AI Development and Deployment Security Policy (SEC-POL-012)

1. Objective

The objective of this policy is to establish comprehensive security requirements for the development, deployment, and operation of Artificial Intelligence (AI) and Machine Learning (ML) technologies at [Company Name]. This policy ensures that AI systems are developed with appropriate security controls, deployed through secure processes, and operated with robust security measures to protect the confidentiality, integrity, and availability of company information and electronic Protected Health Information (ePHI). This policy focuses specifically on technical security requirements for AI development lifecycles, deployment security, and operational security while coordinating with AI ethics and compliance requirements defined in SEC-POL-013.

2. Scope

This policy applies to all [Company Name] workforce members, contractors, and third parties involved in the development, deployment, configuration, or technical management of AI and ML technologies. It encompasses all AI development activities including model creation, training, validation, deployment, and maintenance. This policy covers both internally developed AI systems and the technical integration of third-party AI services, regardless of deployment model (cloud-based, on-premises, or hybrid), and applies to all technical aspects of AI system security including infrastructure, data pipelines, model protection, and operational monitoring.

3. Policy

• [Company Name] shall implement comprehensive security controls throughout the AI development and deployment lifecycle to ensure AI systems are developed, deployed, and operated securely while protecting sensitive information and maintaining system integrity as coordinated with AI ethics and compliance requirements in SEC-POL-013.

3.1 AI Development Security Framework

Secure development practices shall be integrated throughout the AI development lifecycle to ensure AI systems are built with appropriate security controls and protections.

3.1.1 Secure AI Development Lifecycle

• Security-by-Design Principles:
  • Security requirements shall be integrated into AI development lifecycle from initial design through deployment and maintenance
  • Threat modeling shall be conducted for all AI systems during the design phase to identify security risks and mitigation strategies
  • Code review and security testing shall be mandatory for all AI applications and model implementations
  • Vulnerability assessment of AI frameworks, libraries, and dependencies shall be performed before integration
  • Secure coding practices shall be applied to all AI model development and implementation activities
• AI Development Environment Security:
  • Isolated development environments with restricted network access for AI model training and experimentation
  • Version control and change management for AI systems including models, training data, and configuration files
  • Secure development workstations with endpoint protection and monitoring for AI developers
  • Access controls and authentication for AI development tools, platforms, and repositories
  • Development environment monitoring and logging for security events and policy compliance

3.1.2 AI Model Security and Protection

• Model Intellectual Property Protection:
  • Protection of AI models as intellectual property and trade secrets with appropriate classification and handling
  • Secure storage and versioning of AI models with encryption and access controls
  • Model watermarking and fingerprinting to detect unauthorized use or distribution
  • Secure backup and recovery procedures for AI models and related intellectual property
  • Legal protection measures including confidentiality agreements for AI model access
• Adversarial Attack Prevention:
  • Adversarial attack testing during AI model development and validation phases
  • Input validation and sanitization to prevent adversarial inputs and prompt injection attacks
  • Model robustness testing against various attack vectors including poisoning, evasion, and extraction
  • Defensive mechanisms including adversarial training and input monitoring
  • Regular security assessments of AI models for emerging attack techniques

3.2 AI Data Security and Protection

Comprehensive data security controls shall protect AI training data, model inputs, and outputs throughout the AI system lifecycle.

3.2.1 AI Training Data Security

• Training Dataset Protection:
  • Encryption of all AI training datasets containing sensitive information using approved encryption algorithms
  • Secure data pipelines for AI model training with authenticated and encrypted data transmission
  • Data lineage tracking and documentation for all AI datasets including source, transformations, and usage
  • Access controls and monitoring for AI training data with role-based permissions and audit logging
  • Secure data preparation environments with isolation from production systems
• Training Data Lifecycle Management:
  • Data quality and integrity assessments for AI training datasets with validation and verification procedures
  • Secure deletion of training data when no longer needed in accordance with data retention policies
  • Data versioning and change management for training datasets with immutable storage where feasible
  • Regular data refresh and update procedures for AI training datasets with security validation
  • Backup and recovery procedures for critical AI training data with appropriate security controls

