Buying AI Tools? How to Cut Through Buzzwords

CISO Global AI Division

Buying AI Tools? How to Cut Through Buzzwords

The AI label is slapped on just about everything now. It’s the security equivalent of a juice cleanse. Sounds impressive, but often not backed by much substance.

So how do you separate signal from noise?

If you’re evaluating AI tools, chances are you’re already being sold to. Hard. Every product now claims to use AI whether it’s machine learning, deep learning, or just a shell script with a fancy name.

So how do you cut through the noise?

Start by asking: what kind of AI is this? Is it statistical anomaly detection? Supervised learning? Transformer-based summarization? Or just rule-based workflows wrapped in a chatbot? Just thresholds and heuristics rebranded? Ask for evidence: how was the model trained? What data? How often is it updated? What are its known blind spots?

Ask to see the data. What was the model trained on? How recent is that training? What assumptions does it make about your environment? How does it handle edge cases?

If the vendor can’t answer clearly (or worse, doesn’t want to) run.

Ask about transparency. Can you see why the model made the decision it did? Can you override it? Is there human review?

Ask about governance. What happens when it gets something wrong? Is there a feedback loop?

And always, always test it. Don’t just take the demo at face value. Run your own scenarios. See where it breaks. Then decide if it’s worth the price and the risk.

AI doesn’t make bad products good. It makes good products better. Don’t be seduced by buzzwords.

Buzzwords can’t protect you from liability. If you buy something because it sounds smart but can’t perform, that’s on you.

Test it. Don’t take demos at face value. Run it against real past incidents. See what it catches, what it misses, and how much work it actually saves. The burden of proof is on the vendor.

So tune your filters. Buy for outcomes, not acronyms.

Odds are the word “AI” is stamped somewhere on the front of every product you’re seeing. It’s a selling point, a checkbox, and a way to suggest innovation even if the underlying tech hasn’t really changed in years. That’s the problem. The industry has inflated the definition of AI so broadly that it now covers everything from basic scripting to advanced statistical models. And if you’re not careful, you’ll buy something that calls itself AI but delivers nothing new.

Before you spend a dollar, start by asking: what does this tool actually do, and what would it look like without the AI label? If the vendor can’t describe the functionality in terms you understand without leaning on phrases like “machine learning,” “predictive insight,” or “autonomous response” that’s a red flag. A good product should make sense without the marketing layer. You’re not buying AI; you’re buying outcomes.

A common trick is to repackage automation as intelligence. If a tool runs a workflow when certain conditions are met, that’s great. But it’s not AI. That’s logic. That’s rules. If the system classifies events based on thresholds or known signatures, it’s doing something useful, but again don’t confuse it with learning. AI, at a minimum, should be able to adapt, infer, or offer something you didn’t explicitly tell it how to do.

Ask the vendor what data their models were trained on. Was it open-source threat intel? Proprietary customer environments? Synthetic examples? Do they fine-tune per deployment, or is it one-size-fits-all? A lot of the time, you’ll find that what’s being called AI is just a static scoring system with no contextual awareness. And when models are trained on generic data sets that don’t reflect your environment, their value is limited. You’re not getting insight, you’re getting guesswork.

Then there’s generative AI. A lot of tools are now embedding chat-style interfaces and claiming it’s revolutionary. These models are trained to sound convincing, not to be correct. So if your incident summary or alert explanation is coming from a generative model, verify it. And don’t assume that a smooth paragraph means the system actually understands what happened. It doesn’t.

It’s also worth noting that AI features often rely on mature environments to function well. If your asset inventory is incomplete, if your detection coverage is spotty, or if your alert volumes are high and noisy, the AI won’t magically compensate. In fact, it might make things worse by prioritizing false signals or overlooking subtle indicators. The output is only as good as the inputs.

Another key question: does the AI explain its decisions? If the product flags something as suspicious, does it show you why? Can you trace the logic, understand the scoring, and replicate the conclusion manually if needed? If not, you’re handing over trust to a black box. That might be fine for low-risk tasks, but when it comes to incident response, risk scoring, or automated actions, it’s dangerous. Transparency matters.

And finally: don’t let FOMO drive the purchase. Just because your competitor bought an “AI-powered” security platform doesn’t mean it’s working for them. There’s enormous pressure in the industry to be seen as forward-thinking, but pretending you’re doing AI is not the same as building capability. Real security gains come from clarity, process, and sound engineering and not from buzzwords.

Cutting through the hype takes discipline. Treat every AI claim like you would a magic trick: assume it’s marketing until proven otherwise. Dig into how the system learns, what decisions it can make independently, and how it adapts to your environment. Look for tools that enhance your human analysts, not ones that promise to replace them. And remember that a well-tuned rule is often more reliable than a poorly understood model. At the end of the day, “AI” on the brochure is just that, words. Your job is to figure out what’s behind them.