# Security Framework

Comprehensive security considerations, implementation patterns, and compliance requirements for enterprise deployment of the Kaizen Framework.

## Security Architecture Overview

**Kaizen implements enterprise-grade security** through multiple layers:

1. **Authentication & Authorization**: Enterprise SSO and fine-grained access control
2. **Data Protection**: End-to-end encryption and secure data handling
3. **Audit & Compliance**: Comprehensive logging and regulatory compliance
4. **Runtime Security**: Secure execution environment and threat protection

**Current Implementation Status**:
- ✅ **Basic Security**: Input validation, secure defaults, error handling
- 🟡 **Enterprise Security**: Authentication, authorization, encryption (planned)
- 🟡 **Compliance**: Audit trails, regulatory compliance (planned)

## Authentication & Authorization

### Enterprise SSO Integration (Planned)

**Status**: 🟡 Architecture designed, implementation pending

```python
# Future enterprise authentication
kaizen = Kaizen(config={
    'auth_provider': 'enterprise_sso',
    'sso_config': {
        'provider': 'active_directory',  # or 'okta', 'auth0', etc.
        'domain': 'company.com',
        'client_id': 'kaizen-app-id',
        'scopes': ['ai_operations', 'data_access']
    }
})

# User context integration
user_context = {
    'user_id': 'analyst@company.com',
    'roles': ['ai_analyst', 'data_scientist'],
    'permissions': ['model_access', 'sensitive_data'],
    'session_id': 'session_123'
}

# Authenticated agent creation
agent = kaizen.create_agent(
    name="secure_analyst",
    config=agent_config,
    user_context=user_context
)
```

### Fine-Grained Access Control (Planned)

**Role-Based Access Control (RBAC)**:

```python
# Future RBAC configuration
security_config = {
    'rbac_enabled': True,
    'roles': {
        'ai_analyst': {
            'permissions': [
                'create_agents',
                'execute_workflows',
                'access_public_data'
            ],
            'model_access': ['gpt-3.5-turbo', 'gpt-4'],
            'resource_limits': {
                'max_tokens_per_day': 100000,
                'max_concurrent_agents': 5
            }
        },
        'senior_analyst': {
            'permissions': [
                'create_agents',
                'execute_workflows',
                'access_public_data',
                'access_confidential_data',
                'manage_team_agents'
            ],
            'model_access': ['gpt-3.5-turbo', 'gpt-4', 'claude-3'],
            'resource_limits': {
                'max_tokens_per_day': 500000,
                'max_concurrent_agents': 20
            }
        },
        'admin': {
            'permissions': ['*'],
            'model_access': ['*'],
            'resource_limits': {}
        }
    }
}

kaizen = Kaizen(config={'security': security_config})
```

**Attribute-Based Access Control (ABAC)**:

```python
# Future ABAC policies
abac_policies = [
    {
        'name': 'financial_data_access',
        'condition': {
            'user.department': 'finance',
            'data.classification': 'financial',
            'time.business_hours': True
        },
        'actions': ['read', 'analyze'],
        'effect': 'allow'
    },
    {
        'name': 'pii_access_restriction',
        'condition': {
            'data.contains_pii': True,
            'user.clearance_level': {'$lt': 'confidential'}
        },
        'actions': ['*'],
        'effect': 'deny'
    }
]

# Apply policies to agent execution
agent = kaizen.create_agent("financial_analyzer", {
    "model": "gpt-4",
    "security_policies": abac_policies
})
```

### API Key Management (Current)

**Current Implementation**:

```python
# Basic API key configuration
import os

kaizen = Kaizen(config={
    'api_keys': {
        'openai': os.getenv('OPENAI_API_KEY'),
        'anthropic': os.getenv('ANTHROPIC_API_KEY')
    },
    'key_rotation_enabled': False,  # Future feature
    'key_validation': True
})

# Environment-based configuration
# .env file:
# OPENAI_API_KEY=sk-...
# ANTHROPIC_API_KEY=sk-ant-...
# KAIZEN_ENCRYPTION_KEY=...
```

