AI Vendor Security Questions to Ask Before You Buy

So, you’re thinking about bringing on an AI partner.

It’s a huge decision. And let’s be honest, figuring out whether an AI vendor is actually secure can feel overwhelming.

In this video, we’re going to build a smarter framework for evaluating AI vendors. One that cuts through the noise and focuses on what truly matters.

We’re not just talking about another piece of software here.

When you integrate AI, you may be giving that vendor deep access into the heart of your business. It can touch customer data, internal workflows, support systems, knowledge bases, and operational tools.

The stakes are high, which means your security review has to be airtight.

So, how do you begin a thorough review without getting lost in a huge security checklist?

The key is to simplify.

Instead of getting stuck in hundreds of small details, you can build a strong review around three fundamental questions.

Here’s the plan for this explainer.

We’ll start with those three core questions. Then, we’ll walk through how to establish foundational trust, map your data flows, deal with AI-specific risks, and finally, validate every claim the vendor makes.

Let’s start with section one: the three core risk questions.

Think of this as the “why” behind your entire review.

These questions define the core risks you need to understand before signing a contract with an AI vendor.

Here’s what it comes down to.

First, follow the data.

Where is your data going? Where is it stored? Who can access it? And does that include any third-party companies the vendor works with?

Second, understand what the system can actually do.

Can it make changes in your other systems? Can it trigger workflows, update records, create tickets, process refunds, or perform actions on behalf of users?

And what guardrails are in place to stop it from doing something it should not do?

Third, look at accountability.

If something goes wrong, is there a clear audit trail? Can you prove exactly what happened, who triggered it, what data was used, and what action was taken?

That brings us to section two: establishing foundational trust.

Now that we have our core questions, the first step is to get a baseline on the vendor.

This is not about taking their word for it. It is about asking for objective third-party proof.

Before you even get into the AI-specific details, ask this question:

Do they have recognized security certifications that prove they have a mature security program?

If they cannot provide those, that is a major red flag.

Do not just ask, “Are you secure?”

That is not enough.

Ask for specific evidence.

Ask for their SOC 2 Type II report. This gives you a deeper audit of their security controls over time.

Ask for their ISO 27001 certification. This is a globally recognized standard for information security management.

And here is an important detail: make sure the actual product you are buying is included in the scope of that audit.

It is a small check, but it makes a big difference.

Now, let’s move to section three: mapping data and access.

This is where you get practical and start tracking your data.

You are trying to answer the first core question: where does your data go, and who can access it?

Think of this as a four-step process.

First, understand every system the AI will connect to.

Will it connect to your CRM, helpdesk, knowledge base, website, inbox, calendar, payment system, or internal database?

Second, get clear on the vendor’s data policies.

How long do they keep your data? Is it encrypted in transit and at rest? Is customer data used for model training? Can you delete it when needed?

Third, verify their access controls.

Single sign-on and multi-factor authentication should not be optional. They should be standard.

Fourth, make sure administrator actions are logged and fully auditable.

No exceptions.

Now, let’s move to section four: addressing unique AI risks.

We have covered the security fundamentals, and they are important. But AI is not a standard software system.

It brings a new category of risks that traditional security reviews may miss.

First, let’s talk about hallucinations.

A hallucination happens when the AI makes something up.

Imagine an AI support bot confidently giving a customer completely wrong information about your product, policy, or pricing.

That is not just a technical issue. It is a direct threat to customer trust and your brand.

Next, there is prompt injection.

Think of this as tricking the AI.

A bad actor can write a prompt that convinces the model to ignore its safety rules, reveal sensitive information, or perform actions it should not perform.

This is a new kind of vulnerability that is specific to language-based systems.

So, the question for your vendor is:

How are you managing these AI-specific risks?

Can the model be grounded in approved knowledge sources?

In other words, can you force it to answer only from trusted company content?

What happens when the model’s confidence is low?

Does it guess, or does it escalate to a human?

These are the details that separate a reliable AI platform from a risky one.

Now, let’s move to the final section: validating every claim.

This might be the most important part of the entire process.

You have asked the right questions. Now you need to make sure the answers are real.

This is the core mindset shift.

A simple “yes” on a security questionnaire is not an answer. It is only the start of a conversation.

A “yes” means very little without proof.

So, this becomes your rule:

If a vendor says they have role-based access controls, ask them to show you a screenshot or send the policy document.

If they say they log all administrator actions, ask to see a sample of those logs.

If they say data is encrypted, ask where, how, and under what standard.

Every answer should be backed by evidence.

Do not just trust. Verify.

At the end of the day, a real security assessment is an investigation.

It is a structured, evidence-based process designed to genuinely understand and manage risk. It is not just a box-checking exercise.

By focusing on the core questions, demanding baseline proof, mapping your data, reviewing AI-specific risks, and validating every claim with evidence, you move from uncertainty to real confidence.

So, the question is:

How will you apply this framework to make sure your next AI partner is a true asset and not a liability?