AI Governance

Home > Deep Learning & MLOps > AI GovernanceUpdated Dec 2025

AI Risk Classification (EU AI Act)

Risk LevelDefinitionExamplesRequirements
Unacceptable RiskProhibited AI systemsSocial scoring, subliminal manipulation, real-time biometric ID (public)BANNED
High RiskSignificant impact on safety or rightsMedical devices, critical infrastructure, employment, credit scoring, law enforcementStrict requirements: risk management, data governance, documentation, human oversight
Limited RiskModerate transparency concernsChatbots, deepfakes, emotion recognitionTransparency obligations: disclosure of AI use
Minimal RiskLow or no riskSpam filters, recommendation systems, video gamesVoluntary codes of conduct

AI Governance Framework Components

1. Governance Structure

  • AI Ethics Board: Senior leadership oversight
  • AI Review Committee: Cross-functional review team
  • AI Risk Officer: Dedicated governance role
  • Domain Experts: Subject matter expertise
  • Legal & Compliance: Regulatory alignment

2. Policies & Standards

  • AI Principles: Organizational AI values
  • Use Case Guidelines: Acceptable use policies
  • Data Policies: Data usage and privacy
  • Model Standards: Quality and safety requirements
  • Incident Response: Issue handling procedures

3. Approval Workflows

  • Project Intake: Initial review and classification
  • Risk Assessment: Identify and evaluate risks
  • Ethics Review: Fairness, bias, impact analysis
  • Technical Review: Architecture and security
  • Sign-off: Approval gates before deployment

4. Documentation Requirements

  • Model Cards: Model characteristics and performance
  • Datasheets: Dataset documentation
  • Risk Assessments: Impact analysis
  • Audit Trails: Decision logs and changes
  • Compliance Records: Regulatory documentation

5. Monitoring & Auditing

  • Performance Monitoring: Ongoing model tracking
  • Bias Audits: Regular fairness assessments
  • Compliance Audits: Regulatory checks
  • Third-party Audits: Independent validation
  • Incident Tracking: Issue documentation

6. Stakeholder Engagement

  • User Involvement: End-user feedback
  • Community Input: External perspectives
  • Transparency Reports: Public disclosure
  • Grievance Mechanisms: Issue reporting
  • Education & Training: Stakeholder awareness

Model Approval Workflow

assignment

1. Project Intake

Submit proposal with use case, data sources, stakeholders

assessment

2. Risk Assessment

Classify risk level, identify potential harms, mitigation plans

gavel

3. Ethics & Legal Review

Fairness analysis, legal compliance, privacy impact assessment

engineering

4. Technical Review

Architecture, security, performance, monitoring plans

science

5. Testing & Validation

Performance testing, bias testing, adversarial testing

description

6. Documentation

Complete model cards, datasheets, audit trails

check_circle

7. Approval

Sign-off from governance board, conditional or full approval

rocket_launch

8. Deploy & Monitor

Gradual rollout, continuous monitoring, periodic reviews

AI Regulatory Landscape

RegulationRegionScopeKey Requirements
EU AI ActEuropean UnionComprehensive AI regulationRisk-based framework, high-risk requirements, transparency, fines up to 7% revenue
GDPR (AI provisions)European UnionData privacy with AI implicationsRight to explanation, automated decision-making restrictions, data protection
California Privacy Rights Act (CPRA)California, USPrivacy with automated decision-makingOpt-out rights, profiling restrictions, sensitive data protection
US Executive Order on AIUnited StatesFederal AI safety and securitySafety testing, red teaming, transparency for powerful models
China AI RegulationChinaAlgorithmic recommendations, deepfakesRegistration, content control, data localization
HIPAA (AI in healthcare)United StatesProtected health informationPrivacy, security, patient rights for AI systems
Equal Credit Opportunity ActUnited StatesCredit decisionsAdverse action notices, explainability for denials
NIST AI Risk Management FrameworkUnited StatesVoluntary guidanceRisk identification, measurement, mitigation
ISO/IEC 42001InternationalCertifiable AI management systemAIMS certification, risk assessment, PDCA methodology, integrates with ISO 27001

Key AI Governance Frameworks

verifiedISO/IEC 42001

AI Management System Standard

Published: December 2023

First international certifiable standard for AI management systems. Provides structured framework for responsible AI governance.

Key Components:

  • AI Management System (AIMS) structure
  • AI Risk Assessment methodology
  • AI Impact Assessment (AITA)
  • Plan-Do-Check-Act (PDCA) cycle
  • Data governance policies

Certification: 3-year validity with annual surveillance audits

Integrates with: ISO 27001, ISO 9001, ISO 13485

Certifiable

balanceNIST AI RMF

AI Risk Management Framework

Published: January 2023 (v1.0), GenAI Profile July 2024

Flexible framework for incorporating trustworthiness into AI design, development, and deployment.

Four Core Functions:

  • GOVERN: Establish risk management culture & policies
  • MAP: Identify AI system context & potential risks
  • MEASURE: Analyze, assess & monitor AI risks
  • MANAGE: Allocate resources & implement risk response

Applicability: Public & private sector organizations of all sizes

Resources: AI RMF Playbook, Roadmap, Crosswalks

Voluntary

gavelEU AI Act

Regulation (EU) 2024/1689

Effective: August 1, 2024

World's first comprehensive legally binding AI regulation. Risk-based approach with tiered obligations.

Implementation Timeline:

  • Feb 2025: Prohibited AI & AI literacy obligations
  • Aug 2025: GPAI rules, transparency requirements
  • Aug 2026: High-risk AI system requirements
  • Aug 2027: Extended compliance for legacy systems

Penalties: Up to EUR 35M or 7% global revenue

Scope: Any org placing AI on EU market or affecting EU residents

Mandatory

hubIntegrated Implementation Approach

Organizations typically adopt these frameworks in combination:

  1. NIST AI RMF for foundational risk identification and management culture
  2. ISO/IEC 42001 for formal, certifiable governance structures
  3. EU AI Act alignment for regulatory compliance and market access

This layered approach provides risk management foundation, systematic governance, and regulatory compliance across multiple jurisdictions.

AI Governance Tools & Platforms

Model Governance Platforms

  • Fiddler AI: Model governance, monitoring, explainability
  • Arthur AI: Model performance and governance
  • Credo AI: AI governance and compliance platform
  • Robust Intelligence: AI security and governance

Documentation Tools

  • Model Card Toolkit: Standardized model documentation
  • Datasheets for Datasets: Dataset documentation templates
  • VerifyML: Model validation and documentation
  • Aether: Microsoft's responsible AI dashboard

Risk Assessment

  • NIST AI RMF: Risk management framework
  • AI Impact Assessment Tool: Google's risk evaluation
  • Algorithm Audit: Third-party audit services
  • ALTAI: EU's assessment list for trustworthy AI

Compliance Management

  • OneTrust AI Governance: Privacy and AI compliance
  • DataGrail: Privacy automation for AI
  • TrustArc: Privacy and risk compliance
  • Transcend: Data privacy infrastructure