**Future Enterprise Key Management**:

```python
# Future enterprise key management
kaizen = Kaizen(config={
    'key_management': {
        'provider': 'aws_kms',  # or 'azure_keyvault', 'hashicorp_vault'
        'key_rotation': 'automatic',
        'rotation_interval': '30d',
        'encryption_at_rest': True
    }
})
```

## Data Protection

### Encryption (Planned)

**End-to-End Encryption Architecture**:

```python
# Future encryption configuration
encryption_config = {
    'encryption_enabled': True,
    'encryption_algorithm': 'AES-256-GCM',
    'key_derivation': 'PBKDF2',
    'encrypt_at_rest': True,
    'encrypt_in_transit': True,
    'encrypt_in_memory': True,  # For sensitive operations
    'key_management': 'enterprise_kms'
}

kaizen = Kaizen(config={'encryption': encryption_config})

# Encrypted agent with sensitive data handling
secure_agent = kaizen.create_agent("secure_processor", {
    "model": "gpt-4",
    "encryption": {
        "encrypt_inputs": True,
        "encrypt_outputs": True,
        "encrypt_memory": True,
        "key_scope": "user_session"
    }
})
```

**Data Classification and Handling**:

```python
# Future data classification
data_classification = {
    'public': {
        'encryption_required': False,
        'audit_level': 'basic',
        'retention_period': '1y'
    },
    'internal': {
        'encryption_required': True,
        'audit_level': 'standard',
        'retention_period': '3y'
    },
    'confidential': {
        'encryption_required': True,
        'audit_level': 'comprehensive',
        'retention_period': '7y',
        'access_controls': 'strict'
    },
    'restricted': {
        'encryption_required': True,
        'audit_level': 'comprehensive',
        'retention_period': '10y',
        'access_controls': 'strict',
        'approval_required': True
    }
}

# Agent with data classification awareness
classified_agent = kaizen.create_agent("data_processor", {
    "model": "gpt-4",
    "data_classification": "confidential",
    "handling_rules": data_classification['confidential']
})
```

### Input Sanitization (Current)

**Current Security Measures**:

```python
# Current input validation and sanitization
from kaizen.security.validation import InputValidator

class SecurityAwareAgent:
    def __init__(self, config):
        self.validator = InputValidator()
        self.config = config

    def process_input(self, user_input):
        """Process input with security validation."""
        # Current security measures
        validated_input = self.validator.sanitize_input(user_input)

        # Check for potential security threats
        if self.validator.contains_injection_attempt(validated_input):
            raise SecurityError("Potential injection attack detected")

        # Check for sensitive data exposure
        if self.validator.contains_sensitive_patterns(validated_input):
            self.logger.warning("Sensitive data detected in input")
            validated_input = self.validator.mask_sensitive_data(validated_input)

        return validated_input

# Usage with current security validation
agent = kaizen.create_agent("secure_processor", {
    "model": "gpt-4",
    "input_validation": True,
    "sanitization_enabled": True
})
```

### Data Loss Prevention (Planned)

**DLP Integration**:

```python
# Future DLP configuration
dlp_config = {
    'dlp_enabled': True,
    'scan_inputs': True,
    'scan_outputs': True,
    'policies': [
        {
            'name': 'credit_card_detection',
            'pattern': r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b',
            'action': 'block',
            'alert': True
        },
        {
            'name': 'ssn_detection',
            'pattern': r'\b\d{3}-\d{2}-\d{4}\b',
            'action': 'mask',
            'alert': True
        },
        {
            'name': 'email_detection',
            'pattern': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
            'action': 'log',
            'alert': False
        }
    ]
}

agent = kaizen.create_agent("dlp_aware_processor", {
    "model": "gpt-4",
    "dlp_config": dlp_config
})
```

