AI Governance Controls
Choose a control domain to see what your team needs to implement. Each control includes regulatory mappings, maturity levels, and implementation guidance.
See how controls form an AI governance framework. You can also compare tools organized by the same control domains.
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Human Oversight
Review gates, approval workflows, and override mechanisms for AI decisions.
7 controls
AGTAgentic AI
Goal constraints, action boundaries, and escalation paths for autonomous AI agents.
30 controls
SECSecurity
Defenses against crafted inputs that trick AI into misbehaving, including prompt injection, plus controls on who can use each model.
7 controls
ALCAudit & Logging
Tamper-proof records of AI decisions, inputs, outputs, and which model version produced them.
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.
6 controls
MONMonitoring & Drift
Catching AI accuracy that slips over time (drift), alerts on unusual behavior, and operational dashboards.
6 controls
SAFSafety & Reliability
Safe fallbacks when AI fails, output checks, and reliable behavior when someone tries to trick the system.
6 controls
IRCIncident Response
Containment, investigation, and remediation procedures for AI system failures.
6 controls
PRCProcurement
Third-party AI vendor due diligence, contractual obligations, and offboarding.
18 controls
CMPRegulatory Compliance
Multi-jurisdiction regulatory mapping, standards monitoring, and compliance architecture for AI systems.
10 controls
BRDBoard & Executive Governance
Board education, committee charters, executive reporting, risk appetite, and enterprise-wide AI governance program design.
11 controls
MGVModel & Program Governance
Model lifecycle policy, intake and approval workflows, evaluation frameworks, and program-level AI governance maturity.
10 controls
SCTSector-Specific & Emerging
Healthcare, insurance, critical infrastructure, national security, and emerging-use-case controls not covered by domain-general frameworks.
9 controls
76 controls matching filters
Human Oversight
4 controlsAI System Risk Classification
Assign every AI system a risk tier that determines the oversight requirements, review frequency, and documentation standards applied to it.
Human Approval Gate for Consequential AI Decisions
Require a qualified human to review and approve AI-generated recommendations before they produce irreversible or high-stakes outcomes.
Reviewer Competency Requirements
Define minimum competency requirements for humans who review, approve, or override AI-generated outputs in high-risk contexts.
Override and Escalation Procedures
Document the procedures, authority levels, and logging requirements when humans reject, modify, or escalate AI-generated decisions.
Agentic AI
30 controlsAgent Permission Boundaries
Define and enforce the tools, APIs, data sources, and actions each agent may use, granting only the minimum needed (least privilege).
Agent Prompt Injection Defense
Protect AI agents from prompt injection attacks, hidden instructions planted in outside content that take over agent behavior.
Agent Memory and Context Governance
Set rules for agent memory and information kept between sessions. Define permitted content, retention periods, access rights, and deletion conditions.
Multi-Agent Trust Hierarchy
Define explicit rules for which AI agents can instruct, call on, or delegate authority to other agents in multi-agent systems.
Human Approval Gate for Irreversible Agent Actions
Require explicit human approval before hard-to-reverse AI agent actions. Examples include communications, record changes, transactions, and data deletion.
Agent Action Audit Trail
Log every AI agent tool call, decision step, memory read or write, and external interaction. Records must allow reconstruction of the full action sequence.
Agent Scope and Task Boundaries
Set and enforce each agent’s permitted activity. Prevent actions beyond its intended purpose.
Agent Environment Isolation
Run AI agents in isolated environments. Limit system, network, and data access to what their tasks require.
Agent and Non-Human Identity Management
Give each agent a distinct identity with narrowly limited credentials, access controls, and a defined lifecycle. Avoid shared service accounts and user identities.
Agent Knowledge Source Integrity
Check documents, databases, and external sources retrieved by agents. Verify that nobody has tampered with, poisoned, or substituted the content.
Agent Behavior Monitoring and Anomaly Detection
Monitor deployed AI agents for behavioral drift, unusual use of connected tools, unexpected resource use, and actions outside their permitted scope.
Agent Kill Switch and Emergency Stop
Maintain an immediate stop for any agent session, workflow, or type of agent. It must work without agent cooperation and support recovery to a known-safe state.
Kill-Switch Propagation Testing
Test emergency stops across subagents and agents running in parallel. Verify that all agent activity stops within a defined time window.
Multi-Agent Delegation Chain Logging
Log every action in systems where AI agents work together. Preserve enough detail to identify its originating instruction, authorized agent, and responsible person.
Agent OAuth Scope Drift Detection
Monitor AI agents’ OAuth permissions (scopes). Alert when permissions exceed the authorized set or arrive outside the formal approval process.
Agentic AI Deployment Readiness Assessment
Assess tool-using AI agents before deployment. Verify governance controls and evaluate potential effects on connected systems before launch.