3.2.2 AI System Data Protection

• Real-Time Data Security:
  • Encryption of all data at rest and in transit for AI systems processing sensitive information
  • Real-time data protection for AI system inputs and outputs with data loss prevention controls
  • Input validation and sanitization to prevent data injection attacks and model manipulation
  • Output filtering and validation to prevent data leakage and unauthorized information disclosure
  • Data masking and tokenization for sensitive data used in AI system testing and development
• AI Data Monitoring and Logging:
  • Comprehensive monitoring and logging of all AI system data interactions and processing activities
  • Data access logging for AI systems handling ePHI and other sensitive information
  • Anomaly detection for unusual data access patterns or volume in AI systems
  • Integration with data loss prevention (DLP) systems for AI-generated content and outputs
  • Incident detection and alerting for AI data security events and policy violations

3.3 AI Infrastructure and Platform Security

Robust infrastructure security controls shall protect AI systems, platforms, and supporting infrastructure from security threats.

3.3.1 AI Platform Security

• AI Infrastructure Hardening:
  • Security baselines and hardening standards for AI development and deployment platforms
  • Container security for AI workloads including image scanning, runtime protection, and network policies
  • Cloud security configurations for AI services including identity management, network controls, and monitoring
  • GPU and specialized hardware security for AI compute resources with firmware validation and monitoring
  • Infrastructure as code (IaC) security for AI platform deployments with configuration management and drift detection
• AI Service Security Controls:
  • API security controls for AI service integrations including authentication, authorization, and rate limiting
  • Service mesh security for AI microservices with mutual TLS and network segmentation
  • Load balancer and gateway security for AI service endpoints with SSL/TLS termination and monitoring
  • Database security for AI metadata and result storage with encryption and access controls
  • Message queue security for AI processing pipelines with encryption and access management

3.3.2 AI System Access Controls

• Authentication and Authorization:
  • Multi-factor authentication required for all AI system access including development, deployment, and operational access
  • Role-based access control (RBAC) for AI systems and platforms with principle of least privilege
  • Privileged access management (PAM) for AI system administration with session monitoring and recording
  • Service account management for AI system integrations with automated credential rotation
  • API key and token management for AI service access with secure storage and rotation
• Access Monitoring and Management:
  • Regular access reviews and recertification for AI system users with automated workflow and approval
  • Just-in-time (JIT) access for temporary AI system administration and emergency access
  • Access attempt monitoring and alerting for failed authentication and unauthorized access attempts
  • Session management and timeout controls for AI system access with automatic logout procedures
  • Integration with identity and access management (IAM) systems for centralized access control

3.4 AI Deployment and Operations Security

Secure deployment practices and operational security controls shall ensure AI systems are deployed and operated with appropriate security measures.

3.4.1 Secure AI Deployment

• Deployment Pipeline Security:
  • Secure CI/CD pipelines for AI model deployment with automated security testing and validation
  • Model validation and testing procedures before production deployment including security and performance testing
  • Staged deployment approach with security validation at each stage including development, staging, and production
  • Automated rollback procedures for AI model failures or security issues with rapid recovery capabilities
  • Deployment approval gates for production AI systems with security team validation and sign-off
• AI Model Deployment Controls:
  • Model signing and integrity verification for deployment processes to prevent tampering and unauthorized modifications
  • Canary deployment and A/B testing for new AI models with gradual rollout and monitoring
  • Environment promotion controls with security validation between development, staging, and production
  • Configuration management for AI model deployment with version control and change tracking
  • Deployment monitoring and alerting for AI model deployment failures and security events