## Audit & Compliance

### Comprehensive Audit Trails (Planned)

**Status**: 🟡 Architecture designed, implementation pending

```python
# Future audit trail configuration
audit_config = {
    'audit_enabled': True,
    'audit_level': 'comprehensive',  # basic, standard, comprehensive
    'audit_targets': [
        'agent_creation',
        'workflow_execution',
        'data_access',
        'configuration_changes',
        'security_events'
    ],
    'storage': {
        'provider': 'enterprise_siem',
        'retention_period': '7y',
        'encryption_enabled': True,
        'immutable_storage': True
    }
}

kaizen = Kaizen(config={'audit': audit_config})

# Audit-aware agent execution
with kaizen.audit_context(user_id="analyst@company.com") as audit:
    agent = kaizen.create_agent("audited_processor", {
        "model": "gpt-4",
        "audit_metadata": {
            "business_purpose": "financial_analysis",
            "data_classification": "confidential",
            "approval_id": "AUDIT-2024-001"
        }
    })

    results = agent.execute(query)

    # Audit record automatically created
    audit_record = audit.get_record()
    assert audit_record.contains_full_trace()
```

**Audit Record Structure**:

```python
# Future audit record format
audit_record = {
    'timestamp': '2024-01-15T10:30:00Z',
    'event_id': 'kaizen-audit-123456',
    'event_type': 'workflow_execution',
    'user_context': {
        'user_id': 'analyst@company.com',
        'session_id': 'session_789',
        'ip_address': '192.168.1.100',
        'user_agent': 'KaizenClient/1.0'
    },
    'agent_context': {
        'agent_name': 'financial_analyzer',
        'model_used': 'gpt-4',
        'configuration_hash': 'sha256:abc123...'
    },
    'execution_context': {
        'workflow_id': 'wf_456',
        'run_id': 'run_789',
        'duration_ms': 2341,
        'tokens_used': 1500,
        'cost_usd': 0.045
    },
    'data_context': {
        'input_classification': 'confidential',
        'output_classification': 'confidential',
        'data_sources': ['financial_db', 'market_api'],
        'pii_detected': False
    },
    'security_context': {
        'access_granted': True,
        'permissions_used': ['data_access', 'model_execution'],
        'security_alerts': [],
        'compliance_validated': True
    },
    'integrity': {
        'hash': 'sha256:def456...',
        'signature': 'digital_signature...',
        'tamper_evident': True
    }
}
```

### Regulatory Compliance (Planned)

**GDPR Compliance**:

```python
# Future GDPR compliance configuration
gdpr_config = {
    'gdpr_enabled': True,
    'data_minimization': True,
    'purpose_limitation': True,
    'consent_management': True,
    'right_to_erasure': True,
    'data_portability': True,
    'breach_notification': {
        'enabled': True,
        'notification_within': '72h',
        'authority_contact': 'dpo@company.com'
    }
}

# GDPR-compliant agent
gdpr_agent = kaizen.create_agent("gdpr_processor", {
    "model": "gpt-4",
    "gdpr_config": gdpr_config,
    "data_processing_basis": "legitimate_interest",
    "processing_purpose": "customer_service_improvement"
})

# Data subject rights implementation
class GDPRManager:
    def request_data_export(self, user_id: str):
        """Export all data for GDPR Article 20 (Data Portability)."""
        pass

    def request_data_deletion(self, user_id: str):
        """Delete all data for GDPR Article 17 (Right to Erasure)."""
        pass

    def request_data_correction(self, user_id: str, corrections: dict):
        """Correct data for GDPR Article 16 (Right to Rectification)."""
        pass
```

**HIPAA Compliance**:

```python
# Future HIPAA compliance configuration
hipaa_config = {
    'hipaa_enabled': True,
    'business_associate_agreement': True,
    'minimum_necessary_standard': True,
    'phi_handling': {
        'encryption_required': True,
        'access_logging': 'comprehensive',
        'transmission_security': True,
        'integrity_controls': True
    },
    'breach_notification': {
        'enabled': True,
        'notification_within': '60d',
        'covered_entity_contact': 'privacy@healthcare.com'
    }
}

# HIPAA-compliant healthcare agent
healthcare_agent = kaizen.create_agent("medical_assistant", {
    "model": "gpt-4",
    "hipaa_config": hipaa_config,
    "phi_detection": True,
    "minimum_necessary_enforcement": True
})
```

