AI adoption is accelerating, and with it comes a growing dependency on external AI vendors. Foundation model providers, AI-powered analytics platforms, intelligent automation tools, computer vision systems — the average enterprise now works with multiple AI suppliers across different business functions. This creates a new category of third-party risk that traditional vendor management frameworks were not designed to handle.
The challenge is distinct from conventional IT procurement. AI systems can produce unpredictable outputs, embed biases from training data, process sensitive information in opaque ways, and evolve their behaviour through model updates without explicit notification. Artificial intelligence vendor risk therefore requires dedicated assessment criteria, specialised contractual provisions, and ongoing monitoring that goes well beyond standard service-level agreements.
Organisations that treat AI vendor management as an afterthought expose themselves to regulatory penalties, reputational harm, and operational failures that could have been prevented with proper due diligence.
Why AI vendors need different due diligence
Traditional vendor assessments focus on financial stability, information security certifications, and service uptime. These remain relevant for AI suppliers, but they are insufficient. AI-specific due diligence must evaluate model transparency, training data provenance, bias testing practices, data handling and retention policies, incident response for AI failures, and the vendor’s approach to responsible AI.
67%
of organisations report they lack a formal process for assessing AI-specific risks in their vendor relationships — relying instead on generic IT procurement checklists
Source : ISACA AI Governance Survey, 2025
A robust AI vendor assessment framework should cover several dimensions that generic procurement processes miss. These include model explainability (can the vendor explain how outputs are generated?), data governance (where does training data come from and how is your data used?), performance monitoring (how does the vendor track model drift and accuracy degradation?), and ethical AI practices (what bias testing and fairness audits does the vendor conduct?).
Organisations beginning to formalise their approach should start with an AI readiness assessment to understand their current capabilities and gaps in managing AI vendor relationships effectively.
Due diligence framework for AI suppliers
A structured due diligence process for AI vendors should proceed in three phases: pre-selection screening, deep assessment, and ongoing validation.
Pre-selection screening filters potential vendors against minimum requirements: relevant security certifications (ISO 27001, SOC 2), data processing locations compatible with your regulatory obligations, basic transparency about model architecture and training approach, and evidence of responsible AI practices. This phase eliminates vendors that cannot meet baseline expectations before you invest time in deep evaluation.
Deep assessment examines the vendor’s AI practices in detail. Request documentation on model training data sources and curation processes, bias testing methodology and results, model performance benchmarks on tasks relevant to your use case, data retention and deletion policies, incident response procedures for AI-specific failures (hallucinations, bias incidents, data leaks), and sub-processor relationships where your data may be shared with other AI providers.
AI vendor due diligence should not operate in isolation. It must connect to your broader AI governance framework — ensuring that vendor assessment criteria align with your organisation’s risk appetite, ethical principles, and regulatory obligations.
Ongoing validation is where most organisations fall short. AI systems change — models are retrained, fine-tuned, or replaced entirely. A vendor that passed assessment six months ago may operate quite differently today. Establish regular review cycles and require vendors to notify you of material changes to their AI systems.
Contractual requirements for AI vendors
Standard vendor contracts rarely address AI-specific risks adequately. When procuring AI systems, negotiate provisions that protect your organisation across several critical areas.
Transparency obligations. The vendor must disclose the general approach to model training, significant model updates or replacements, known limitations and failure modes, and any use of your data for model training or improvement. Without these clauses, you may discover too late that your proprietary data has been used to train a model that serves your competitors.
Performance and accuracy commitments. Define measurable performance thresholds relevant to your use case — accuracy rates, false positive/negative limits, response time requirements. Include provisions for what happens when performance degrades below agreed thresholds, including remediation timelines and exit rights.
Data handling. Specify precisely how your data is processed, stored, and deleted. Address whether your data is used to train or improve the vendor’s models, where data is processed geographically, how data is segregated from other customers’ data, and what happens to your data upon contract termination. For organisations managing personal data, these provisions must align with GDPR requirements for AI systems.
Audit rights. Reserve the right to audit the vendor’s AI practices — or to engage an independent third party to do so. This is increasingly important under the EU AI Act and should cover model performance, data handling, bias testing, and security practices.
EU AI Act supply chain obligations
The EU AI Act introduces specific obligations that directly affect AI vendor management. Organisations deploying AI systems classified as high-risk bear compliance responsibilities that extend across the supply chain — even when the AI system is provided by an external vendor.
