🏢 Enterprise AI Security

🎯 Learning Objectives

• Design enterprise AI security architecture
• Implement comprehensive security controls
• Establish AI risk management frameworks
• Create operational security procedures
• Manage third-party AI security risks

📚 Core Concepts

🔧 Implementation Strategies

class EnterpriseAISecurityPlatform:
    def __init__(self, config):
        self.config = config
        self.components = {
            'model_registry': ModelRegistry(),
            'security_monitoring': SecurityMonitoring(),
            'access_control': AccessControl(),
            'risk_assessment': RiskAssessment(),
            'compliance_engine': ComplianceEngine()
        }
        
    def deploy_secure_model(self, model, metadata):
        """Deploy model with enterprise security controls"""
        # Security validation
        security_check = self.validate_model_security(model)
        if not security_check['passed']:
            raise SecurityValidationError(security_check['issues'])
        
        # Risk assessment
        risk_score = self.assess_model_risk(model, metadata)
        
        # Deploy with monitoring
        deployment = self.deploy_with_monitoring(model, risk_score)
        
        return deployment
    
    def validate_model_security(self, model):
        """Validate model against security requirements"""
        checks = {
            'adversarial_robustness': self.check_adversarial_robustness(model),
            'data_privacy': self.check_data_privacy(model),
            'bias_fairness': self.check_bias_fairness(model),
            'transparency': self.check_transparency(model)
        }
        
        return {
            'passed': all(check['passed'] for check in checks.values()),
            'checks': checks
        }

class AIRiskManagement:
    def __init__(self):
        self.risk_categories = {
            'technical': ['model_vulnerabilities', 'data_breaches', 'system_failures'],
            'operational': ['human_error', 'process_failures', 'third_party_risks'],
            'business': ['reputation_damage', 'regulatory_fines', 'competitive_disadvantage'],
            'ethical': ['bias_discrimination', 'privacy_violations', 'unfair_outcomes']
        }
        
    def assess_ai_risk(self, ai_system, context):
        """Comprehensive AI risk assessment"""
        risk_profile = {}
        
        for category, risks in self.risk_categories.items():
            category_risks = []
            for risk_type in risks:
                assessment = self.assess_individual_risk(ai_system, risk_type, context)
                category_risks.append(assessment)
            
            risk_profile[category] = {
                'risks': category_risks,
                'overall_score': self.calculate_category_score(category_risks)
            }
        
        return {
            'risk_profile': risk_profile,
            'overall_risk_score': self.calculate_overall_risk(risk_profile),
            'recommendations': self.generate_risk_recommendations(risk_profile)
        }
    
    def generate_risk_recommendations(self, risk_profile):
        """Generate risk mitigation recommendations"""
        recommendations = []
        
        for category, data in risk_profile.items():
            if data['overall_score'] > 0.7:  # High risk threshold
                recommendations.extend(self.get_mitigation_strategies(category))
        
        return recommendations

📊 Enterprise Security Metrics