AI Safety Pack - Policy-to-Controls Mapping (Coder2 Draft)

Data

1. Data Classification and Categorization

   • Owner: IT
   • Evidence Artifact: Data classification matrix
   • Implementation Notes: Define data categories (e.g., public, confidential) and implement access controls accordingly.

2. Data Encryption in Transit and at Rest

   • Owner: IT
   • Evidence Artifact: Encryption keys and policies
   • Implementation Notes: Use strong encryption protocols for data transmission and storage to protect sensitive information.

Access

1. Access Control Lists (ACLs)

   • Owner: IT
   • Evidence Artifact: ACL configurations
   • Implementation Notes: Implement fine-grained access controls to restrict user access based on roles and responsibilities.

2. Authentication Mechanisms

   • Owner: IT
   • Evidence Artifact: Authentication logs
   • Implementation Notes: Use multi-factor authentication (MFA) to ensure secure user access to systems and data.

Vendor

1. Vendor Risk Assessment

   • Owner: Legal/Risk
   • Evidence Artifact: Vendor risk assessment reports
   • Implementation Notes: Regularly assess third-party vendors' security practices and risk levels before allowing access to sensitive data or systems.

2. Contractual Obligations with Vendors

   • Owner: Legal
   • Evidence Artifact: Vendor contracts
   • Implementation Notes: Ensure vendor contracts include clauses for data protection, confidentiality, and compliance with AI safety policies.

Logging

1. Audit Logs

   • Owner: IT
   • Evidence Artifact: Audit logs database
   • Implementation Notes: Maintain comprehensive audit logs to track user activities, system changes, and access attempts.

2. Event Monitoring

   • Owner: IT
   • Evidence Artifact: Event monitoring dashboards
   • Implementation Notes: Implement real-time monitoring tools to detect and respond to security events or anomalies promptly.

Human-in-loop

1. Human Review of High-Risk Decisions

   • Owner: HR/Legal/Risk
   • Evidence Artifact: Decision logs and reviews
   • Implementation Notes: Establish processes for human review of AI-driven decisions that have high risk implications.

2. User Consent Management

   • Owner: Legal
   • Evidence Artifact: User consent forms and records
   • Implementation Notes: Ensure user consent is obtained before collecting, processing, or using their data for AI applications.

Incident Response

1. Incident Response Plan

   • Owner: IT/Risk
   • Evidence Artifact: Incident response plan document
   • Implementation Notes: Develop and regularly update an incident response plan to address potential security breaches or AI-related incidents effectively.

2. Post-Incident Review

   • Owner: IT/Risk
   • Evidence Artifact: Post-incident review reports
   • Implementation Notes: Conduct thorough reviews after security incidents to identify lessons learned and improve future responses.

Training

1. Regular Security Training for Employees

   • Owner: HR
   • Evidence Artifact: Training records and attendance logs
   • Implementation Notes: Provide ongoing security training sessions to ensure employees are aware of AI safety policies and best practices.

2. Training Programs for Vendor Personnel

   • Owner: Legal/Risk
   • Evidence Artifact: Training program documents and completion records
   • Implementation Notes: Train third-party vendor personnel on data protection, confidentiality, and compliance with AI safety guidelines.

3. AI Ethics Training for Stakeholders

   • Owner: HR/Legal
   • Evidence Artifact: Training materials and participation logs
   • Implementation Notes: Offer training programs to key stakeholders (e.g., executives, IT staff) on AI ethics and responsible AI practices.