Every security vendor has an AI story now. Some of them are real. Many aren't.
The challenge for security leaders is that the people doing the selling know more about the marketing than the technology, and the people doing the buying often lack the technical depth to probe the claims. The result is a lot of expensive tools that underdeliver.
Here's a practical framework for cutting through it.
Start With the Claim
The first step is identifying exactly what the vendor is claiming AI does in their product. Be specific. "AI-powered" is not a claim. "Our ML model detects novel malware variants not in known signature databases by analyzing behavioral patterns in PE file execution" is a claim.
Press vendors to be specific:
- What problem does the AI solve, specifically?
- What does the AI do that a non-AI approach (rules, signatures, heuristics) cannot?
- Where does the AI sit in the detection or response workflow?
If they can't answer these questions specifically, the AI in their product is probably a marketing feature, not an operational one.
Ask About the Training Data
Machine learning models are only as good as the data they were trained on. The training data determines what the model knows, what it can generalize from, and where it will fail.
Questions to ask:
- What data was the model trained on? How recent is it?
- Was it trained on your industry's data or general data?
- How often is the model retrained?
- What happens when the model encounters data outside its training distribution?
A vendor who can't answer training data questions either doesn't know (a problem) or doesn't want to tell you (also a problem).
Understand the False Positive Rate
Every detection system generates false positives. The question is how many, under what conditions, and how that impacts your team's workload. AI-based detections are not inherently better or worse than rule-based ones, but vendors often imply they are.
Ask for:
- False positive rates in customer environments similar to yours
- How alert volume changed after deployment
- What tuning is required and who does it
A vendor who claims near-zero false positives either hasn't been deployed at scale or is cherry-picking numbers.
Test It on Your Data
The strongest signal is a proof of concept on your actual environment. Generic demos on vendor-supplied data are not meaningful. Your environment has different baselines, different noise, different attack patterns.
Before any significant purchase, insist on:
- A POC using your data (or realistic synthetic data matching your environment)
- Clear success criteria defined in advance
- Access to raw detection output, not just a dashboard
If the vendor won't run a POC, ask why.
Look for Explainability
A model that tells you something is malicious without telling you why is a black box. In a security context, black boxes are dangerous. They fail silently, they can't be tuned intelligently, and analysts can't use them to build understanding.
Ask:
- Can the model explain why it flagged a specific alert?
- What features drove the detection?
- Can analysts access the underlying evidence, not just the verdict?
Explainability isn't just a nice-to-have. It's what separates a useful detection tool from an expensive alert generator.
Don't Buy AI to Buy AI
The most common mistake is acquiring AI capabilities because AI is expected, not because there's a specific problem it solves better than alternatives.
Before any AI security purchase, define:
- The specific problem you're trying to solve
- What you're doing now and why it's insufficient
- What success looks like in measurable terms
- What the non-AI alternative would cost
If the AI solution doesn't clearly outperform the alternative on your specific problem, it probably doesn't justify the premium.
GTK Cyber's executive AI training is built around this kind of rigorous evaluation framework, not vendor presentations, but the technical literacy to ask the right questions and interpret the answers. If you're making AI security decisions for your organization, it's worth a day to develop that foundation.
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