Why Is AI Vendor Due Diligence Critical?
Companies increasingly rely on third-party AI vendors for machine learning infrastructure, pre-trained models like GPT-4, Claude, or Gemini, specialized AI services for vision, speech, or analytics, and AI-powered business applications. While these partnerships accelerate AI adoption and reduce development costs, they also create risks including security vulnerabilities in vendor systems exposing your data, compliance failures creating regulatory liability, performance issues affecting your operations, and vendor lock-in limiting flexibility.
Recent incidents involving AI vendor security breaches, model failures, and compliance violations demonstrate that third-party AI risk can’t be ignored. Companies remain liable for vendor failures in many contexts—GDPR holds data controllers responsible for processor violations, customers blame you for vendor service failures, and regulators may not accept “the vendor did it” as a defense.
Effective AI vendor due diligence protects against these risks while enabling beneficial partnerships.
Pre-Engagement Due Diligence
Vendor Financial Stability
Assess vendor financial health to avoid disruption from vendor failure. Review financial statements if available, funding history and investor quality, customer count and revenue stability, and burn rate versus runway for startups.
AI startups may offer cutting-edge technology but carry higher failure risk than established vendors.
Market Position and References
Evaluate vendor reputation through customer references and case studies, analyst reports and market position, press coverage and incident history, and regulatory compliance track record.
Speak with current customers about their experiences.
Technical Capabilities Assessment
Verify that vendor capabilities match your needs including model performance on relevant benchmarks, scalability to your anticipated usage, integration capabilities with your systems, and API reliability and uptime history.
Request proof-of-concept trials before committing.
Security and Privacy Due Diligence
Security Certifications and Audits
Request evidence of security programs including SOC 2 Type II reports, ISO 27001 certification, penetration testing results, and vulnerability management processes.
Verify certifications are current and cover relevant service components.
Data Handling Practices
Understand how vendors handle your data including where data is stored and processed, who has access to data, how data is encrypted and protected, and data retention and deletion practices.
For AI services, clarify whether your data trains vendor models or remains isolated.
Privacy Compliance
Verify vendor compliance with applicable privacy laws including GDPR for European data processing, CCPA for California resident data, sector-specific regulations like HIPAA, and vendor’s role (processor, sub-processor, service provider).
Obtain appropriate data processing agreements.
Incident Response and Breach History
Review vendor’s incident response capabilities and history including breach notification procedures and timelines, past security incidents and resolutions, cyber insurance coverage, and business continuity and disaster recovery plans.
Past incidents aren’t disqualifying but should be understood.
Compliance and Regulatory Due Diligence
AI-Specific Regulatory Compliance
Assess vendor compliance with emerging AI regulations including EU AI Act conformity for high-risk systems, state AI bias and transparency requirements, sector-specific AI regulations, and documentation and audit capabilities.
Export Control Compliance
For vendors providing access to advanced AI, verify export control compliance including technology classification, customer screening procedures, restrictions on use in certain countries, and compliance with Entity List requirements.
Bias Testing and Fairness
If using AI for decisions affecting individuals, review vendor’s bias testing including fairness metrics and evaluation methodologies, demographic performance disparities, mitigation strategies, and ongoing monitoring commitments.
Intellectual Property Due Diligence
IP Ownership and Rights
Clarify IP ownership for vendor-developed technology, your inputs and data, outputs and results, and custom models or configurations.
Ensure you receive necessary licenses for your use.
Training Data Provenance
Understand training data sources and rights including data licenses and permissions, copyright compliance for training data, representation and diversity, and potential IP infringement risks.
Vendors should warrant appropriate training data rights.
Open Source Compliance
Review vendor’s use of open source including open source component inventory, license compliance verification, and absence of GPL/AGPL in proprietary services.
Open source violations in vendor code may affect you.
Contractual Protections
Service Level Agreements
Negotiate SLAs defining performance standards including uptime and availability guarantees, response time commitments, accuracy or quality metrics, and support response times.
SLAs should include remedies for failures like service credits.
Data Protection Terms
Ensure contracts include robust data protection provisions covering processing only per instructions, data security obligations, sub-processor management, assistance with data subject requests, and data return or deletion at termination.
Liability and Indemnification
Address liability allocation through caps on vendor liability, indemnification for IP infringement, data breach notification obligations, and insurance requirements.
Balance vendor limitations with adequate protection.
Termination and Transition
Plan for relationship end through reasonable termination rights, data portability and export, transition assistance periods, and prohibition on vendor data retention.
Operational Risk Management
Vendor Lock-In Mitigation
Avoid excessive dependency through multi-vendor strategies where feasible, use of standard APIs enabling switching, data portability requirements, and maintaining internal expertise.
Performance Monitoring
Implement ongoing vendor performance tracking including SLA compliance monitoring, quality and accuracy metrics, security incident tracking, and regular vendor reviews.
Business Continuity Planning
Develop contingencies for vendor failure including backup vendor relationships, failover capabilities, and internal capability development for critical functions.
Vendor Relationship Management
Regular Vendor Assessments
Conduct periodic reviews of vendor relationships including annual security questionnaires, compliance re-certification, performance metric reviews, and contract renegotiation opportunities.
Vendor Tiering
Categorize vendors by criticality and risk including tier 1 for critical, high-risk vendors requiring extensive diligence, tier 2 for important vendors with moderate risk, and tier 3 for low-risk, easily replaceable vendors.
Focus resources on higher-tier vendors.
Vendor Communication Channels
Establish clear communication for issue escalation, security incident notification, service changes or updates, and contract management.
Industry-Specific Considerations
Healthcare AI Vendors
Healthcare organizations must verify HIPAA compliance, Business Associate Agreements, FDA clearance if applicable, and clinical validation evidence.
Financial Services AI Vendors
Financial institutions should assess regulatory compliance with banking regulators, model risk management, algorithmic trading compliance, and fair lending requirements.
Government AI Procurement
Government agencies face additional requirements including FedRAMP authorization for cloud services, Section 508 accessibility compliance, and FAR/DFARS contractual requirements.
Red Flags in AI Vendor Evaluation
Watch for warning signs including refusal to provide security documentation, vague or unsubstantiated performance claims, unclear data handling practices, no references from comparable customers, and resistance to reasonable contract terms.
Significant red flags may warrant disqualification.
Documentation and Governance
Vendor Due Diligence Files
Maintain organized documentation including due diligence questionnaires and responses, security assessments and certifications, contracts and amendments, performance and compliance records, and incident and issue tracking.
Board and Executive Reporting
Report material vendor relationships and risks to leadership including critical vendor dependencies, significant risk exposures, major incidents or failures, and mitigation strategies.
Conclusion: Proactive Third-Party Risk Management
Third-party AI vendors create both opportunities and risks. Effective due diligence programs balance enabling innovation with protecting against security, compliance, performance, and business continuity risks.
Companies should conduct thorough pre-engagement diligence, negotiate protective contract terms, monitor vendor performance continuously, and plan for vendor failures or transitions.
Contact Rock LAW PLLC for AI Vendor Due Diligence
At Rock LAW PLLC, we help companies evaluate and contract with AI vendors.
We assist with:
- Vendor due diligence frameworks and questionnaires
- AI vendor contract negotiation
- Data processing agreement review
- Security and compliance assessment
- IP and licensing evaluation
- Vendor relationship governance
Contact us to develop robust AI vendor management practices.
Related Articles:
- Data Processing Agreements for AI Companies
- Warranties in AI Software Contracts
- Preparing for M&A Due Diligence
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