Agentic Autonomy Expansion Criteria
Define criteria for expanding agent autonomy after deployment. Require supporting evidence and approval through the same governance process used for initial deployment.
Agent Data Modification Blast-Radius Containment
Limit which data each agent can change. Cap the damage from malfunctions, misuse, or prompt injection, and make affected data recoverable.
AI Tool and Plugin Supply Chain Risk Assessment
Assess supply-chain risks from agents’ third-party tools, plugins, and extensions. Include AI-generated code added to production software. Apply software supply-chain controls to these outside components.
RAG Retrieval Boundary Controls for Regulated Data
Set retrieval boundaries in RAG (AI document lookup) pipelines. Keep regulated, classified, and out-of-scope data out of what AI agents see to prevent unauthorized disclosure or sensitive-data mixing.
Human Oversight Classification Rationale Log
Document why each AI agent action requires human-in-the-loop (approval first) or human-on-the-loop (monitoring) oversight. Retain an auditable record of the reasoning behind those choices.
Agentic AI Governance Tooling Attestation
Obtain vendor attestations before using platform tools as primary agent oversight controls. Verify that monitoring data (telemetry) is complete, tamper-evident, and adequate for governance.
Agentic AI Security Assessment, CBRN and Cyber Espionage
Assess AI agent deployments for high-consequence misuse, including chemical, biological, radiological, and nuclear facilitation or AI-orchestrated cyber espionage. Implement mitigations proportionate to identified risks.
AI Permission Escalation Tabletop Exercise Program
Run recurring tabletop exercises on AI agents gaining and spreading unauthorized permissions. Test containment controls, incident detection, response effectiveness, and governance procedures.
Agent Access Entitlement Runtime Re-Validation
Re-check an agent's access entitlements against current policy at the moment it requests a resource, not only when it was last deployed or reviewed. A revoked or narrowed entitlement takes effect on the agent's next request, not at the next release cycle.
Mid-Task Agent Intervention Protocol
Give human overseers a way to redirect, pause, or correct an agent while it is mid-task, without having to invoke a full kill switch or wait for the task to finish.
Agent Ownership and Accountability Register
Keep a register that names an accountable person for every deployed AI agent. Record who owns its outcomes, who sponsors it at executive level, and what happens when either person leaves.
Agent External System Access Boundaries
Limit which outside websites, services, and government systems each agent may contact. Stop agents that keep retrying after being blocked, and alert on any contact outside the approved list.
User-Built Agent Go-Live Review
Require a proportionate review before any agent built by an employee on a self-service platform goes live. Match the depth of review to what the agent can reach and do.
MCP Server Inventory and Configuration Baseline
Keep an inventory of every MCP server (the connectors that let AI agents use tools and data) in the organization. Hold each one to a baseline for authentication, permissions, logging, and data-loss coverage.
Security
5 controlsPrompt Injection Prevention
Detect and block inputs designed to override instructions, extract sensitive information, or cause unintended AI behavior.
AI System Access Controls
Apply authentication, authorization, and role-based access controls to AI systems, their APIs (software connections), and the sensitive data they process.
Sensitive Data Handling in AI Pipelines
Prevent personally identifiable information, credentials, health data, and other sensitive content from entering AI models, prompts, or logs inappropriately.
AI API Credential Management
Securely manage, regularly replace, and audit API keys and credentials used to access AI services and model providers.
Adversarial Robustness Testing
Systematically test AI systems against hostile inputs, unusual cases, and known attack techniques before deployment and on a recurring basis.
Audit & Logging
2 controlsAI Decision Logging
Log inputs, outputs, model versions, confidence scores, and context for every AI decision affecting people or business outcomes.
High-Risk AI Audit Trail
Keep tamper-evident audit trails for AI in regulated domains. Cover the lifecycle from input through decision to outcome.
Change Management
3 controlsAI Model Version Control
Track model versions, configurations, prompts, and deployment history so that any past live setup can be recreated and compared.
Model Deployment Gate Process
Require formal approval before new model versions, prompt changes, or configuration updates go live in AI systems.
Model Rollback and Emergency Shutdown
Test procedures for rapidly reverting or disabling AI systems after failures or safety events.
Data Governance
3 controlsPII Handling in AI Systems
Establish controls governing how personally identifiable information is handled when it flows through AI inputs, outputs, training data, and logs.
AI Output Retention and Deletion
Define and enforce retention schedules and deletion procedures for AI-generated content, decisions, and the personal data contained within them.
AI-Generated Code and Open-Source License Compliance
Identify open-source license obligations and supply-chain risks in AI-generated code. Address them before adding the code to production systems.
Monitoring & Drift
2 controlsAI Output Anomaly Detection
Automatically detect unusual, unexpected, or potentially harmful AI outputs in live use for investigation and response.
