Maturing Your AI Vendor Security Assessment Process

It's hard to find a SaaS application these days that doesn’t include some form of AI. A recent McKinsey report found that 55% of organizations had adopted AI in at least one function, and that number is rising steadily. Whether it's summarizing emails, recommending code, or interpreting natural language queries, AI has become the quiet engine under many digital hoods. But with great functionality comes great responsibility, especially when that AI is powered by a third party.

Imagine your customer data, sensitive internal documents, or proprietary algorithms being processed by a vendor's AI model. That's not just a data privacy headache—it's a potential PR disaster or compliance violation waiting to happen. In fact, IBM's 2024 Cost of a Data Breach report noted that breaches involving third-party software cost companies an average of $4.9 million. The stakes are high, which is why organizations must evolve their AI vendor assessment processes beyond just ticking boxes.

Understanding AI risk: more than just data leaks

When assessing vendors, it helps to break down AI risk into three core categories:

1. Third-party data risks: What happens when you send your data to another company? Can you trust them to keep it encrypted, secure, and properly audited?

2. AI-specific risks: Is the model biased? Can its outputs be explained or audited? Could it leak intellectual property?

3. Line-of-business risks: How does the AI impact your business operation or reputation? What happens when it goes wrong?

Each of these areas deserves focused attention. Too often, teams concentrate only on data risks, missing more nuanced threats like bias or transparency failures.

Signs your assessment process is maturing 

1. Clear separation of risk types: Mature teams know how to distinguish between AI-specific and generic third-party risks. That means letting standard InfoSec processes handle encryption or log access, and freeing your AI risk specialists to zero in on hallucinations, model misuse, or data poisoning.

2. All output is treated as untrusted: Smart teams operate on a "zero-trust" philosophy when it comes to AI-generated content. For example, instead of assuming an LLM's code generation is safe, they test it, sandbox it, and validate it before use. Treating all output as potentially harmful isn't paranoid—it's professional.

3. Threat modeling failure cases: Instead of reacting to risks, mature organizations anticipate them. That means asking, "What happens if the model fails silently? If it's unavailable? If it spews toxic output on a public site?" Threat modeling isn't just for firewalls anymore.

Real-world examples: when AI goes sideways 

• Transparency gone wrong: A secure email gateway powered by AI suddenly allows phishing emails to slip through. Why? The AI logic is a black box. There's no way to trace why the behavior changed.

• Bias in code: A model outputs Python snippets favoring a specific library. But it's hallucinating methods from older versions. The app crashes and developers are left baffled.

• Vendor outage domino effect: Your app uses an LLM to screen for XSS payloads. One day, the vendor's model fails and your app starts ingesting malicious input. Game over.

These aren't hypotheticals. These are real scenarios organizations have already faced.

Prioritizing what's important 

Let's face it: most risk assessment teams don't have infinite time or headcount. A sign of maturity is knowing where to focus. Not every AI-infused app poses high risk. Teams that map data flow, request focused documentation, and evaluate only the riskiest use cases make smarter use of their resources.

One trick: build a question pool, but limit each vendor assessment to a handful of targeted questions. Ask only what can actually change your go/no-go decision. Everything else is noise.

The business angle: line -of-business risks are no joke 

Sometimes, the most dangerous risks aren't technical—they're reputational or operational, such as:

• A hiring app subtly biased against a protected class

• A chatbot that can't explain how it rejected a customer claim
• An OpenAI API update that starts interpreting brand names as violent threats

These are line-of-business risks. And while they're harder to pin down, mature teams don't ignore them. Instead, they:

• Define what's in scope
• Know when they don't have the business context to assess risk
• Train business leaders to ask the right AI questions

The path to a stronger AI vendor assessment process doesn't have to be overwhelming. Here are three starting points:

1. Categorize: Use the three buckets—data, AI-specific, and business risk—to guide every vendor evaluation.

2. Streamline: Prioritize high-risk applications, and reduce unnecessary questions to focus your efforts.

3. Review often: AI is evolving fast. Your assessment process should evolve just as fast.

AI isn't magic; it's just math at scale. But trusting someone else's math with your data and your reputation? That's a business decision—and a risky one. Maturity means approaching that risk with clarity, focus, and realism.
Start small, iterate often, and keep your eyes open. The AI tide is rising, but with the right processes in place, your boat won't just stay afloat—it'll lead the fleet.