AI Vendor Security and Safety Assessment Guide

What It Offers ?

This guide provides a ready-to-use question set to help assess AI vendors across a wide range of dimensions—security, privacy, ethics, robustness, data handling, explainability, and more. It’s designed to plug directly into your existing Third-Party Risk Management (TPRM) workflows and can be especially valuable before signing contracts or onboarding new tools involving machine learning or generative AI.

Structured like a due diligence playbook, it emphasizes real-world applicability and operational control. The document balances policy goals with actionable details—something many AI “principle documents” tend to miss.

What Works Well

1. Holistic, Modular Design

From business impact to security architecture, from explainability to nth-party risk, the guide reflects an understanding that AI risk is multidimensional. It’s clear, logically sequenced, and easily adaptable to different organizational contexts.

2. Concrete, Decision-Oriented Questions

This isn’t a vague checklist. Many questions are phrased to elicit specific operational practices—like whether the vendor supports BYOK (Bring Your Own Key), enables client-configurable retention periods, or provides model scorecards. That focus helps buyers make informed, traceable choices.

3. Risk Prioritization Advice

The introduction includes a call to prioritize based on risk level, reminding readers that not every system deserves the same scrutiny. That pragmatic framing keeps the process lean without sacrificing thoroughness.

4. Integration-Ready with Existing Frameworks

The guide aligns neatly with industry frameworks like ISO/IEC 42001, NIST AI RMF, EU AI Act, and even more traditional compliance approaches like SOC 2 or ISO 27001—allowing smooth integration into current TPRM systems.

🚩 Gaps and Caveats

No built-in risk rating or scoring rubric: While the guide offers excellent questions, it stops short of providing a structured scoring system. This might require extra work to align it with procurement or audit dashboards.
Vendor burden vs. buyer capacity: Smaller AI vendors may struggle to answer every question at the depth expected here, and not every procurement team will have the internal bandwidth to interpret complex responses without expert support.
Limited examples or model answers: Some illustrative answers or common red flags would be helpful, especially for readers less familiar with AI-specific risks.

💡 Why it matters?

As AI systems become embedded into enterprise operations—from HR tools to customer support and content moderation—security and safety due diligence cannot stop at generic IT questions. This guide fills that gap, giving procurement teams a concrete foundation to interrogate model design, data usage, bias mitigation, adversarial robustness, and more. It’s especially relevant for regulated sectors, critical infrastructure, and organizations bound by data residency or AI transparency rules.

It also helps bridge the policy-to-practice gap in AI governance, where high-level principles often lack real procurement hooks. Here, the link between responsible AI and vendor contracts is made explicit.

Best For:

Procurement teams evaluating generative AI, LLMs, or automated decision tools
AI governance officers drafting internal vendor evaluation criteria
Legal, risk, or security teams negotiating AI contracts or reviewing SLAs
Auditors and internal controls leads needing structured AI vendor assessment frameworks

Author Note:

Dennis Ah King is a respected voice in AI risk and governance, with a background in strategic security risk management. His work reflects a hands-on, operational perspective—translating policy language into procurement and implementation controls. More on his work: Dennis Ah King on LinkedIn