Behavioral Anomaly Detection for Agentic Systems
Monitor AI agents for unexpected action sequences, data or system access, and behavior inconsistent with assigned tasks.
Safety & Reliability
6 controlsHallucination Detection and Mitigation
Implement controls to detect, reduce, and manage AI-generated factual errors and fabrications before they reach end users or inform decisions.
AI Output Validation
Check AI outputs against quality, safety, and format criteria before users or other systems receive them.
AI Graceful Degradation
Define and implement fallback behavior for AI systems when they are unavailable, underperforming, or producing outputs below acceptable quality thresholds.
AI Reliability Testing
Systematically test AI systems for consistency, repeatability, handling of unusual inputs, and behavior under heavy use before deployment and on a recurring basis.
Harmful Content Filtering
Apply input and output filtering to prevent AI systems from generating or acting on harmful, toxic, illegal, or policy-violating content.
Post-Deployment Adversarial Testing Cadence
Schedule recurring red-teaming (simulated attacks) of live systems by risk tier. Continue this testing after pre-deployment assessments.
Incident Response
6 controlsAI Incident Classification
Classify AI incidents by type and severity. Use the categories to set response urgency and notification requirements.
AI Incident Response Playbook
Document step-by-step procedures for identifying, containing, investigating, and resolving AI system incidents, including role assignments and escalation paths.
AI Harm Notification Procedures
Define procedures for notifying regulators, affected individuals, and other required parties when an AI system causes or contributes to harm.
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.
Cross-Jurisdictional Incident Reporting Tracker
Track AI incident notification deadlines across operating jurisdictions. Map each deadline to the incident categories triggering it.
Model & Program Governance
7 controlsAI Model Preview and Staged Release Policy
Distinguish preview and experimental access from approved production use. Require documented governance approval at each release stage before wider deployment.
AI System Intake and Approval Workflow
Use a standard intake process before new AI systems enter the organization. Record use case, data classification, risk tier, and ownership. Route approvals across relevant functions and retain governance, risk, and compliance (GRC) records.
AI Governance Program Milestone Framework
Set governance milestones throughout deployment. Require completion before a system advances to its next lifecycle stage.
Continuous AI Assurance Function Design
Operate an ongoing assurance function that produces regular evidence of control effectiveness. Give boards, regulators, and enterprise customers current records of the governance program’s performance.
Generative AI Input Data Classification
Classify data entering generative AI through prompts, background material, retrieved documents, results from connected tools, and conversation history. Address privacy, confidentiality, and regulatory risks absent from general data classification rules.
RAI Benchmark-Aligned Evaluation Framework
Map AI system evaluations to published benchmarks such as HELM Safety, AIR-Bench, and FACTS. Produce evidence regulators, auditors, and enterprise customers can compare against independent standards.
Emerging AI Modality Classification and Governance Extension
Detect new AI capabilities entering the organization, including ambient AI, agents that handle images and audio, brain-computer interfaces, and always-on assistants. Extend governance coverage before widespread deployment.
Sector-Specific & Emerging
7 controlsAnthropomorphic and Companion AI Safeguards
Set design requirements and reviews for AI that simulates personality, emotional connection, or companionship. Address psychological influence, protections for minors, and disclosure duties for ongoing interpersonal interaction.
Clinical AI Governance Committee Charter
Create a healthcare AI committee with clinical and technical expertise. Define quorum, decision rights, and escalation authority over clinical support and patient-care systems. Schedule reviews around applicable FDA Software as a Medical Device (SaMD) guidance and state clinical standards.
Critical Infrastructure AI Risk Assessment and Containment
Assess AI risks in energy, water, transport, and financial market infrastructure. Cover limiting AI's reach into operational technology, consequences of failure, and dependencies across sectors. Address these alongside standard enterprise AI risks.
National Security and Dual-Use AI Risk Assessment
Assess AI systems and research with both commercial and national security or weapons applications. Address export controls from the US Commerce Department's Bureau of Industry and Security (BIS), defense-related ITAR arms export duties, dual-use research protocols, and monitoring for foreign adversarial misuse.
Self-Hosted Open-Weight AI Model Governance
Set intake rules for AI model files (weights) downloaded from public repositories and hosted internally. Check integrity, licensing, and safety before deployment. Manage ongoing updates separately from vendor-hosted AI procurement.
AI-Specific External Complaints and Redress Mechanism
Provide a formal complaints process for customers, employees, affected individuals, and the public. Give timely responses and human review of AI-assisted decisions on request. Provide meaningful redress when decisions are incorrect or unfair.
AI System Algorithm Register
Maintain a register of internal and public-facing AI systems. Record purpose, decision scope, risk classification, data inputs, and accountability contacts. Address applicable requirements under the EU AI Act, NYC Local Law 144, Amsterdam-model registers, and equivalent frameworks.