€15M+
maximum fine for non-compliance with EU AI Act provider obligations — with deployers also liable if they fail to conduct adequate vendor due diligence
Source : EU AI Act, Article 99
Deployer obligations under the Act include ensuring that AI systems are used in accordance with their intended purpose, maintaining human oversight as specified by the provider, monitoring the system’s performance and reporting serious incidents, and conducting a fundamental rights impact assessment for certain high-risk uses. Critically, these obligations cannot be fully delegated to the vendor — the deploying organisation remains accountable.
Supply chain transparency is a core principle. Providers of high-risk AI systems must supply deployers with sufficient documentation to understand the system’s capabilities, limitations, and appropriate use. As a deployer, you must verify that this documentation exists and is adequate. If your vendor cannot provide it, that is a significant compliance red flag.
For organisations subject to AI compliance requirements more broadly, vendor management becomes a central pillar of the compliance programme. The Act’s Article 4 training obligations also mean your procurement and compliance teams need sufficient AI literacy to evaluate vendor claims critically.
Ongoing monitoring of AI suppliers
Assessment at procurement stage is necessary but not sufficient. AI systems require continuous monitoring because their behaviour can shift over time through model updates, data drift, and changing usage patterns.
Performance monitoring. Track AI system outputs against agreed benchmarks. Establish automated alerting for accuracy degradation, unexpected output patterns, or increased error rates. Require vendors to provide dashboards or API access for monitoring model performance in your specific environment.
Incident tracking. Maintain a log of AI-related incidents — hallucinations, biased outputs, data handling issues, availability failures. Analyse trends over time. A single incident may be within tolerance; a pattern demands escalation and vendor review.
Regulatory change monitoring. AI regulation is evolving rapidly across jurisdictions. Monitor whether your vendors are adapting to new requirements — the UK’s approach to AI regulation, sector-specific rules in financial services and healthcare, and emerging standards like ISO 42001. A vendor that falls behind regulatory expectations becomes your compliance liability.
Build AI vendor monitoring into your existing AI risk assessment process rather than creating a parallel system. This ensures vendor risks are evaluated alongside internal AI risks using consistent criteria and escalation paths.
Periodic reassessment. Schedule formal vendor reviews at least annually — more frequently for high-risk AI systems. Re-examine the dimensions assessed during due diligence: has the vendor’s approach to bias testing changed? Have they updated their data handling practices? Have there been leadership or ownership changes that might affect their AI strategy?
Building internal capability for AI vendor management
Effective AI vendor management requires people who understand both procurement discipline and AI-specific risks. This is rarely a skill set that exists naturally within procurement or compliance teams.
Upskill procurement teams. Procurement professionals need to understand the fundamentals of how AI systems work, what questions to ask vendors, how to evaluate technical documentation, and what red flags to watch for. Generic awareness is not enough — targeted AI training for employees in procurement, legal, and compliance roles is essential.
Create cross-functional review teams. AI vendor assessments benefit from multiple perspectives: procurement brings commercial discipline, IT security evaluates infrastructure risks, data protection officers assess privacy implications, and business stakeholders validate fitness for purpose. No single function has the complete picture.
Document and standardise. Create AI vendor assessment templates, standard contractual clauses, and monitoring checklists that can be reused across the organisation. This prevents each business unit from reinventing the process and ensures consistent standards.
From procurement to partnership
AI vendor management is not about creating barriers to adoption — it is about enabling safe, responsible, and effective use of AI across the organisation. Vendors that welcome scrutiny, provide transparency, and invest in responsible AI practices are the ones worth building long-term partnerships with. Those that resist due diligence or cannot articulate their approach to bias, data handling, and compliance are risks best avoided.
Brain provides AI training built for the teams managing AI vendor relationships — procurement, compliance, legal, and risk professionals. Role-specific modules covering vendor assessment frameworks, contractual requirements, EU AI Act obligations, and ongoing monitoring practices. Practical scenarios drawn from real vendor management challenges, not abstract theory.
Related articles
AI Governance + Compliance: Unified Framework (GDPR, AI Act, NIST)
Integrate AI governance with GDPR and EU AI Act compliance in one framework. NIST AI RMF mapping, audit-ready checklist, real implementation playbook.
AI Governance Framework: 7-Step Checklist + ISO 42001 Template
Build your AI governance framework in 7 steps. Free checklist, ISO 42001 alignment, EU AI Act mapping, and the 4 governance principles that matter.
AI Regulation News 2026: Global Updates You Need Now
Stay current on the EU AI Act, US state-level AI laws, and UK regulatory changes. Concise global roundup for business leaders in 2026.