3.4.2 AI Operations Security

• Continuous Security Monitoring:
  • Real-time monitoring of AI system performance, security events, and anomalous behavior
  • Behavioral analysis and anomaly detection for AI system operations with automated alerting
  • Security event correlation for AI systems with integration to Security Operations Center (SOC)
  • Threat detection and response capabilities specific to AI systems and attack vectors
  • Integration with security information and event management (SIEM) systems for centralized monitoring
• AI System Maintenance Security:
  • Patch management for AI frameworks, libraries, and dependencies with security priority assessment
  • Regular security assessments and penetration testing of AI systems and infrastructure
  • Model retraining and update procedures with security validation and approval processes
  • Performance monitoring and capacity management for AI systems with security impact assessment
  • Incident response procedures specific to AI security events with specialized response capabilities

3.5 AI Security Monitoring and Incident Response

Comprehensive monitoring and incident response capabilities shall provide early detection and rapid response to AI security incidents.

3.5.1 AI Security Monitoring

• Model Performance and Security Monitoring:
  • Continuous monitoring of AI model accuracy, performance, and drift with security impact assessment
  • Input and output monitoring for AI systems to detect adversarial attacks and anomalous behavior
  • Model behavior analysis to identify potential security issues, bias, or performance degradation
  • User interaction monitoring for AI systems with pattern analysis and anomaly detection
  • Integration with application performance monitoring (APM) tools for comprehensive AI system visibility
• AI Security Metrics and Alerting:
  • Key security indicators (KSIs) for AI system security including attack detection, access violations, and performance anomalies
  • Automated alerting for AI security events with severity classification and escalation procedures
  • Security dashboard and reporting for AI systems with executive visibility and compliance tracking
  • Trend analysis and reporting for AI security metrics with proactive risk identification
  • Integration with business intelligence and reporting tools for AI security insights

3.5.2 AI Incident Response

• AI-Specific Incident Response:
  • Specialized incident response procedures for AI security events including model compromise, data breaches, and adversarial attacks
  • AI incident classification and severity assessment with specialized response teams and procedures
  • Evidence collection and forensic analysis for AI security incidents with model and data preservation
  • Containment and mitigation procedures for AI security incidents with rapid response capabilities
  • Recovery and restoration procedures for AI systems with business continuity considerations
• AI Incident Coordination:
  • Integration with organizational incident response procedures including communication and escalation
  • Coordination with AI ethics and compliance teams for incidents involving bias, fairness, or regulatory issues
  • Legal and regulatory notification procedures for AI incidents with compliance team coordination
  • Post-incident review and improvement for AI security events with lessons learned integration
  • Cross-functional incident response training including AI-specific scenarios and tabletop exercises

3.6 Third-Party AI Service Security

Security controls for third-party AI services shall ensure appropriate security standards and protections for external AI integrations.

3.6.1 AI Vendor Security Assessment

• Vendor Security Evaluation:
  • Comprehensive security assessment of third-party AI vendors including infrastructure, data protection, and compliance
  • Security questionnaires and audits for AI service providers with documented security controls validation
  • Penetration testing and security validation of third-party AI services where feasible
  • Geographic data location restrictions and data sovereignty requirements for AI service providers
  • Service level agreements (SLAs) including security requirements, incident response, and breach notification

3.6.2 AI Service Integration Security

• Secure Integration Practices:
  • API security controls for third-party AI service integrations including authentication, encryption, and monitoring
  • Data minimization and protection for information sent to third-party AI services
  • Output validation and filtering for third-party AI service responses with security and content filtering
  • Network security controls for AI service communications including VPN, firewall, and monitoring
  • Contract and legal protections for AI service usage including data protection and liability clauses

3.7 Coordination with AI Ethics and Compliance

This policy coordinates with SEC-POL-013 (AI Ethics and Compliance Policy) to ensure comprehensive coverage of AI security, ethics, and compliance requirements.

3.7.1 Policy Integration Points

• Cross-Policy Coordination:
  • Security requirements shall support ethical AI principles and compliance obligations defined in SEC-POL-013
  • Technical security controls shall enable audit trails and monitoring required for AI governance and compliance
  • Incident response procedures shall coordinate with ethics and compliance teams for comprehensive AI incident management
  • Development security processes shall integrate bias testing and fairness validation requirements from SEC-POL-013
  • Vendor security assessments shall include ethical AI and compliance evaluation criteria from SEC-POL-013

4. Standards Compliance

This AI Development and Deployment Security Policy aligns with and supports compliance requirements from multiple regulatory frameworks while coordinating with AI ethics and compliance requirements in SEC-POL-013.

