment['checks']
            if not c['passed'] and c.get('remediation')
        ]
 
        return assessment

class AIEthicsFramework:
    """Framework for ethical AI development and deployment."""
 
    principles = {
        'fairness': {
            'description': 'AI systems should treat all individuals fairly',
            'requirements': [
                'Bias assessment before deployment',
                'Regular fairness audits',
                'Diverse training data',
                'Explainable decision processes'
            ]
        },
        'transparency': {
            'description': 'AI systems should be transparent about their nature and limitations',
            'requirements': [
                'Clear AI disclosure to users',
                'Documented decision logic',
                'Accessible explanations for affected parties',
                'Published AI governance policies'
            ]
        },
        'accountability': {
            'description': 'Clear accountability for AI system outcomes',
            'requirements': [
                'Designated system owners',
                'Defined escalation paths',
                'Incident response procedures',
                'Regular governance reviews'
            ]
        },
        'privacy': {
            'description': 'AI systems should protect individual privacy',
            'requirements': [
                'Privacy impact assessments',
                'Data minimization',
                'Purpose limitation',
                'User consent where required'
            ]
        },
        'safety': {
            'description': 'AI systems should be safe and secure',
            'requirements': [
                'Security testing before deployment',
                'Continuous monitoring',
                'Fail-safe mechanisms',
                'Regular security updates'
            ]
        }
    }
 
    def assess_ethical_compliance(self, ai_system: dict) -> dict:
        """Assess AI system against ethics framework."""
 
        assessment = {
            'system_id': ai_system['id'],
            'principles_assessment': {}
        }
 
        for principle, details in self.principles.items():
            principle_assessment = {
                'compliant': True,
                'requirements_met': [],
                'requirements_not_met': []
            }
 
            for requirement in details['requirements']:
                is_met = self._check_requirement(ai_system, principle, requirement)
                if is_met:
                    principle_assessment['requirements_met'].append(requirement)
                else:
                    principle_assessment['requirements_not_met'].append(requirement)
                    principle_assessment['compliant'] = False
 
            assessment['principles_assessment'][principle] = principle_assessment
 
        assessment['overall_compliant'] = all(
            p['compliant'] for p in assessment['principles_assessment'].values()
        )
 
        return assessment

class AIRiskAssessment:
    """Comprehensive AI risk assessment framework."""
 
    risk_categories = {
        'security': {
            'weight': 0.25,
            'factors': [
                'prompt_injection_vulnerability',
                'data_poisoning_risk',
                'model_theft_exposure',
                'adversarial_robustness',
                'access_control_strength'
            ]
        },
        'privacy': {
            'weight': 0.20,
            'factors': [
                'training_data_pii',
                'inference_data_sensitivity',
                'data_retention_practices',
                'cross_border_transfers',
                'anonymization_effectiveness'
            ]
        },
        'operational': {
            'weight': 0.20,
            'factors': [
                'availability_criticality',
                'scalability_risk',
                'dependency_concentration',
                'disaster_recovery_readiness',
                'monitoring_capability'
            ]
        },
        'reputational': {
            'weight': 0.15,
            'factors': [
                'bias_potential',
                'output_harm_possibility',
                'transparency_level',
                'public_perception_sensitivity',
                'media_exposure_risk'
            ]
        },
        'compliance': {
            'weight': 0.20,
            'factors': [
                'regulatory_applicability',
                'documentation_completeness',
                'audit_readiness',
                'consent_management',
                'rights_request_capability'
            ]
        }
    }
 
    def assess_risk(self, ai_system: dict) -> dict:
        """Perform comprehensive risk assessment."""
 
        assessment = {
            'system_id': ai_system['id'],
            'assessment_date': datetime.utcnow().isoformat(),
            'assessor': ai_system.get('assessor'),
            'category_scores': {},
            'overall_risk_score': 0,
            'risk_level': None,
            'findings': [],
            'recommendations': []
        }
 
        total_weighted_score = 0
 
        for category, details in self.risk_categories.items():
            category_score = self._assess_category(ai_system, category, details['factors'])
            assessment['category_scores'][category] = category_score
            total_weighted_score += category_score['score'] * details['weight']
 
            # Collect findings
            for finding in category_score.get('findings', []):
                assessment['findings'].append({
                    'category': category,
                    **finding
                })
 
        assessment['overall_risk_score'] = round(total_weighted_score, 2)
        assessment['risk_level'] = self._determine_risk_level(assessment['overall_risk_score'])
        assessment['recommendations'] = self._generate_recommendations(assessment)
 
        return assessment
 
    def _determine_risk_level(self, score: float) -> str:
        """Determine risk level from score."""
        if score >= 80:
            return 'critical'
        elif score >= 60:
            return 'high'
        elif score >= 40:
            return 'medium'
        else:
            return 'low'
 
    def _assess_category(self, ai_system: dict, category: str,
                        factors: List[str]) -> dict:
        """Assess a specific risk category."""
 
