# 1. AI-Powered Security Tools

Let's kick this off with the topic that's causing the most vendor-induced headaches in 2026.

Context: Every vendor now claims to be "AI-powered." Most aren't, in any meaningful sense. Some bolted a GPT wrapper onto their UI last quarter. Others have genuine ML pipelines they've been refining for years. Your job is to figure out which is which, and whether the AI actually solves a problem you have, or is just a sales narrative. These questions are designed to make a vendor either prove competence or visibly squirm.

12 Questions to Ask Any "AI-Powered" Security Vendor

1. "Specifically, which parts of your product use AI/ML, and which use traditional rules, signatures, or heuristics?"
Why: Forces them to separate marketing from engineering. A credible vendor will cleanly delineate (e.g., "ML for anomaly scoring; rules for known IOCs"). A weak one will hand-wave that "everything is AI-driven."
Good answer looks like: A clear architectural breakdown. Red flag: Vague "our platform is built on AI from the ground up" with no specifics.

2. "Are you using your own trained models, fine-tuned open-source models, or are you calling third-party APIs like OpenAI, Anthropic, or Google?"
Why: This has massive implications for data privacy, latency, cost, and lock-in. Many "AI" features are just GPT-4 API calls, which means your security data may be leaving your environment.
Good answer: Honest disclosure of the model stack and where data flows. Red flag: Evasion or "proprietary, can't disclose."

3. "What data is sent to the model at inference time, where does it go, and is any of it retained or used for training?"
Why: This is the privacy/compliance question. If your alerts, logs, or files are being shipped to a third-party LLM, your legal and compliance teams need to know yesterday.
Good answer: Specific data flow diagram, retention policy, opt-outs, regional hosting. Red flag: "Don't worry, it's secure."

4. "Can you walk me through a real detection your AI made that a traditional rule-based system would have missed, with the actual telemetry?"
Why: Separates demos from reality. Forces them to show evidence, not slides.
Good answer: A concrete case study with technical detail. Red flag: Generic anecdotes, "we can't share that for confidentiality."

5. "What's your false positive rate, and how is it measured? How does it compare to your non-AI baseline?"
Why: AI's #1 unsolved problem in security is FP volume. If they don't measure it, they're not serious. If they claim near-zero FPs, they're lying.
Good answer: Honest metrics, methodology, and trade-offs discussed. Red flag: "Our AI is so good we barely have false positives."

6. "How do you handle model drift, and how often are models retrained or updated?"
Why: Models decay. Threat landscapes shift. A vendor without a retraining cadence is shipping a stale product.
Good answer: Defined cadence (weekly/monthly), drift detection telemetry, customer-specific tuning. Red flag: Blank stares or "it's continuously learning."

7. "Is the model explainable? When it flags something, can my analyst see why?"
Why: Black-box AI alerts are unactionable. Your SOC needs to know whether to escalate or dismiss, and "the AI said so" doesn't fly with auditors, regulators, or insurers.
Good answer: Feature attribution, confidence scores, supporting evidence shown in UI. Red flag: "It's a deep neural network, so explainability is hard."

8. "How do you prevent prompt injection, model poisoning, or adversarial inputs against your AI?"
Why: If they're using LLMs anywhere user- or attacker-controlled data touches, this is now a real attack surface (see OWASP LLM Top 10). A vendor that hasn't thought about this is exposing you.
Good answer: Specific mitigations, red-teaming of their own models, input validation. Red flag: "That's not a realistic threat."

9. "If I turned off every AI feature tomorrow, what would I lose, and is the rest of your product still worth the price?"
Why: Tests whether the AI is a genuine differentiator or marketing veneer over a conventional product. Often, the "non-AI" core is what you'd actually be paying for.
Good answer: Honest articulation of AI as augmentation vs. core. Red flag: Panic, or "it's all integrated, can't be separated."

10. "Can your AI be tuned, suppressed, or overridden by my team, or is it a black box we have to trust?"
Why: Mature products give analysts agency. Immature ones force you to accept the model's judgment. Operationally, the latter creates frustrated SOC teams and ignored alerts.
Good answer: Tunable thresholds, allowlist/denylist, feedback loops that improve detection. Red flag: "You shouldn't need to tune it; the AI handles it."

11. "Show me your AI roadmap. What's shipping in the next 6 months, and what's still aspirational?"
Why: Distinguishes vendors with a strategy from vendors riding the hype cycle. Also reveals whether they're building durable capability or chasing trend headlines.
Good answer: Concrete features, dates, with acknowledged uncertainties. Red flag: Buzzword bingo, "agentic AI, autonomous SOC, self-healing."

12. "Who on your team owns AI/ML, and what's their background? Can I speak with them, not just sales engineering?"
Why: Real AI requires real ML engineering and data science talent. If their "AI team" is one contractor or a couple of repurposed devs, you'll know in 5 minutes of conversation.
Good answer: Named leader with relevant credentials, willing to take a technical call. Red flag: Sales deflection, "our whole engineering team works on it."

13. (Bonus) "What happens to your product, and my detections, if your LLM provider has an outage, raises prices 5x, or changes their terms of service?"
Why: Vendors building on third-party APIs have created a dependency you've inherited. This question forces honesty about resilience and supply chain risk.
Good answer: Multi-model fallback, on-prem options, contingency planning. Red flag: "That's never happened."

🎯 The Meta-Test

If a vendor can't comfortably answer questions 1, 2, 3, and 9, walk away. Those four alone separate genuine AI-augmented security products from rebranded legacy tools with a chatbot stapled on.