AI Vendor Risk Assessment Examples

Your AI vendor just passed SOC 2 Type II with flying colors. Three months later, their recommendation algorithm starts exhibiting discriminatory patterns that expose you to regulatory action. This scenario played out for a major financial services firm in 2023, highlighting why traditional vendor assessments fail for AI systems.

AI vendors introduce risks that standard security questionnaires miss: model opacity, training data poisoning, adversarial attacks, and algorithmic bias. The attack surface extends beyond infrastructure to include the models themselves. A compromised AI system doesn't just leak data—it makes bad decisions at scale.

This guide examines how organizations successfully adapted their TPRM programs for AI vendors, including specific assessment criteria, monitoring approaches, and lessons learned from early adopters.

The Healthcare AI Vendor Assessment Case

A regional health system with 12 hospitals needed to assess an AI vendor providing clinical decision support tools. Their existing vendor onboarding lifecycle focused on HIPAA compliance and traditional security controls. The AI system would analyze patient data and recommend treatment protocols—a high-risk use case requiring specialized assessment.

Initial Assessment Approach

The TPRM team started with their standard vendor risk assessment but quickly identified gaps:

Traditional Security Controls (covered adequately):

• Infrastructure security
• Access management
• Encryption standards
• Incident response procedures

AI-Specific Risks (missing from standard assessment):

• Training data provenance and bias
• Model explainability requirements
• Performance degradation monitoring
• Adversarial robustness testing

Developed AI Assessment Framework

The team created supplemental assessment criteria mapped to their existing risk tiering system:

Risk Category	Traditional IT Vendor	AI Vendor Addition
Data Security	Encryption, access controls	Training data governance, federated learning protocols
Availability	Uptime SLAs, DR plans	Model performance SLAs, drift detection
Compliance	HIPAA, SOC 2	AI fairness audits, explainability documentation
Third Party	Subprocessor management	Training data supplier vetting

Vendor Onboarding Modifications

The health system modified their vendor onboarding lifecycle to include:

1. Pre-contract AI disclosure requirements

   • Model architecture overview
   • Training data sources and demographics
   • Known limitations and failure modes
   • Bias testing methodology

2. Expanded due diligence scope

   • Review of model cards and datasheets
   • Assessment of retraining frequency
   • Evaluation of human-in-the-loop controls
   • Analysis of decision audit trails

3. Contract amendments

   • Right to audit model performance
   • Notification of significant model updates
   • Liability for algorithmic discrimination
   • Data deletion from training sets

Financial Services AI Risk Tiering Evolution

A multinational bank transformed their vendor risk tiering after an AI-powered KYC vendor's false positive rate suddenly spiked, flagging many legitimate customers as high risk.

Original Risk Tiering Criteria

Their legacy system classified vendors as Critical/High/Medium/Low based on:

• Data volume and sensitivity
• System criticality
• Regulatory exposure
• Financial impact

AI-Adapted Risk Tiering

The bank added AI-specific factors to their risk scoring:

Autonomy Level (new dimension):

• Tier 1: AI provides recommendations, humans decide
• Tier 2: AI makes decisions with human oversight
• Tier 3: AI makes autonomous decisions

Model Risk Factors:

• Decisional impact scope (individual vs. systemic)
• Reversibility of AI decisions
• Model complexity and interpretability
• Training data representativeness

This created a two-dimensional risk matrix combining traditional IT risk with AI autonomy risk.

Continuous Monitoring Implementation

A technology company managing 200+ vendors implemented AI-specific continuous monitoring after discovering their chatbot vendor's model had degraded, producing increasingly irrelevant responses.

Monitoring Framework Components

Traditional Security Monitoring (maintained):

• Vulnerability scanning
• Access reviews
• Incident notifications
• Certificate management

AI Performance Monitoring (added):

• Monthly accuracy metrics submission
• Drift detection alerts
• Bias metric tracking
• Adversarial testing results

Technical Implementation

The team built automated monitoring using:

1. API-based metric collection

   • Vendors submit performance data via standardized APIs
   • Automated threshold alerts for degradation
   • Trend analysis for gradual drift

2. Quarterly model audits

   • Sampling of AI decisions for review
   • Comparison against human baseline
   • Edge case testing protocols

3. Attack surface monitoring

   • Model endpoint security scanning
   • Input validation testing
   • Adversarial example detection

Key Findings

After 18 months of AI-specific monitoring:

• a notable share of AI vendors showed significant model drift
• a meaningful portion of failed bias testing in quarterly audits
• some experienced adversarial attacks
• Traditional security incidents remained at 3%

Lessons Learned and Best Practices

Assessment Design

What worked:

• Separating AI risks from IT security risks in questionnaires
• Requiring technical documentation (model cards, datasheets)
• Including data scientists in vendor assessments
• Mapping AI risks to business impact scenarios

What failed:

• Treating AI as just another SaaS application
• Relying solely on vendor attestations
• One-time assessments without continuous monitoring
• Generic security questionnaires

Organizational Changes

Successful programs made structural adaptations:

• Created AI risk specialist roles within TPRM teams
• Established cross-functional AI governance committees
• Developed AI-specific vendor contract templates
• Built relationships with AI ethics and safety teams

Common Edge Cases

Vendor Acquisition: When an assessed traditional vendor acquires AI capabilities or gets acquired by an AI company, reassessment is critical. One organization discovered their document management vendor added AI features without notification.

Model Updates: Unlike software updates, model retraining can fundamentally change behavior. Programs must define what constitutes a "material change" requiring reassessment.

Composite AI Systems: Vendors increasingly chain multiple AI models. Risk assessment must consider cascade effects and error propagation.

Compliance Framework Integration

Organizations successfully integrated AI assessments with existing frameworks:

SOC 2 + AI: Using SOC 2 common criteria plus AI-specific controls from the AICPA's AI assurance guidance

ISO 27001 + ISO 23053: Combining information security management with AI trustworthiness requirements

NIST Cybersecurity Framework + NIST AI Risk Management Framework: Dual framework approach for comprehensive coverage

Frequently Asked Questions

How do we assess AI vendors without in-house AI expertise?

Partner with data science teams or hire consultants for technical reviews. Focus on requiring standardized documentation like model cards that business teams can interpret. Many organizations start by assessing AI vendor governance processes before diving into technical model evaluation.

Should AI vendors have different SLAs than traditional IT vendors?

Yes. Include model performance metrics (accuracy, bias scores) alongside traditional uptime SLAs. Define acceptable drift thresholds and required notification timeframes for model updates.

How frequently should we reassess AI vendors?

Quarterly for high-risk AI applications, semi-annually for medium-risk. Traditional annual assessments miss model drift and emerging biases. Continuous monitoring should supplement periodic deep-dive assessments.

Can we use our existing vendor questionnaires for AI vendors?

Existing questionnaires provide a foundation but miss critical AI risks. Add sections on training data governance, model explainability, bias testing, and adversarial robustness. Keep security questions but expand scope significantly.

What's the biggest mistake in AI vendor assessments?

Focusing exclusively on the model while ignoring the data pipeline. Many AI failures stem from training data issues, ongoing data quality problems, or data drift rather than model architecture flaws.