        factor_scores = []
        findings = []
 
        for factor in factors:
            score, finding = self._assess_factor(ai_system, factor)
            factor_scores.append(score)
            if finding:
                findings.append(finding)
 
        return {
            'score': sum(factor_scores) / len(factor_scores),
            'factor_scores': dict(zip(factors, factor_scores)),
            'findings': findings
        }
 
 
class AIRiskTreatment:
    """AI risk treatment planning and tracking."""
 
    treatment_options = {
        'mitigate': {
            'description': 'Implement controls to reduce risk',
            'examples': [
                'Add input validation',
                'Implement access controls',
                'Deploy monitoring',
                'Add guardrails'
            ]
        },
        'transfer': {
            'description': 'Transfer risk to third party',
            'examples': [
                'Cyber insurance',
                'Vendor liability agreements',
                'Indemnification clauses'
            ]
        },
        'accept': {
            'description': 'Accept residual risk with approval',
            'examples': [
                'Document risk acceptance',
                'Obtain executive sign-off',
                'Implement monitoring'
            ]
        },
        'avoid': {
            'description': 'Eliminate the risk by not proceeding',
            'examples': [
                'Do not deploy the system',
                'Remove risky features',
                'Choose alternative approach'
            ]
        }
    }
 
    def create_treatment_plan(self, risk_assessment: dict) -> dict:
        """Create risk treatment plan based on assessment."""
 
        treatment_plan = {
            'assessment_id': risk_assessment['assessment_id'],
            'created_date': datetime.utcnow().isoformat(),
            'treatments': [],
            'residual_risk': None
        }
 
        for finding in risk_assessment['findings']:
            treatment = self._determine_treatment(finding)
            treatment_plan['treatments'].append(treatment)
 
        # Calculate expected residual risk
        treatment_plan['residual_risk'] = self._calculate_residual_risk(
            risk_assessment['overall_risk_score'],
            treatment_plan['treatments']
        )
 
        return treatment_plan

class AISecurityControlsFramework:
    """Technical security controls for AI systems."""
 
    control_layers = {
        'perimeter': {
            'controls': [
                {
                    'id': 'PER-001',
                    'name': 'API Gateway Protection',
                    'description': 'All AI API traffic routed through secured gateway',
                    'implementation': 'WAF, rate limiting, authentication'
                },
                {
                    'id': 'PER-002',
                    'name': 'Network Segmentation',
                    'description': 'AI infrastructure isolated in dedicated network segment',
                    'implementation': 'VLANs, firewall rules, micro-segmentation'
                },
                {
                    'id': 'PER-003',
                    'name': 'DDoS Protection',
                    'description': 'Protection against volumetric attacks',
                    'implementation': 'Cloud DDoS protection, rate limiting'
                }
            ]
        },
        'application': {
            'controls': [
                {
                    'id': 'APP-001',
                    'name': 'Input Validation',
                    'description': 'All inputs validated and sanitized',
                    'implementation': 'Schema validation, injection detection'
                },
                {
                    'id': 'APP-002',
                    'name': 'Output Filtering',
                    'description': 'AI outputs filtered for harmful content',
                    'implementation': 'Content policy, PII detection, output validators'
                },
                {
                    'id': 'APP-003',
                    'name': 'Guardrails',
                    'description': 'Behavioral guardrails on AI systems',
                    'implementation': 'Prompt engineering, fine-tuning, runtime checks'
                }
            ]
        },
        'data': {
            'controls': [
                {
                    'id': 'DAT-001',
                    'name': 'Encryption at Rest',
                    'description': 'All AI data encrypted at rest',
                    'implementation': 'AES-256, key management'
                },
                {
                    'id': 'DAT-002',
                    'name': 'Encryption in Transit',
                    'description': 'All data encrypted in transit',
                    'implementation': 'TLS 1.3, certificate pinning'
                },
                {
                    'id': 'DAT-003',
                    'name': 'Data Classification',
                    'description': 'AI training and inference data classified',
                    'implementation': 'Data labeling, handling policies'
                }
            ]
        },
        'identity': {
            'controls': [
                {
                    'id': 'IDN-001',
                    'name': 'Authentication',
                    'description': 'Strong authentication for AI access',
                    'implementation': 'OAuth2, API keys, MFA'
                },
                {
                    'id': 'IDN-002',
                    'name': 'Authorization',
                    'description': 'Fine-grained access control',
                    'implementation': 'RBAC, attribute-based access'
                },
                {
                    'id': 'IDN-003',
                    'name': 'Service Identity',
                    'description': 'Strong identity for AI services',
                    'implementation': 'Service accounts, workload identity'
                }
            ]
        },
        'monitoring': {
            'controls': [
                {
                    'id': 'MON-001',
                    'name': 'Security Logging',
                    'description': 'Comprehensive security event logging',
                    'implementation': 'SIEM integration, audit trails'
                },
                {
                    'id': 'MON-002',
                    'name': 'Anomaly Detection',
                    'description': 'Detect anomalous AI behavior',
                    'implementation': 'ML-based detection, threshold alerting'
                },
                {
                    'id': 'MON-003',
                    'name': 'Model Monitoring',
                    'description': 'Monitor model performance and drift',
                    'implementation': 'Performance metrics, drift detection'
                }
            ]
        }
    }
 
    def assess_control_coverage(self, ai_system: dict) -> dict:
        """Assess security control coverage for AI system."""
 
