AI Model Vendor Due Diligence Case Study

AI model vendors present unique risk profiles that standard vendor assessments miss. Unlike traditional software vendors, AI providers introduce algorithmic bias risks, training data compliance issues, and model explainability challenges that directly impact your regulatory posture.

This case study examines how a mid-sized financial institution discovered and mitigated critical risks when onboarding an AI vendor for customer service automation. The vendor's initial SOC 2 Type II certification masked deeper issues with data handling practices and model governance that only emerged through specialized AI-focused diligence.

The assessment framework developed through this process now serves as the institution's standard for evaluating all AI and machine learning vendors, reducing onboarding time from 12 weeks to 6 weeks while catching twice as many critical risks.

Background and Initial Discovery

The vendor selection began routinely. A customer experience team identified an AI chatbot provider promising most first-contact resolution rates. The vendor checked standard boxes: SOC 2 Type II certified, ISO 27001 compliant, and strong references from similar-sized financial institutions.

During initial risk tiering, the vendor scored as medium-risk based on traditional criteria. They would process customer inquiries but not financial transactions. Standard security questionnaires came back clean.

The first red flag appeared during technical architecture review. The vendor mentioned their model was "trained on diverse financial services conversations" but provided no specifics about data sources. When pressed, they revealed the training dataset included publicly available customer service transcripts scraped from forums and social media.

The Four-Phase AI Diligence Framework

Phase 1: Data Lineage and Compliance Mapping

The risk team developed a specialized questionnaire focusing on AI-specific concerns:

Data Source Assessment:

• Origin of all training data
• Personal information handling procedures
• Cross-border data transfer mechanisms
• Data retention and deletion capabilities
• Consent verification for scraped data

Initial findings revealed the vendor couldn't provide complete data lineage documentation. Their training data included:

• 2.3 million customer service interactions from public forums
• 890,000 social media complaints mentioning financial institutions
• 1.5 million support ticket summaries from unknown sources

Legal review confirmed this created immediate GDPR Article 6 violations and potential CCPA non-compliance.

Phase 2: Model Governance and Explainability

Traditional vendor assessments ignore algorithmic accountability. The team added specific model governance requirements:

Technical Assessment Matrix:

Requirement	Vendor Status	Risk Level
Model version control	Partial - major versions only	High
Bias testing documentation	None available	Critical
Explainability features	Black box model	High
Performance monitoring	Basic accuracy metrics only	Medium
Drift detection	Not implemented	High

The vendor's model operated as a complete black box. They couldn't explain why specific responses were generated, creating potential fair lending compliance issues if the chatbot provided different information to protected classes.

Phase 3: Security and Attack Surface Analysis

AI models introduce novel attack vectors. The security assessment expanded beyond traditional penetration testing:

AI-Specific Security Gaps Identified:

• No protection against adversarial inputs
• Model extraction vulnerabilities present
• Training data poisoning detection absent
• No monitoring for prompt injection attacks

One concerning discovery: the vendor's API allowed unlimited queries without rate limiting, enabling potential model theft through systematic probing.

Phase 4: Continuous Monitoring Requirements

Standard vendor monitoring tracks uptime and security patches. AI vendors require additional oversight:

Implemented Monitoring Framework:

1. Monthly model performance benchmarking
2. Quarterly bias testing against protected characteristics
3. Weekly output quality sampling
4. Real-time anomaly detection for unexpected responses
5. Training data update notifications

Remediation and Contract Negotiations

The vendor initially resisted additional requirements, citing competitive concerns about model transparency. Negotiations stalled until the institution quantified potential regulatory penalties:

• GDPR violations: Up to €20 million or 4% of global turnover
• CCPA penalties: $7,500 per intentional violation
• Fair lending violations: Uncapped civil money penalties
• Reputational damage from biased AI outcomes

This financial impact analysis motivated vendor cooperation. Over six weeks, they implemented:

1. Complete data lineage documentation with provenance tracking
2. Quarterly bias audits by independent third parties
3. Model explainability features for high-stakes decisions
4. Contractual commitments to remediate identified biases
5. Indemnification clauses for AI-specific risks

