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.
28 controls
SECSecurity
Defenses against crafted inputs that trick AI into misbehaving, including prompt injection, plus controls on who can use each model.
6 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.
17 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
83 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.
Automation Bias Prevention
Implement measures to detect and counteract the tendency for human reviewers to defer to AI recommendations without adequate critical evaluation.
Reviewer Competency Requirements
Define minimum competency requirements for humans who review, approve, or override AI-generated outputs in high-risk contexts.
Agentic AI
20 controlsAgent 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.
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 Knowledge Source Integrity
Check documents, databases, and external sources retrieved by agents. Verify that nobody has tampered with, poisoned, or substituted the content.
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.
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.
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 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
3 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.
Audit & Logging
3 controlsAI Decision Logging
Log inputs, outputs, model versions, confidence scores, and context for every AI decision affecting people or business outcomes.
AI Explainability Documentation
Document how AI systems reach decisions in sufficient detail that affected individuals, reviewers, and regulators can understand and challenge outcomes.
Regulatory Audit Readiness
Keep AI documentation, logs, and governance records ready for efficient retrieval during regulatory inquiries or audits.
Change Management
2 controlsModel 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
4 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.
Data Minimization for AI Systems
Limit AI processing to data needed for its defined purpose. Avoid unnecessary collection, retention, or use of personal information.
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
3 controlsModel Drift Detection
Monitor live AI systems for data drift, concept drift, and shifts in outputs that signal degraded or changed model behavior.
AI Output Anomaly Detection
Automatically detect unusual, unexpected, or potentially harmful AI outputs in live use for investigation and response.
Continuous Model Evaluation
Run ongoing automated tests on reserved test data and deliberately tricky inputs to continuously measure live model performance.
Safety & Reliability
4 controlsAI 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.
Incident Response
3 controlsAI 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.
Cross-Jurisdictional Incident Reporting Tracker
Track AI incident notification deadlines across operating jurisdictions. Map each deadline to the incident categories triggering it.
Procurement
10 controlsAI Vendor Due Diligence
Assess AI vendors against security, governance, and compliance criteria before procurement and at defined intervals during the vendor relationship.
AI Contractual Requirements
Set minimum AI vendor contract terms for data handling, transparency, audit rights, and incident notification.
AI Procurement Risk Assessment
Assess technical, legal, privacy, and operational risks before approving an AI system or service purchase. Document the findings.
Vendor Safety Commitment Verification
Check whether vendors honor published safety commitments, voluntary pledges, and contractual duties throughout the relationship.
Vendor Model Update Disclosure and Re-Assessment Protocol
Require disclosure of material vendor model updates, including capability changes, safety results, and revised model cards (vendors' published model fact sheets). Trigger internal reassessment when updates affect prior due diligence.
AI Vendor Concentration Risk Assessment
Assess dependence on a small number of AI vendors or underlying model providers. Document supplier alternatives supporting continuity if a primary provider fails, suspends access, or becomes unavailable.
Federal AI Procurement Submission and Review Process
Set a process for federal AI procurement submissions. Track voluntary pre-deployment evaluation commitments becoming mandatory and update procurement workflows accordingly.
Shadow AI and Third-Party Widget Inventory and Classification
Discover and classify AI built into cloud software (SaaS), browser extensions, and code running in web pages. Apply appropriate processing and vendor-risk controls to these shadow AI sources.
Procurement-Stage AI Governance Conditions
Set governance conditions that must be met before AI procurement finishes. Include contractual standards, whistleblowing policies, and internal approval triggers.
AI Developer Tool Data Boundary Controls
Define which coding assistants and AI developer tools the enterprise permits, including what data they may transmit. Evaluate data boundaries before deployment and whenever vendor policies change.
Regulatory Compliance
6 controlsInternational AI Standards Monitoring Workflow
Track AI standards updates from ISO, NIST, OECD, ITU, and other bodies. Review internal obligations when changes are material.
Voluntary AI Framework Obligation Mapping
Compare industry pledges, government agreements, and regulatory sandbox commitments with sector requirements. Identify resulting compliance risks and additional obligations.
Non-Legislative AI Obligation Tracker
Track AI governance duties arising through procurement rules, bilateral agreements, regulatory sandbox exit conditions, and regulatory guidance letters.
Regulatory Engagement Process for AI Standards Development
Define participation in regulatory consultations, comment periods, and public-private working groups developing AI rules and standards.
AI Content Watermarking and Labeling Compliance
Keep a checklist of jurisdiction-specific AI content labeling, watermarking, and provenance (content origin) requirements. Implement the necessary technical controls and procedures.
Federal AI Regulatory Monitoring and Pre-Deployment Vetting
Monitor federal executive orders, agency guidance, and rules for the most advanced AI models. Maintain pre-deployment checks reflecting current US expectations.
Board & Executive Governance
8 controlsDirector AI Literacy and Competency Assessment
Assess directors’ AI knowledge against defined standards. Address gaps through targeted education so the board can carry out its oversight duties.
AI Governance Committee Charter and Decision Rights
Charter an AI governance committee with members from relevant functions. Define its mandate, membership, decision rights, quorum, escalation paths, and board reporting duties.
Board-Level AI Safety Committee Charter
Establish a board committee responsible for AI safety oversight and high-consequence risk decisions. Keep its fiduciary responsibilities distinct from the operational governance committee.
AI Governance ESG and Investor Disclosure
Set a process for reporting AI governance maturity, risk management, and safety practices to shareholders, institutional investors, and ESG agencies.
AI Governance Maturity Assessment
Assess the governance program against defined maturity frameworks and external benchmarks. Use the findings to prioritize improvements.
AI Risk Tolerance and Appetite Documentation
Document and approve AI risk tolerance across relevant risk categories. Obtain board sign-off and schedule periodic reviews.
Voluntary AI Governance Adequacy Standard
Define an internal governance standard where binding AI mandates are absent. Document how it meets stakeholder expectations and anticipated regulatory requirements.
AI Governance Training Program and Completion Tracking
Maintain AI training tailored to staff roles, with documented curricula and annual completion targets. Keep auditable completion records and review content periodically. Cover staff responsible for design, deployment, review, and oversight.
Model & Program Governance
8 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.
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.
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.
AI-Generated Deliverable Disclosure and Citation Standards
Set AI disclosure standards for client-facing, regulatory, and published work. Verify AI-generated citations and factual claims before external distribution. Professional services teams should disclose AI involvement before closing engagements.
AI Capability Claim Substantiation Standard
Set documentation standards for AI capability claims. Cover marketing, product documentation, sales discussions, regulatory submissions, and procurement responses. Retain supporting evidence that meets Federal Trade Commission (FTC) disclosure expectations and enterprise due diligence requirements.
AI Output Pre-Publication Verification for High-Stakes Claims
Require human checks of AI-generated numbers, legal citations, regulatory references, and other high-stakes claims before external publication or regulatory submission. Keep verification checklists and auditable approval records.
Sector-Specific & Emerging
5 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.
Insurance Sector AI Documentation Standards
Set documentation standards for AI in underwriting, claims, pricing, and fraud detection. Meet applicable state insurance examination expectations, National Association of Insurance Commissioners (NAIC) model bulletin requirements, and algorithmic accountability duties.
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.