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.
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What applies to me? →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.
24 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.
6 controls
MONMonitoring & Drift
Performance drift detection, anomaly alerting, and operational dashboards.
6 controls
SAFSafety & Reliability
Graceful degradation, fail-safe defaults, and reliability under adversarial inputs.
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.
15 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.
9 controls
MGVModel & Program Governance
Model lifecycle policy, intake and approval workflows, evaluation frameworks, and program-level AI governance maturity.
9 controls
SCTSector-Specific & Emerging
Healthcare, insurance, critical infrastructure, national security, and emerging-use-case controls not covered by domain-general frameworks.
9 controls
77 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
17 controlsAgent Prompt Injection Defense
Protect AI agents from prompt injection attacks — adversarial instructions embedded in external content that hijack agent behavior.
Agent Memory and Context Governance
Define policies governing what AI agents store in memory or persistent context, how long it is retained, who can access it, and under what conditions it is deleted.
Human Approval Gate for Irreversible Agent Actions
Require explicit human approval before an AI agent takes actions that are difficult or impossible to reverse, such as sending communications, modifying records, executing transactions, or deleting data.
Agent Action Audit Trail
Log every tool call, decision step, memory read/write, and external interaction made by an AI agent so that the full action sequence can be reconstructed after the fact.
Agent Scope and Task Boundaries
Define and enforce the boundaries of what an AI agent is permitted to do, preventing it from expanding its activity beyond its intended purpose.
Agent Knowledge Source Integrity
Validate that documents, databases, and external sources retrieved by AI agents during task execution have not been tampered with, poisoned, or substituted with adversarial content.
Agent Kill Switch and Emergency Stop
Maintain the operational capability to halt any running agent session, workflow, or agent class immediately — without relying on the agent itself to stop — and recover to a known-safe state.
Kill-Switch Propagation Testing
Regularly test that halt commands propagate correctly through all subagent layers and parallel orchestration environments, stopping all agent activity within a defined time window.
Multi-Agent Delegation Chain Logging
Log and attribute every action in a multi-agent system with sufficient detail to trace any action back to its originating instruction, authorized agent, and human principal.
Agent OAuth Scope Drift Detection
Monitor OAuth token scopes granted to AI agents and alert when scopes exceed the originally authorized set or when new permissions are acquired outside the formal provisioning process.
Agentic AI Deployment Readiness Assessment
Require a structured pre-deployment readiness assessment for tool-enabled AI agents, verifying that key governance controls are in place and that the agent's impact on connected systems has been evaluated before go-live.
Agentic Autonomy Expansion Criteria
Define standardized criteria for incrementally widening an AI agent's autonomy thresholds after initial deployment, ensuring that autonomy expansions are deliberate, evidence-based, and approved through the same governance process as initial deployment.
Agent Data Modification Blast-Radius Containment
Define and enforce limits on the scope of data resources a single AI agent can modify, ensuring that an agent malfunction, misuse, or prompt injection cannot propagate data corruption beyond a bounded and recoverable scope.
AI Tool and Plugin Supply Chain Risk Assessment
Assess and manage supply chain risk from third-party tools, plugins, and extensions used by AI agents, including AI-generated code committed to production repositories, applying software supply chain security controls at the AI extension layer.
RAG Retrieval Boundary Controls for Regulated Data
Implement retrieval boundary controls in RAG (retrieval-augmented generation) pipelines to prevent regulated, classified, or out-of-scope data from entering an AI agent's context window, reducing the risk of unauthorized disclosure or cross-contamination of sensitive information.
Agentic AI Governance Tooling Attestation
Require vendor attestation for platform-level tools used as primary agent oversight controls, validating that telemetry is complete, tamper-evident, and sufficient for governance purposes before the tool is relied upon as a control.
AI Permission Escalation Tabletop Exercise Program
Conduct recurring tabletop exercises that simulate AI agent permission escalation and propagation scenarios, testing whether existing controls contain the escalation, incident response teams can detect and respond effectively, and governance processes are sufficient.
Security
3 controlsPrompt Injection Prevention
Detect and block adversarial inputs designed to override AI system instructions, extract sensitive information, or cause the model to behave in unintended ways.
AI System Access Controls
Apply authentication, authorization, and role-based access controls to AI systems, APIs, 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
Record AI system inputs, outputs, model version, confidence scores, and contextual metadata for every decision that affects individuals 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
Maintain AI documentation, logs, and governance records in a state that can be produced efficiently in response to a regulatory inquiry or audit.