        assessment = {
            'system_id': ai_system['id'],
            'layers': {},
            'overall_coverage': 0,
            'gaps': []
        }
 
        total_controls = 0
        implemented_controls = 0
 
        for layer, details in self.control_layers.items():
            layer_assessment = {
                'controls': [],
                'coverage': 0
            }
 
            for control in details['controls']:
                status = self._check_control_implementation(ai_system, control)
                layer_assessment['controls'].append({
                    **control,
                    'status': status
                })
 
                total_controls += 1
                if status == 'implemented':
                    implemented_controls += 1
                elif status == 'partial':
                    implemented_controls += 0.5
                else:
                    assessment['gaps'].append({
                        'layer': layer,
                        'control': control['id'],
                        'name': control['name'],
                        'priority': self._get_control_priority(control)
                    })
 
            layer_implemented = sum(
                1 if c['status'] == 'implemented' else 0.5 if c['status'] == 'partial' else 0
                for c in layer_assessment['controls']
            )
            layer_assessment['coverage'] = layer_implemented / len(details['controls'])
            assessment['layers'][layer] = layer_assessment
 
        assessment['overall_coverage'] = implemented_controls / total_controls
 
        return assessment

class AISecurityOperations:
    """Operational security for AI systems."""
 
    def __init__(self, config: dict):
        self.monitoring = AISecurityMonitoring(config.get('monitoring', {}))
        self.incident_response = AIIncidentResponse(config.get('ir', {}))
        self.vulnerability_management = AIVulnerabilityManagement(config.get('vuln', {}))
 
    def run_security_operations(self):
        """Continuous security operations."""
 
        # 1. Monitor AI systems
        alerts = self.monitoring.collect_and_analyze()
 
        for alert in alerts:
            # 2. Triage alerts
            triage_result = self._triage_alert(alert)
 
            if triage_result['is_incident']:
                # 3. Escalate to incident response
                self.incident_response.create_incident(alert, triage_result)
 
            elif triage_result['is_vulnerability']:
                # 4. Add to vulnerability queue
                self.vulnerability_management.add_finding(alert)
 
        # 5. Generate operations report
        return self._generate_ops_report()
 
 
class AISecurityMonitoring:
    """Security monitoring for AI systems."""
 
    def __init__(self, config: dict):
        self.detection_rules = self._load_detection_rules(config)
        self.baselines = self._load_baselines(config)
 
    def collect_and_analyze(self) -> List[dict]:
        """Collect telemetry and detect anomalies."""
 
        alerts = []
 
        # Collect telemetry from all AI systems
        telemetry = self._collect_telemetry()
 
        # Run detection rules
        for rule in self.detection_rules:
            matches = rule.evaluate(telemetry)
            for match in matches:
                alerts.append({
                    'rule_id': rule.id,
                    'severity': rule.severity,
                    'match_data': match,
                    'timestamp': datetime.utcnow().isoformat()
                })
 
        # Baseline anomaly detection
        anomalies = self._detect_baseline_anomalies(telemetry)
        alerts.extend(anomalies)
 
        return alerts
 
    detection_rules_examples = [
        {
            'id': 'AI-DET-001',
            'name': 'Prompt Injection Attempt',
            'description': 'Detect prompt injection patterns in inputs',
            'severity': 'high',
            'query': '''
                SELECT * FROM ai_requests
                WHERE input MATCHES injection_patterns
                AND timestamp > now() - interval '5 minutes'
            '''
        },
        {
            'id': 'AI-DET-002',
            'name': 'Model Extraction Attempt',
            'description': 'Detect systematic querying patterns',
            'severity': 'critical',
            'query': '''
                SELECT user_id, COUNT(*) as query_count,
                       STDDEV(input_similarity) as pattern_score
                FROM ai_requests
                WHERE timestamp > now() - interval '1 hour'
                GROUP BY user_id
                HAVING query_count > 1000 AND pattern_score < 0.3
            '''
        },
        {
            'id': 'AI-DET-003',
            'name': 'Guardrail Bypass Attempt',
            'description': 'Detect attempts to bypass safety guardrails',
            'severity': 'critical',
            'query': '''
                SELECT * FROM ai_requests
                WHERE guardrail_triggered = true
                AND same_user_within_5min_count > 5
            '''
        }
    ]

class AIComplianceFramework:
    """Manage AI compliance requirements."""
 