Implementation and Outcomes

Post-remediation vendor onboarding proceeded with enhanced controls:

Onboarding Lifecycle Modifications:

• Week 1-2: Legal review of AI-specific contract terms
• Week 3-4: Technical integration with monitoring hooks
• Week 5: Bias testing on production-like data
• Week 6: Stakeholder training on model limitations

Three months post-implementation, continuous monitoring caught the first issue. The model showed a notable share of lower resolution rates for customers with non-English names. The vendor's remediation process, now contractually required, resolved the bias within two weeks.

Lessons Learned and Framework Evolution

This case study yielded several framework improvements:

Risk Tiering Adjustments:

• Any vendor using AI/ML automatically starts at medium-risk minimum
• Vendors processing personal data for model training tier as high-risk
• Black box models without explainability features require CISO approval

Vendor Onboarding Enhancements:

• Dedicated AI risk questionnaire supplementing standard assessments
• Mandatory legal review for data processing agreements
• Technical proof-of-concept including bias testing before contract signing
• Quarterly business reviews focusing on model performance metrics

Common Variations and Edge Cases

Different AI vendor types require adjusted approaches:

Natural Language Processing Vendors: Focus on training data diversity and linguistic bias testing. One healthcare client discovered their transcription vendor performed a substantial portion of worse on accented English, creating care quality disparities.

Computer Vision Providers: Emphasize demographic representation in training data. A retail client's facial recognition vendor showed some higher false positive rates for darker skin tones, requiring complete model retraining.

Predictive Analytics Vendors: Scrutinize feature selection and correlation vs. causation issues. An insurance client found their risk scoring vendor used zip codes as proxies for protected characteristics, creating redlining risks.

Compliance Framework Alignment

The enhanced diligence process maps to multiple regulatory requirements:

GDPR Compliance:

• Article 22: Automated decision-making protections
• Article 35: Data Protection Impact Assessments for AI systems
• Article 5: Transparency and lawfulness of processing

U.S. Regulatory Guidance:

• OCC 2021-17: Model Risk Management for AI
• CFPB focus on fair lending implications
• FTC guidance on AI transparency and accountability

Industry Standards:

• ISO/IEC 23053:2022 - Framework for AI systems using ML
• NIST AI Risk Management Framework
• IEEE standards for algorithmic bias assessment

Frequently Asked Questions

How long should AI vendor due diligence take compared to standard IT vendors?

Expect 4-6 additional weeks for AI-specific assessments. Model explainability reviews and bias testing cannot be rushed without missing critical risks.

What's the minimum documentation required from AI vendors before contract signing?

Require complete training data documentation, model cards detailing capabilities and limitations, bias testing results, and architectural diagrams showing data flows. Missing any element should halt procurement.

Should we treat embedded AI features in traditional software differently?

Yes. Any vendor using AI for decision-making, recommendations, or processing customer data needs the enhanced diligence framework, even if AI isn't their primary service.

How often should AI vendor assessments be refreshed?

Annually at minimum, but high-risk vendors using continuously learning models need quarterly reviews. Model updates can introduce new biases or vulnerabilities between assessments.

What skills does a TPRM team need for AI vendor assessment?

Add data science expertise through hiring or partnerships. Traditional security and compliance teams often miss model-specific risks without ML knowledge. Consider training existing staff or engaging specialized consultants.

Can standard GRC platforms handle AI vendor monitoring?

Most lack AI-specific capabilities. You'll need additional tooling for model performance tracking, bias detection, and explainability monitoring. Some vendors now offer AI governance modules addressing these gaps.

What contractual terms are essential for AI vendors?

Include rights to audit training data, requirements for bias remediation, explainability guarantees, and specific liability allocation for AI-driven decisions. Standard limitation of liability clauses often exclude AI risks.

How do you assess vendors who claim proprietary model architectures?

Require third-party audits with detailed reports. If vendors refuse transparency, document the elevated risk and require additional controls like output monitoring and regular bias testing.