AI Vendor Risk Assessment Template
Evaluate AI vendors with a structured 100-point scorecard. Assess security, model governance, compliance, reliability, and vendor viability. Includes risk ratings and approval recommendations.
Key Insights
Third-party AI introduces risks your organization doesn't directly control: how vendors handle your data, whether their models are tested for bias, what happens when their systems fail, whether they'll exist in two years. Vendor risk assessment for AI requires evaluating dimensions beyond traditional vendor due diligence.
This template provides a structured risk assessment framework specifically for AI vendors. It covers Security & Data Privacy, AI Model Governance, Compliance & Legal, Operational Reliability, and Vendor Viability—with weighted scoring, risk rating thresholds, and clear approval criteria.
Overview
When you use an AI vendor, you inherit their risks. If they're breached, your data is exposed. If their model is biased, your decisions are biased. If they use your data to train general models, your competitive advantage walks out the door. Third-party AI risk assessment must evaluate these factors systematically.
This template provides a comprehensive vendor risk assessment framework designed for AI services. It ensures you evaluate what matters for AI—not just check standard security boxes.
What's Inside
Section 1: Security & Data Privacy (30% weight)
- Data encryption (at rest and in transit)
- Access controls and authentication
- Data residency and regional compliance
- Data retention and deletion capabilities
- Security certifications (SOC 2, ISO 27001)
- Incident response procedures
- Penetration testing practices
Section 2: AI Model Governance (25% weight)
- Model training transparency
- Customer data usage policy
- Bias testing and fairness assessments
- Explainability capabilities
- Version control and documentation
- Output monitoring and logging
Section 3: Compliance & Legal (20% weight)
- Regulatory compliance (GDPR, CCPA, HIPAA)
- AI-specific regulation readiness
- IP and licensing terms
- Contract terms (liability, indemnification, SLAs)
- Audit rights
Section 4: Operational Reliability (15% weight)
- Uptime SLA and availability
- Scalability capabilities
- Integration quality
- Support responsiveness
- Disaster recovery/business continuity
Section 5: Vendor Viability (10% weight)
- Financial stability
- Market position
- Product roadmap
- Exit strategy and data portability
Risk Rating Framework:
- 85-100: Low Risk (Approved)
- 70-84: Medium Risk (Conditional approval, require remediation)
- 50-69: High Risk (Escalate to security/legal review)
- Below 50: Critical Risk (Do not proceed)
Assessment Documentation:
- Key findings summary
- Required remediation (for conditional approval)
- Assessor and approver sign-off
Who This Is For
- Vendor Risk Management assessing AI vendors
- Security Teams evaluating third-party AI security
- Procurement conducting AI vendor due diligence
- AI Governance Teams ensuring vendor oversight
- Legal/Compliance reviewing AI vendor risk
Why This Resource
Standard vendor risk assessments don't cover AI-specific factors. This template adds AI Model Governance, ensuring you evaluate whether vendors test for bias, how they handle your data for training, and whether they can explain their AI's decisions.
The weighted scoring and risk rating thresholds provide clear guidance—not just scores, but what those scores mean for approval decisions.
FAQ
Q: How do we get vendor information to complete this assessment?
A: Request security questionnaire completion, SOC 2 reports, and specific documentation on AI practices. The template criteria tell vendors what information you need. Gaps in responses should reduce scores.
Q: What about vendors who score in the "conditional" range?
A: Conditional approval means proceeding with documented remediation requirements. Define specific actions the vendor must take (or compensating controls you'll implement), timelines, and verification approach.
Q: How often should we reassess vendors?
A: Annual reassessment is typical for standard vendors. More frequent review (quarterly) may be appropriate for high-risk or critical AI vendors.
What's Inside
Section 1: Security & Data Privacy (30% weight)
- Data encryption (at rest and in transit)
- Access controls and authentication
- Data residency and regional compliance
- Data retention and deletion capabilities
- Security certifications (SOC 2, ISO 27001)
- Incident response procedures
- Penetration testing practices
Section 2: AI Model Governance (25% weight)
- Model training transparency
- Customer data usage policy
- Bias testing and fairness assessments
- Explainability capabilities
- Version control and documentation
- Output monitoring and logging
Section 3: Compliance & Legal (20% weight)
- Regulatory compliance (GDPR, CCPA, HIPAA)
- AI-specific regulation readiness
- IP and licensing terms
- Contract terms (liability, indemnification, SLAs)
- Audit rights
Section 4: Operational Reliability (15% weight)
- Uptime SLA and availability
- Scalability capabilities
- Integration quality
- Support responsiveness
- Disaster recovery/business continuity
Section 5: Vendor Viability (10% weight)
- Financial stability
- Market position
- Product roadmap
- Exit strategy and data portability
Risk Rating Framework:
- 85-100: Low Risk (Approved)
- 70-84: Medium Risk (Conditional approval, require remediation)
- 50-69: High Risk (Escalate to security/legal review)
- Below 50: Critical Risk (Do not proceed)
Assessment Documentation:
- Key findings summary
- Required remediation (for conditional approval)
- Assessor and approver sign-off
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