Change Management
2 controlsModel Deployment Gate Process
Require formal approval before new model versions, prompt changes, or configuration updates are deployed to production AI systems.
Model Rollback and Emergency Shutdown
Maintain tested procedures to rapidly revert an AI system to a prior version or disable it entirely in response to detected 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 pipelines, and logs.
Data Minimization for AI Systems
Ensure AI systems only process the data strictly necessary for their defined purpose, avoiding 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
Establish controls to identify, track, and manage open-source license obligations and supply chain risks introduced by AI-generated code before it is committed to production systems.
Monitoring & Drift
3 controlsModel Drift Detection
Monitor production AI systems for data drift, concept drift, and output distribution shifts that indicate degraded or changed model behavior.
AI Output Anomaly Detection
Automatically detect unusual, unexpected, or potentially harmful AI outputs in production for investigation and response.
Continuous Model Evaluation
Run ongoing evaluation pipelines against held-out test sets and curated adversarial examples to continuously measure model performance in production.
Safety & Reliability
4 controlsAI Output Validation
Validate AI-generated outputs against defined quality, safety, and format criteria before they are presented to users or used in downstream processes.
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, edge-case handling, and behavior under load 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
Maintain a live tracker of incident notification deadlines across all jurisdictions where the organization operates AI systems, pre-mapped to the incident categories that trigger each obligation.
Procurement
9 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
Define minimum contractual provisions that must be present in agreements with AI vendors, covering data handling, transparency, audit rights, and incident notification.
AI Procurement Risk Assessment
Assess and document the risks of procuring an AI system or service before approval, including technical, legal, privacy, and operational risks.
Vendor Safety Commitment Verification
Establish a workflow to verify that AI vendors are honoring their published safety commitments, voluntary pledges, and contractual safety obligations on an ongoing basis — not only at the time of procurement.
Vendor Model Update Disclosure and Re-Assessment Protocol
Require AI vendors to disclose material model updates, including capability changes, safety evaluation results, and model card revisions, and establish an internal re-assessment trigger process so that vendor model changes do not nullify the organization's prior due diligence.
AI Vendor Concentration Risk Assessment
Assess and manage the risk arising from organizational dependence on a small number of AI vendors or underlying model providers, and maintain a documented supplier redundancy posture to ensure operational continuity if a primary vendor is disrupted, suspends access, or becomes unavailable.
Federal AI Procurement Submission and Review Process
Establish an internal process for meeting AI vendor submission requirements under federal procurement rules, and monitor the transition of voluntary pre-deployment evaluation commitments to mandatory requirements so that procurement workflows remain compliant as the regulatory baseline shifts.
Shadow AI and Third-Party Widget Inventory and Classification
Detect and classify AI capabilities embedded in third-party SaaS tools, browser extensions, and client-side scripts operating within the organization's environment, and apply appropriate data processor and vendor risk controls to these shadow AI vectors.
Procurement-Stage AI Governance Conditions
Establish governance preconditions that must be satisfied before AI system procurement is completed, including binding contractual commitments to governance standards, whistleblowing policy requirements, and internal approval workflow triggers that make governance a dependency of procurement rather than a post-hoc addition.
Regulatory Compliance
6 controlsInternational AI Standards Monitoring Workflow
Track changes to international AI standards from ISO, NIST, OECD, ITU, and other bodies, and translate material updates into internal compliance obligation reviews.
Voluntary AI Framework Obligation Mapping
Map voluntary AI commitments (industry pledges, government agreements, sandbox conditions) against sector-specific regulatory requirements to identify where voluntary obligations create compliance risk or regulatory uplift.
Non-Legislative AI Obligation Tracker
Identify and track AI governance obligations that arise outside formal legislation, including procurement rules, bilateral agreements, sandbox exit conditions, and regulatory guidance letters.
Regulatory Engagement Process for AI Standards Development
Define how the organization participates in regulatory consultation processes, comment periods, and public-private working groups during the development of AI regulations and standards.
AI Content Watermarking and Labeling Compliance
Maintain an operational checklist of jurisdiction-specific requirements for labeling, watermarking, and provenance disclosure of AI-generated content, and implement the required technical and procedural controls.
Federal AI Regulatory Monitoring and Pre-Deployment Vetting
Monitor US federal AI regulatory developments across executive orders, agency guidance, and frontier model requirements, and maintain a pre-deployment vetting protocol aligned to current federal expectations.
Board & Executive Governance
7 controlsDirector AI Literacy and Competency Assessment
Establish a board-level AI literacy program that assesses director competency against defined standards, closes identified gaps through targeted education, and ensures the board can discharge its AI oversight obligations effectively.
