What is AI Governance

AI governance is the framework of policies, procedures, and controls that organizations implement to ensure artificial intelligence systems operate ethically, comply with regulations, and align with business objectives. It encompasses risk assessment, accountability structures, transparency requirements, and continuous monitoring of AI systems throughout their lifecycle.

Key takeaways:

• AI governance establishes accountability and oversight for AI systems across the organization
• Regulatory frameworks like GDPR Article 22 and emerging AI regulations require documented governance processes
• Third-party AI vendors must demonstrate governance maturity through control mapping and audit trails
• Effective AI governance integrates with existing GRC frameworks through crosswalks to ISO 27001, SOC 2, and NIST

AI governance represents a critical control domain within third-party risk management as organizations increasingly rely on vendors deploying machine learning and automated decision-making systems. For compliance officers managing vendor relationships, AI governance maturity directly impacts regulatory exposure, particularly under GDPR Article 22 (automated decision-making), the EU AI Act, and sector-specific regulations like SR 11-7 for financial services.

The challenge extends beyond traditional IT controls. AI systems introduce unique risks through model drift, algorithmic bias, and explainability gaps that standard security frameworks don't address. When these systems sit within your vendor ecosystem, the governance challenge compounds—you inherit not only the AI risks but also the accountability for ensuring your vendors maintain appropriate controls.

This guide provides a practitioner-focused framework for evaluating AI governance in your third-party ecosystem, including specific control requirements, assessment criteria, and regulatory mapping needed for defensible vendor due diligence.

Core Components of AI Governance

AI governance operates through five interconnected control domains that map to established GRC frameworks:

1. Accountability and Oversight Structure

Every AI system requires documented ownership and escalation paths. In vendor assessments, verify:

• Executive sponsor identified for high-risk AI applications
• AI ethics committee or equivalent governance body with quarterly review cadence
• RACI matrix documenting roles for model development, validation, deployment, and monitoring
• Incident response procedures specific to AI failures (not general IT incidents)

Control mapping: SOC 2 CC1.2 (Board Oversight), ISO 27001 A.5.1.1 (Policies for Information Security)

2. Risk Assessment and Impact Analysis

AI-specific risk assessments differ from traditional IT risk assessments. Required documentation includes:

Algorithmic Impact Assessments (AIA) covering:

• Population segments affected by automated decisions
• Potential for discriminatory outcomes
• Accuracy benchmarks and acceptable error rates
• Human review requirements and appeal processes

Model Risk Management addressing:

• Training data provenance and quality controls
• Validation methodologies and frequency
• Performance degradation thresholds
• Retraining triggers and procedures

Framework reference: NIST AI Risk Management Framework (AI RMF 1.0), particularly the Map and Measure functions.

3. Transparency and Explainability Controls

Regulatory change management for AI centers on evolving transparency requirements. Current state:

Regulation	Transparency Requirement	Vendor Evidence Required
GDPR Art. 22	"Meaningful information about the logic involved"	Documented explanation methodology
EU AI Act (Draft)	Technical documentation for high-risk systems	Complete model cards, testing results
NYC Local Law 144	Bias audit summaries	Published audit reports, disparate impact ratios
CCPA (as amended)	Opt-out mechanisms for automated decisions	Technical implementation proof

Vendors must demonstrate both technical explainability (model interpretability) and user-facing transparency (clear notices about AI use).

4. Data Governance Integration

AI governance cannot exist in isolation from data governance. Key integration points:

Training Data Controls:

• Data lineage documentation from source to model
• Consent verification for personal data usage
• Bias detection in historical datasets
• Data minimization practices (using only necessary features)

Ongoing Data Management:

• Drift detection between training and production data
• PII handling in model inputs and outputs
• Data retention aligned with model lifecycle
• Cross-border data transfer controls for cloud-based AI

5. Continuous Monitoring and Audit Trail

Static assessments fail for AI systems. Implement continuous monitoring through:

Performance Metrics:

• Accuracy, precision, recall tracked monthly
• Fairness metrics across protected categories
• Latency and availability for real-time systems
• User feedback and appeal rates

Audit Trail Requirements:

• Every model version change logged with justification
• A/B test results and rollback procedures
• Incident reports with root cause analysis
• Regulatory inquiries and responses

Practical Implementation in Vendor Management

Initial Due Diligence

During vendor selection, AI governance maturity indicators include:

1. Published AI principles or ethics statements (necessary but insufficient)
2. ISO/IEC 23053 certification for AI trustworthiness (emerging standard)
3. Model cards or documentation for each AI system
4. Third-party audit reports specifically addressing AI controls
5. Insurance coverage explicitly including algorithmic errors

Ongoing Monitoring

Post-contract AI governance requires:

Quarterly Business Reviews covering:

• Model performance against SLA benchmarks
• Bias testing results and remediation
• Regulatory changes impacting AI usage
• Incident metrics and lessons learned

Annual Assessments including:

• Full model re-validation
• Training data audit
• Control effectiveness testing
• Regulatory crosswalk updates

Contract Considerations

AI-specific contract terms extend beyond standard DPAs:

Section X: Artificial Intelligence Governance

X.1 Vendor shall maintain documented AI governance policies meeting [Industry Standard]
X.2 Vendor shall provide 30-day notice of material model changes
X.3 Vendor shall conduct bias testing quarterly and share results
X.4 Vendor shall maintain explainability documentation per regulatory requirements
X.5 Vendor shall provide API access for performance monitoring
X.6 Right to audit includes model validation and training data review

Industry-Specific Requirements

Financial Services

• SR 11-7 model risk management requirements
• Fair lending considerations for credit decisioning
• FCRA compliance for AI in credit reporting

Healthcare

• FDA Software as Medical Device (SaMD) framework
• HIPAA considerations for AI processing PHI
• Clinical validation requirements

Human Resources

• EEOC guidance on AI in hiring
• State-specific AI audit requirements
• Accommodation processes for AI-screened candidates

Frequently Asked Questions

How does AI governance differ from traditional IT governance?

AI governance addresses unique risks like model drift, algorithmic bias, and explainability that don't exist in deterministic software. It requires continuous performance monitoring, fairness testing, and regulatory alignment specific to automated decision-making.

Which vendors require AI governance assessments?

Any vendor using machine learning for decisions affecting your customers or operations—including HR platforms with resume screening, marketing tools with personalization, fraud detection services, and chatbots handling customer data.

What's the minimum viable AI governance program for smaller vendors?

Document model ownership, conduct quarterly performance reviews, maintain training data records, and implement basic bias testing. Even simple rule-based systems need governance if they make automated decisions about people.

How do I map AI governance to existing frameworks like SOC 2?

AI governance maps across multiple trust service criteria: CC1.2 (oversight), CC1.4 (risk assessment), CC3.2 (risk mitigation), and PI1.1 (privacy). Create supplemental controls for AI-specific risks not covered by standard criteria.

What evidence should I collect during an AI governance audit?

Model documentation, training data samples, validation reports, bias testing results, incident logs, performance dashboards, governance committee minutes, and regulatory correspondence. Focus on demonstrating consistent processes, not perfect outcomes.

When should I escalate AI governance concerns?

Escalate immediately for unexplained performance degradation, evidence of discriminatory outcomes, regulatory inquiries, or vendor unwillingness to provide transparency. AI risks compound quickly—early escalation prevents larger issues.

How often should AI governance controls be reviewed?

High-risk AI systems need monthly performance reviews and quarterly governance committee oversight. Annual framework assessments should coincide with regulatory updates. Trigger immediate reviews after incidents or major model changes.