4.1 Regulatory Compliance Mapping

Policy Section	Standard/Framework	Control Reference
3.1	HITRUST CSF v11.2.0	09.b - System Development Controls
3.1	HITRUST CSF v11.2.0	06.a - Configuration Management Policy
3.2	HITRUST CSF v11.2.0	19.a - Data Protection and Privacy Policy
3.2	HITRUST CSF v11.2.0	09.a - Data Protection Policy
3.3	HITRUST CSF v11.2.0	08.a - Network Protection Policy
3.3	HITRUST CSF v11.2.0	11.a - Access Control Policy
3.4	HITRUST CSF v11.2.0	06.b - Change Management
3.5	HITRUST CSF v11.2.0	15.a - Incident Response Policy
3.5	HITRUST CSF v11.2.0	12.c - Log Monitoring
3.6	HITRUST CSF v11.2.0	14.a - Third Party Assurance
3.2	HIPAA Security Rule	45 CFR § 164.308(a)(4) - Information Access Management
3.2	HIPAA Security Rule	45 CFR § 164.312(a)(2)(iv) - Encryption and Decryption
3.5	HIPAA Security Rule	45 CFR § 164.312(b) - Audit Controls
3.6	HIPAA Security Rule	45 CFR § 164.314(a)(1) - Business Associate Contracts
3.1, 3.4	SOC 2 Trust Services Criteria	CC8.1 - System Development
3.3	SOC 2 Trust Services Criteria	CC6.1 - Logical Access Security
3.2	SOC 2 Trust Services Criteria	CC6.7 - Data Transmission and Disposal
3.5	SOC 2 Trust Services Criteria	CC7.1 - System Monitoring
3.1	NIST Cybersecurity Framework	PR.PT - Protective Technology
3.2	NIST Cybersecurity Framework	PR.DS - Data Security
3.5	NIST Cybersecurity Framework	DE.CM - Security Continuous Monitoring
3.5	NIST Cybersecurity Framework	RS.RP - Response Planning

5. Definitions

• Adversarial Attack: Deliberate attempts to manipulate AI systems through crafted inputs designed to cause incorrect outputs or behavior.

• AI Development Lifecycle: The complete process of developing AI systems from initial design through deployment and maintenance.

• API Security: Security controls and measures applied to Application Programming Interfaces to protect data and functionality.

• Model Drift: Degradation in AI model performance over time due to changes in underlying data patterns or distributions.

• Model Signing: Cryptographic process to verify the integrity and authenticity of AI models.

• Prompt Injection: Attack technique where malicious inputs are designed to manipulate AI system behavior through crafted prompts.

• Training Data Poisoning: Attack where malicious data is introduced into training datasets to compromise AI model behavior.

6. Responsibilities

Role	Responsibility
AI Security Team	Implementation and management of AI security controls, AI system security monitoring, and coordination with SEC-POL-013 requirements.
Data Scientists/AI Engineers	Development of secure AI systems, implementation of security controls in AI development, and coordination with AI ethics requirements.
DevOps/MLOps Engineers	Secure deployment and operations of AI systems, CI/CD pipeline security, and infrastructure protection for AI platforms.
IT Security Team	Integration of AI security with enterprise security controls, incident response for AI security events, and vendor security assessments.
System Administrators	Configuration and maintenance of secure AI infrastructure, access control management, and monitoring of AI system security.
Security Operations Center (SOC)	24/7 monitoring of AI security events, incident detection and response, and coordination with AI development teams.
Compliance Team	Coordination of AI security compliance requirements with SEC-POL-013 and regulatory validation of AI security controls.
All AI Development Staff	Implementation of secure development practices, compliance with AI security policies, and coordination with AI ethics and compliance teams.