    regulatory_requirements = {
        'eu_ai_act': {
            'name': 'EU AI Act',
            'applicable_to': 'AI systems deployed in EU',
            'key_requirements': [
                'Risk classification',
                'Technical documentation',
                'Human oversight',
                'Transparency',
                'Quality management',
                'Conformity assessment'
            ],
            'timeline': {
                'prohibited_practices': '2025-02',
                'gpai_requirements': '2025-08',
                'high_risk_requirements': '2026-08'
            }
        },
        'gdpr': {
            'name': 'General Data Protection Regulation',
            'applicable_to': 'Processing of EU personal data',
            'key_requirements': [
                'Lawful basis for processing',
                'Data minimization',
                'Purpose limitation',
                'Data subject rights',
                'Data protection impact assessment',
                'Privacy by design'
            ]
        },
        'ccpa': {
            'name': 'California Consumer Privacy Act',
            'applicable_to': 'California residents\' data',
            'key_requirements': [
                'Disclosure requirements',
                'Opt-out rights',
                'Access and deletion rights',
                'Non-discrimination'
            ]
        }
    }
 
    def assess_compliance(self, ai_system: dict,
                         regulations: List[str]) -> dict:
        """Assess AI system compliance with regulations."""
 
        assessment = {
            'system_id': ai_system['id'],
            'assessment_date': datetime.utcnow().isoformat(),
            'regulations': {}
        }
 
        for reg in regulations:
            if reg in self.regulatory_requirements:
                reg_assessment = self._assess_regulation(
                    ai_system,
                    self.regulatory_requirements[reg]
                )
                assessment['regulations'][reg] = reg_assessment
 
        # Overall compliance status
        assessment['overall_compliant'] = all(
            r['compliant'] for r in assessment['regulations'].values()
        )
 
        return assessment
 
 
class AIAuditFramework:
    """Framework for AI system audits."""
 
    audit_areas = {
        'governance': [
            'Policy documentation',
            'Accountability structures',
            'Risk management processes',
            'Change management'
        ],
        'technical': [
            'Security controls implementation',
            'Access control effectiveness',
            'Monitoring and logging',
            'Incident response capability'
        ],
        'data': [
            'Data governance',
            'Data quality',
            'Privacy controls',
            'Data retention'
        ],
        'model': [
            'Model documentation',
            'Model performance',
            'Bias assessment',
            'Explainability'
        ],
        'operations': [
            'Operational procedures',
            'Training records',
            'Incident history',
            'Continuous improvement'
        ]
    }
 
    def conduct_audit(self, ai_system: dict, scope: List[str]) -> dict:
        """Conduct AI system audit."""
 
        audit_report = {
            'audit_id': str(uuid.uuid4()),
            'system_id': ai_system['id'],
            'audit_date': datetime.utcnow().isoformat(),
            'scope': scope,
            'findings': [],
            'recommendations': [],
            'overall_rating': None
        }
 
        for area in scope:
            if area in self.audit_areas:
                area_findings = self._audit_area(ai_system, area)
                audit_report['findings'].extend(area_findings)
 
        # Generate recommendations
        audit_report['recommendations'] = self._generate_recommendations(
            audit_report['findings']
        )
 
        # Calculate overall rating
        audit_report['overall_rating'] = self._calculate_audit_rating(
            audit_report['findings']
        )
 
        return audit_report

## AI Security Framework Implementation Roadmap
 
### Phase 1: Foundation (Months 1-3)
- [ ] Establish AI governance structure
- [ ] Define AI security policies
- [ ] Conduct initial AI inventory
- [ ] Perform baseline risk assessment
- [ ] Identify regulatory requirements
 
### Phase 2: Core Controls (Months 4-6)
- [ ] Implement perimeter security controls
- [ ] Deploy application security controls
- [ ] Establish data protection measures
- [ ] Configure identity and access management
- [ ] Set up security monitoring
 
### Phase 3: Operations (Months 7-9)
- [ ] Establish security operations processes
- [ ] Deploy detection capabilities
- [ ] Create incident response procedures
- [ ] Implement vulnerability management
- [ ] Train security team on AI threats
 
### Phase 4: Compliance (Months 10-12)
- [ ] Complete compliance assessments
- [ ] Address compliance gaps
- [ ] Prepare audit documentation
- [ ] Conduct internal audit
- [ ] Establish continuous compliance monitoring
 
### Phase 5: Maturity (Ongoing)
- [ ] Continuous improvement
- [ ] Regular framework updates
- [ ] Benchmarking against standards
- [ ] Advanced threat detection
- [ ] AI security innovation