AI ML Vendor Risk Assessment Template

AI and ML vendors introduce unique third-party risks that traditional security questionnaires miss. Model drift, training data poisoning, adversarial attacks, and algorithmic discrimination require specialized assessment criteria beyond standard InfoSec controls.

This template addresses the gap between conventional vendor assessments and AI-specific risk domains. Built for TPRM teams managing AI vendors across regulated industries, it combines traditional security controls with emerging AI governance requirements from NIST RMF, EU AI Act draft provisions, and industry-specific guidance like SR 11-7 for financial services.

The framework uses risk-tiered assessment levels — from basic ML APIs to critical decision-making systems — ensuring proportionate due diligence without overwhelming vendors providing low-risk services. Each section includes evidence request templates, control mapping references, and practical scoring guidance developed from assessing 200+ AI vendors across financial services, healthcare, and technology sectors.

Core Template Components

1. Vendor Classification and Risk Tiering

The template begins with vendor categorization to determine assessment depth:

Tier 1 - Low Risk ML Services

• Pre-trained models with no customization
• Standard NLP/computer vision APIs
• No access to sensitive data
• Assessment: 40-50 core questions

Tier 2 - Moderate Risk ML Applications

• Custom model training on client data
• Decision support systems
• Limited PII processing
• Assessment: 80-100 questions including bias testing

Tier 3 - High Risk AI Systems

• Automated decision-making affecting individuals
• Healthcare diagnostics or financial underwriting
• Large-scale PII processing
• Assessment: 150+ questions with technical deep dives

2. Model Governance and Development

This section evaluates the vendor's ML development lifecycle:

Training Data Management

• Data source documentation and lineage
• Bias assessment in training datasets
• Data quality control procedures
• Privacy-preserving training techniques (federated learning, differential privacy)

Model Development Controls

• Version control and reproducibility
• Testing and validation methodologies
• Performance monitoring thresholds
• Drift detection mechanisms

Evidence Requirements:

• Model cards or documentation templates
• Bias testing reports
• Performance benchmarking results
• Change management procedures

3. Security and Privacy Controls

Standard security controls adapted for AI/ML contexts:

Infrastructure Security

• GPU cluster access controls
• Model storage encryption
• API authentication mechanisms
• Network segmentation for training environments

Data Protection

• Input data sanitization procedures
• Output filtering for PII leakage
• Model inversion attack defenses
• Membership inference protections

Mapped Controls:

• SOC 2 CC6.1, CC7.2 (logical access)
• ISO 27001 A.9.1, A.13.1 (access control, network security)
• GDPR Article 25 (data protection by design)

4. Algorithmic Accountability

Unique to AI assessments, this section addresses:

Explainability Requirements

• Model interpretability methods (SHAP, LIME)
• Decision audit trails
• Explanation interfaces for end users
• Technical documentation depth

Fairness and Bias Testing

• Protected attribute testing
• Disparate impact assessments
• Ongoing monitoring procedures
• Remediation processes

Human Oversight

• Override capabilities
• Review procedures for high-stakes decisions
• Escalation pathways
• Training for human reviewers

5. Operational Resilience

AI-specific availability and performance criteria:

Service Reliability

• Model serving SLAs
• Failover mechanisms
• Graceful degradation strategies
• Capacity planning for inference

Incident Response

• Model failure scenarios
• Rollback procedures
• Communication protocols
• Root cause analysis for model errors

Industry-Specific Applications

Financial Services

• SR 11-7 Compliance: Model risk management alignment
• Fair Lending: ECOA and fair lending testing for credit models
• AML/KYC: Explainability for suspicious activity detection
• Evidence Focus: Model validation reports, disparate impact testing

Healthcare

• FDA Considerations: Software as Medical Device (SaMD) pathways
• HIPAA Alignment: PHI handling in training data
• Clinical Validation: Real-world performance studies
• Evidence Focus: Clinical trial data, de-identification methods

Technology Sector

• Scale Considerations: Multi-tenant isolation
• API Security: Rate limiting, authentication patterns
• Developer Documentation: Integration guides, SDK security
• Evidence Focus: Penetration test reports, API documentation

Implementation Best Practices

1. Phased Rollout Strategy

Start with Tier 3 (high-risk) vendors to validate the framework:

