Implementation Layer
AI Governance Controls
Operational controls for real-world enterprise AI systems — organized by domain, mapped to regulations, with maturity levels and implementation guidance.
Human Oversight
Review gates, approval workflows, and override mechanisms for AI decisions.
6 controls
AGTAgentic AI
Goal constraints, action boundaries, and escalation paths for autonomous AI agents.
8 controls
SECSecurity
Adversarial input defense, prompt injection protection, and model access controls.
5 controls
ALCAudit & Logging
Immutable records of AI decisions, inputs, outputs, and model versions.
5 controls
CHMChange Management
Model release governance, version rollback, and change approval workflows.
5 controls
DGCData Governance
Training data provenance, privacy controls, and data retention policies.
5 controls
MONMonitoring & Drift
Performance drift detection, anomaly alerting, and operational dashboards.
5 controls
SAFSafety & Reliability
Graceful degradation, fail-safe defaults, and reliability under adversarial inputs.
5 controls
IRCIncident Response
Containment, investigation, and remediation procedures for AI system failures.
5 controls
PRCProcurement
Third-party AI vendor due diligence, contractual obligations, and offboarding.
5 controls
12 controls matching filters
Human Oversight
2 controlsAI Output Review Workflow
Define a structured, documented process for reviewing AI outputs before they are acted upon or distributed.
Override and Escalation Procedures
Document the procedures, authority levels, and logging requirements when humans reject, modify, or escalate AI-generated decisions.
Security
1 controlAudit & Logging
1 controlChange Management
3 controlsAI Model Version Control
Track model versions, configurations, prompts, and deployment history so that any production state can be reproduced and compared.
AI Model Change Documentation
Record what changed between model versions, why the change was made, what testing was performed, and who approved the deployment.
Model Deprecation Procedure
Define the process for retiring AI models from production, including notification, data handling, audit trail preservation, and transition planning.
Monitoring & Drift
1 controlIncident Response
3 controlsAI Incident Classification
Define a taxonomy for AI incidents that categorizes events by type and severity, determining the appropriate response urgency and notification requirements.
AI Post-Incident Review
Conduct a structured review after every significant AI incident to identify root causes, contributing factors, and systemic improvements.
AI Incident Log and Tracking
Maintain a centralized, structured log of all AI incidents, near-misses, and governance concerns, accessible to the AI governance function.