**SOC 2 Compliance**:

```python
# Future SOC 2 compliance configuration
soc2_config = {
    'soc2_enabled': True,
    'trust_service_criteria': {
        'security': True,
        'availability': True,
        'processing_integrity': True,
        'confidentiality': True,
        'privacy': True
    },
    'controls': {
        'access_controls': 'comprehensive',
        'change_management': 'formal',
        'system_monitoring': 'continuous',
        'incident_response': 'documented',
        'vendor_management': 'assessed'
    }
}
```

## Runtime Security

### Secure Execution Environment (Current)

**Current Security Measures**:

```python
# Current secure execution patterns
class SecureExecutionContext:
    def __init__(self, security_config):
        self.config = security_config
        self.sandbox_enabled = security_config.get('sandbox', True)
        self.resource_limits = security_config.get('resource_limits', {})

    def execute_safely(self, agent, inputs):
        """Execute agent with security controls."""
        try:
            # Input validation
            validated_inputs = self.validate_inputs(inputs)

            # Resource limiting
            with self.resource_limiter():
                # Sandboxed execution
                with self.sandbox():
                    results = agent.execute(validated_inputs)

            # Output validation
            validated_outputs = self.validate_outputs(results)

            return validated_outputs

        except SecurityError as e:
            self.log_security_event(e)
            raise
        except Exception as e:
            self.log_execution_error(e)
            raise

# Usage with current security
secure_context = SecureExecutionContext(security_config)
results = secure_context.execute_safely(agent, user_inputs)
```

**Resource Limits and Sandboxing**:

```python
# Current resource limiting
resource_limits = {
    'max_execution_time': 300,  # 5 minutes
    'max_memory_mb': 1024,      # 1GB
    'max_tokens_per_request': 4000,
    'max_requests_per_minute': 60,
    'max_concurrent_executions': 5
}

# Sandbox configuration
sandbox_config = {
    'enabled': True,
    'isolation_level': 'process',  # process, container, vm
    'network_access': 'restricted',
    'file_access': 'read_only',
    'system_calls': 'filtered'
}
```

### Threat Detection and Response (Planned)

**Security Monitoring**:

```python
# Future threat detection
threat_detection = {
    'enabled': True,
    'monitoring_targets': [
        'unusual_execution_patterns',
        'excessive_resource_usage',
        'suspicious_input_patterns',
        'data_exfiltration_attempts',
        'privilege_escalation_attempts'
    ],
    'response_actions': {
        'log_event': True,
        'alert_security_team': True,
        'temporary_suspension': True,
        'automatic_investigation': True
    }
}

# Threat detection integration
class ThreatDetector:
    def detect_anomalies(self, execution_context):
        """Detect security threats during execution."""
        pass

    def respond_to_threat(self, threat_type, severity):
        """Respond to detected security threats."""
        pass
```

## Security Best Practices

### Development Security

**Secure Coding Practices**:

```python
# Security-focused development patterns

class SecureAgent:
    """Security-aware agent implementation."""

    def __init__(self, config: Dict[str, Any]):
        # Validate configuration
        self._validate_security_config(config)

        # Initialize with secure defaults
        self.config = self._apply_security_defaults(config)

        # Set up security monitoring
        self.security_monitor = SecurityMonitor(self.config)

    def _validate_security_config(self, config: Dict[str, Any]) -> None:
        """Validate security configuration."""
        required_fields = ['model', 'security_level']
        for field in required_fields:
            if field not in config:
                raise SecurityConfigError(f"Required security field missing: {field}")

        # Validate security level
        valid_levels = ['basic', 'standard', 'high', 'critical']
        if config['security_level'] not in valid_levels:
            raise SecurityConfigError(f"Invalid security level: {config['security_level']}")

    def _apply_security_defaults(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Apply secure defaults to configuration."""
        defaults = {
            'input_validation': True,
            'output_sanitization': True,
            'audit_logging': True,
            'encryption_at_rest': True,
            'session_timeout': 3600  # 1 hour
        }

        # Apply defaults for missing values
        for key, value in defaults.items():
            if key not in config:
                config[key] = value

        return config

    def execute(self, inputs: Any) -> Any:
        """Execute with comprehensive security controls."""
        with self.security_monitor.execution_context() as ctx:
            # Pre-execution security checks
            ctx.validate_inputs(inputs)
            ctx.check_permissions()
            ctx.verify_resource_limits()

            try:
                # Secure execution
                results = self._execute_securely(inputs)

                # Post-execution security checks
                ctx.validate_outputs(results)
                ctx.log_execution_success()

                return results

            except Exception as e:
                ctx.log_execution_failure(e)
                raise
```