1. Pilot with 3-5 critical AI vendors
2. Refine questions based on vendor feedback
3. Develop internal scoring rubrics
4. Train assessment team on AI-specific risks
5. Expand to Tier 2 and Tier 1 vendors

2. Evidence Collection Optimization

Pre-Assessment Package

• Request model cards upfront
• Collect existing SOC 2/ISO reports
• Review public API documentation
• Identify gaps requiring live demos

Structured Evidence Repository

• Create standardized folders for each evidence type
• Use naming conventions: VENDOR_YYYY-MM-DD_EVIDENCE-TYPE
• Maintain evidence currency matrix (quarterly updates for high-risk)

3. Scoring Methodology

Implement weighted scoring based on vendor tier:

Control Domain	Tier 1 Weight	Tier 2 Weight	Tier 3 Weight
Security	40%	35%	30%
Model Governance	20%	30%	35%
Privacy	25%	20%	20%
Operational	15%	15%	15%

4. Continuous Monitoring

Post-assessment monitoring for AI vendors:

• Quarterly model performance reviews
• Annual bias testing updates
• Incident notification requirements
• Material change triggers (new models, data sources)

Common Implementation Mistakes

1. Over-Assessing Low-Risk Vendors

Sending 150+ questions to a translation API vendor wastes resources. Use risk tiering to right-size assessments.

2. Ignoring Model Lifecycle Changes

Static annual assessments miss model updates. Require notification of material changes: new training data, architectural changes, or performance degradation.

3. Accepting Vague Responses

"We use industry best practices for bias testing" provides no evidence. Require specific methodologies, test results, and remediation examples.

4. Missing Cross-Functional Input

Pure InfoSec reviews miss AI risks. Include data science, legal, and business stakeholders in high-risk assessments.

5. Neglecting Third-Party Dependencies

AI vendors often rely on foundational models (GPT, BERT) or cloud ML services. Assess concentration risk and sub-processor controls.

Regulatory Alignment Matrix

Framework	Relevant Sections	Key Requirements
SOC 2	CC1.2, CC3.2, CC9.2	Risk assessment, privacy, changes
ISO 27001	A.8, A.14.2, A.15	Asset management, dev security, suppliers
GDPR	Articles 22, 25, 35	Automated decisions, DPbD, DPIA
CCPA	§1798.185(a)(16)	Automated decision opt-out
NIST AI RMF	GOVERN, MAP, MEASURE	Governance, risk mapping, metrics

Frequently Asked Questions

How do I determine which tier to assign an AI vendor?

Evaluate three factors: data sensitivity (PII/PHI handling), decision impact (affects individuals directly?), and integration depth (API vs custom model). High-risk indicators include healthcare diagnostics, credit decisions, or employment screening.

What evidence should I prioritize for initial AI vendor assessments?

Start with model documentation (architecture, training data sources), recent bias testing reports, and security assessment reports (SOC 2, penetration tests). These provide most risk visibility quickly.

How often should AI vendor assessments be updated?

High-risk (Tier 3) vendors need quarterly performance reviews and annual full reassessments. Moderate-risk vendors require semi-annual check-ins. Low-risk vendors can follow standard annual cycles unless material changes occur.

Which compliance frameworks specifically address AI/ML risks?

NIST AI Risk Management Framework provides the most comprehensive guidance. ISO/IEC 23053 and 23894 address AI trustworthiness. The EU AI Act (when finalized) will set prescriptive requirements. Industry-specific guidance includes SR 11-7 for banking.

How do I assess vendors who won't share model details due to IP concerns?

Focus on outcomes and controls rather than architectural specifics. Request performance benchmarks, bias testing methodologies, security controls documentation, and consider third-party AI audits or certifications as evidence alternatives.

What's the minimum viable AI vendor assessment for small teams?

Focus on five areas with 8-10 questions each: training data handling, model performance monitoring, security controls, bias testing practices, and incident response. This 40-question subset covers critical risks.

How do I handle vendors using multiple third-party AI services?

Map the full AI supply chain during initial assessment. Require notification of new AI subprocessors. For critical vendors, assess concentration risk if multiple vendors rely on the same foundational models.