AI Governance Committee Charter and Decision Rights
Establish a cross-functional AI governance committee with a formal charter defining its mandate, composition, decision rights, quorum requirements, escalation paths, and reporting obligations to the board.
Board-Level AI Safety Committee Charter
Establish a dedicated board-level committee with fiduciary responsibility for AI safety oversight, distinct from the operational AI governance committee, with defined authority over high-consequence AI risk decisions.
AI Governance ESG and Investor Disclosure
Establish a structured process for disclosing AI governance maturity, AI-related risk management, and AI safety posture to shareholders, institutional investors, and ESG rating agencies.
AI Governance Maturity Assessment
Conduct structured self-assessments and external benchmarking of the organization's AI governance program against defined maturity frameworks, and use assessment results to prioritize governance improvements.
AI Risk Tolerance and Appetite Documentation
Establish a formal process for defining, documenting, and approving the organization's AI risk tolerance and appetite across key risk categories, with board-level sign-off and periodic review.
Voluntary AI Governance Adequacy Standard
Define an internal AI governance adequacy standard for organizations operating without binding AI mandates, providing a documented and defensible governance posture that satisfies stakeholder expectations and anticipated regulatory requirements.
Model & Program Governance
7 controlsAI Model Preview and Staged Release Policy
Establish an internal policy that distinguishes preview and experimental AI system access from approved production deployment, and requires documented governance sign-off at each release stage before a system advances to broader use.
AI System Intake and Approval Workflow
Define a standardized intake process for all new AI system deployments that captures use case, data classification, risk tier, and ownership before the system enters the organization's environment, with cross-functional approval routing and GRC recordkeeping.
AI Governance Program Milestone Framework
Define structured governance milestones — evaluated at intervals across a deployment's lifecycle — that must be completed before an AI system advances to the next stage, treating governance readiness as a project dependency rather than a parallel or post-hoc activity.
Generative AI Input Data Classification
Establish a classification policy for data entering generative AI systems as inputs — prompts, context windows, retrieved documents, tool outputs, and conversation history — addressing privacy, confidentiality, and regulatory risks specific to the generative AI input surface that general data classification policies do not cover.
Emerging AI Modality Classification and Governance Extension
Establish a process for detecting when new AI modalities — ambient AI, multimodal agents, brain-computer interfaces, always-on AI assistants, and other emerging capability types — enter the organization's environment, and for extending governance coverage to those modalities before they are widely deployed.
AI-Generated Deliverable Disclosure and Citation Standards
Define standards for disclosing AI involvement in client-facing, regulatory, or published deliverables, and for verifying citations and factual claims in AI-generated content before external distribution, including disclosure before engagement closeout for professional services organizations.
AI Capability Claim Substantiation Standard
Establish a documentation standard for AI capability claims made internally and externally — in marketing materials, product documentation, sales conversations, regulatory submissions, and procurement responses — that produces substantiation evidence meeting FTC disclosure expectations and enterprise customer due diligence requirements.
Sector-Specific & Emerging
5 controlsAnthropomorphic and Companion AI Safeguards
Establish design requirements and governance review processes for AI systems that simulate human personality, emotional connection, or companionship, addressing psychological influence risks, minor user protections, and disclosure obligations that apply to AI products designed for ongoing interpersonal interaction.
Insurance Sector AI Documentation Standards
Establish documentation standards for AI systems used in insurance underwriting, claims adjudication, pricing, and fraud detection that meet state insurance commissioner market conduct examination expectations, NAIC model bulletin requirements, and applicable state-level algorithmic accountability obligations.
Self-Hosted Open-Weight AI Model Governance
Establish an intake policy and governance controls for AI model weights downloaded from public repositories and deployed in the organization's own infrastructure, addressing integrity verification, license compliance, safety evaluation before deployment, and ongoing update management distinct from vendor-hosted AI procurement.
AI-Specific External Complaints and Redress Mechanism
Design and operate a formal mechanism for external parties — customers, employees, subjects of AI decisions, and members of the public — to submit complaints about AI system outputs or decisions, receive timely responses, access human review of AI-assisted decisions upon request, and obtain meaningful redress where the AI decision was incorrect or unfair.
AI System Algorithm Register
Design and maintain a standardized register of deployed AI systems — public-facing or internal — that documents each system's purpose, decision scope, risk classification, data inputs, and accountability contacts, meeting emerging algorithmic accountability requirements from the EU AI Act, New York Local Law 144, Amsterdam-model algorithm registers, and equivalent frameworks.