### Deployment Security

**Production Security Configuration**:

```python
# Production security configuration template
production_security_config = {
    'environment': 'production',
    'security_level': 'high',

    # Authentication & Authorization
    'auth': {
        'provider': 'enterprise_sso',
        'mfa_required': True,
        'session_timeout': 3600,
        'concurrent_sessions': 1
    },

    # Encryption
    'encryption': {
        'algorithm': 'AES-256-GCM',
        'key_rotation': 'monthly',
        'encrypt_at_rest': True,
        'encrypt_in_transit': True
    },

    # Audit & Monitoring
    'audit': {
        'level': 'comprehensive',
        'real_time_monitoring': True,
        'siem_integration': True,
        'retention_period': '7y'
    },

    # Network Security
    'network': {
        'firewall_enabled': True,
        'intrusion_detection': True,
        'ddos_protection': True,
        'ip_whitelisting': True
    },

    # Compliance
    'compliance': {
        'frameworks': ['soc2', 'gdpr', 'hipaa'],
        'automated_scanning': True,
        'compliance_reporting': 'monthly'
    }
}
```

### Security Monitoring Dashboard (Planned)

**Security Metrics and Alerting**:

```python
# Future security dashboard
class SecurityDashboard:
    def get_security_metrics(self):
        """Get real-time security metrics."""
        return {
            'authentication_events': {
                'successful_logins': 1250,
                'failed_logins': 15,
                'mfa_challenges': 45,
                'suspicious_activity': 2
            },
            'authorization_events': {
                'permission_grants': 890,
                'permission_denials': 12,
                'privilege_escalations': 0
            },
            'execution_security': {
                'secure_executions': 5431,
                'security_violations': 3,
                'threat_detections': 1,
                'incidents_resolved': 2
            },
            'data_protection': {
                'encrypted_operations': 5431,
                'pii_detections': 45,
                'dlp_blocks': 8,
                'data_classifications': 2341
            }
        }

    def get_security_alerts(self):
        """Get active security alerts."""
        return [
            {
                'alert_id': 'SEC-2024-001',
                'severity': 'medium',
                'type': 'unusual_access_pattern',
                'description': 'User accessing models outside normal hours',
                'timestamp': '2024-01-15T02:30:00Z',
                'status': 'investigating'
            }
        ]
```

## Implementation Roadmap

### Phase 1: Enhanced Security Foundation (Current + 2-4 weeks)

**Immediate Improvements**:
- ✅ Enhanced input validation and sanitization
- ✅ Secure configuration management
- ✅ Basic audit logging
- ✅ Error handling security

### Phase 2: Enterprise Authentication (4-6 weeks)

**Authentication & Authorization**:
- 🟡 Enterprise SSO integration
- 🟡 RBAC implementation
- 🟡 API key management
- 🟡 Session management

### Phase 3: Data Protection (6-8 weeks)

**Encryption & Privacy**:
- 🟡 End-to-end encryption
- 🟡 Data classification
- 🟡 DLP integration
- 🟡 Privacy controls

### Phase 4: Compliance & Monitoring (8-12 weeks)

**Compliance & Audit**:
- 🟡 Comprehensive audit trails
- 🟡 GDPR compliance
- 🟡 HIPAA compliance
- 🟡 SOC 2 compliance
- 🟡 Real-time monitoring

---

**🔒 Security Framework Established**: This comprehensive security guide provides the foundation for enterprise-grade security implementation. While many advanced features are planned, the current basic security measures provide a solid foundation for development and testing environments.
