Artificial intelligence has quickly become a standard feature in vendor products and services. In many cases, this is driven less by necessity and more by pressure to keep up with competitors. As a result, “AI-powered” has turned into a marketing label rather than a clear technical capability. For organizations responsible for third-party risk, this creates a new problem: AI now has to be vetted as part of vendor due diligence, often without clear answers from the vendors themselves.
At a high level, AI does not change the purpose of vendor risk management. The goal remains the same: understand what the vendor is doing, what data is involved, what could go wrong, and how those risks are controlled. What AI changes is the complexity and opacity of the answers. Many vendors cannot clearly explain how their AI works, where it is hosted, or what happens when it produces incorrect or harmful outputs. That lack of clarity is itself a risk.
In Assessing AI Vendors You Should Understand;
Where Does The Data Go
One of the first concerns in assessing AI vendors is data handling. When a vendor introduces AI, the immediate question becomes where customer data goes. Some vendors rely on third-party or consumer-grade models, which may reuse inputs for training. Others cannot clearly state whether data is retained, anonymized, or segregated between customers. In vendor risk management, vague answers here should be treated as a red flag. If a vendor cannot explain whether your data is used for training, how long it is stored, or who has access to it, then the organization cannot meaningfully assess confidentiality or compliance risk.
Where The AI Model is Hosted is Important.
Another recurring issue is infrastructure transparency. Vendors frequently advertise AI features but struggle to explain whether the model runs on their own infrastructure, a public cloud service, or an external provider. This matters because hosting decisions affect jurisdiction, regulatory exposure, resilience, and incident response. If an AI system fails or behaves unpredictably, organizations need to know what controls exist to stop, roll back, or override it. “Trust us” is not a control, and the inability to describe fallback or rollback mechanisms indicates immature risk management.
What Human Oversight is in Place
Human oversight is another baseline expectation that becomes critical when AI is involved. For low-impact use cases, automation may be acceptable. For anything that influences decisions in regulated or high-risk environments, there must be a human in the loop. The most defensible model is one where AI supports decision-making by flagging, scoring, or summarizing, while a human remains accountable for the final action. Vendors that position AI as fully autonomous without human review are effectively asking customers to accept unbounded operational and compliance risk.
Ensuring There is Evidence of AI Governance
Risk management frameworks also matter. Mature vendors can explain how they assess, test, and govern their AI models before and after deployment. This includes how models are evaluated for bias, explainability, predictability, and misuse. Frameworks such as the NIST AI Risk Management Framework or standards aligned with ISO initiatives give structure to these conversations, but the real test is whether the vendor can show evidence of applying them. Policies without enforcement, testing, or documentation add little value.
Saying It’s Secure Isn’t the Same as Testing It
Security testing is another area where reality often falls short of claims. Many vendors say they have considered prompt injection and malicious inputs, but few can describe how those risks were tested or mitigated. Some organizations have started quietly introducing prompt-injection scenarios into vendor assessments to see whether vendors detect or acknowledge the issue. The results are often revealing. While it is unrealistic to expect security teams to “fix” fundamental weaknesses in large language models, it is reasonable to expect vendors to demonstrate awareness, testing, and guardrails appropriate to their risk profile.
Ask the Right Questions to Understand AI Risks
AI risks become clear when the right questions are asked.From a vendor risk perspective, AI does not require an entirely new playbook. It requires deeper skepticism and better questions. Where does the data go? How is it separated between customers? Is it used for training? Who has access? What happens when outputs are wrong? Is there a human override? Has the system been tested against abuse? Are audit logs available? Can the vendor show certifications, assessments, or external reviews that support their claims?
The uncomfortable truth is that many vendors cannot answer these questions today. Some rushed AI features into production without governance, security review, or clear ownership. Others rely on third-party platforms they do not fully understand. For organizations managing third-party risk, this means assuming less and verifying more.
Final Thoughts
AI in vendor risk management is not about blocking innovation. It is about preventing organizations from inheriting unmanaged risk through their supply chain. As AI becomes embedded in more vendor offerings, the ability to assess it clearly, critically, and consistently will separate mature risk programs from reactive ones. The baseline is: if a vendor cannot explain how their AI handles data, manages risk, and maintains human accountability, the risk decision should be treated accordingly.