The IT Leader's Checklist: Evaluating AI Tools Without Getting Burned

I've been on both sides of the AI tool procurement table. As a buyer, I've signed contracts I regretted. As a consultant, I've helped organizations avoid the same mistakes. The pattern is consistent: smart IT leaders get caught up in impressive demos, skip critical due diligence, and end up locked into tools that don't deliver. It's not that the tools are fraudulent — most of them work to some degree. The problem is the gap between what's demonstrated and what's operational.

I built this 15-point checklist over three years of evaluating AI tools. Every item on it exists because I (or someone I worked with) got burned by skipping it. Print it out. Bring it to vendor meetings. Don't sign anything until you can answer every question.

Section 1: Does the AI Actually Work?

1. Can you test it on YOUR data before purchasing?

This is the single most important question. Any vendor unwilling to let you run a proof of concept on your actual data is hiding something. Vendor demo environments are curated to make the product look good. Your environment has noise, edge cases, and configurations the vendor hasn't seen. Insist on a 30-day POC with your data or walk away.

2. What is the false positive rate in production environments similar to yours?

Not the false positive rate in the demo. Not the false positive rate in the white paper. The false positive rate in a production environment with similar data volume, complexity, and industry. Ask for customer references who will share this specific metric. If the vendor can't provide them, assume the false positive rate is worse than they claim.

3. How does the AI handle edge cases and novel threats?

AI is great at recognizing patterns it's been trained on. Ask specifically: what happens when the AI encounters something it hasn't seen before? Does it flag unknown patterns as suspicious (good) or silently pass them (dangerous)? Ask for examples of how the tool handled a zero-day or novel attack technique. If the answer is vague, the tool probably doesn't handle novelty well.

Section 2: What Will It Actually Cost?

4. What is the total cost of ownership for year one, including compute and staff time?

Don't accept the license fee as the answer. Ask specifically about compute requirements, data ingestion costs, integration engineering time, and ongoing tuning effort. If the vendor can only give you the license fee, they either don't know or don't want you to know. Either answer is a red flag. A mature vendor will have a TCO model they've built from actual deployments.

5. How does pricing scale with data volume or user count?

AI tools that charge per event, per query, or per data volume can have wildly unpredictable costs. Ask for pricing at 2x, 5x, and 10x your current volume. Ask whether there are caps, overages, or throttling. I've seen organizations hit a 3x cost increase in year two because their data volume grew faster than expected and the pricing model punished scale.

6. What does renewal pricing look like?

Many AI tool vendors offer aggressive year-one pricing to get you locked in, then increase prices 20-40% at renewal. Ask for the renewal price structure up front. Better yet, negotiate multi-year pricing before signing. Your leverage is highest before you're a customer.

Section 3: Integration and Operations

7. Which of your integrations are native connectors vs. API-only?

There's a massive difference between "we integrate with Splunk" (a maintained, tested connector) and "we have an API you can use to connect to Splunk" (you're building and maintaining the integration yourself). Ask specifically about each integration you need. Get documentation for the connectors you'll use, not just a logo wall.

8. What happens to my data during an outage or contract termination?

If the vendor's AI tool processes your security data in their cloud, where does that data live? What happens to it if the vendor has an outage? What happens to your data — including any models trained on it — if you terminate the contract? Get this in writing. I've seen organizations lose access to their own security data because the vendor's export process was cumbersome and slow.

9. How do you handle model updates, and can I roll back?

AI models get updated. Sometimes those updates break things. If the vendor pushes a model update that increases your false positive rate by 300%, can you roll back to the previous model version while you investigate? If the answer is no, you're at the mercy of the vendor's update schedule and QA process.

Section 4: Security and Privacy of the AI Itself

10. Is my data used to train models that serve other customers?

This is the question that makes vendors uncomfortable, which is exactly why you need to ask it. Some AI security vendors use customer data to improve their models, which means your sensitive security telemetry is influencing models that other organizations use. Depending on your industry and regulatory requirements, this may be a non-starter. Get a clear, written answer.

11. Where does AI processing happen — on my infrastructure, your cloud, or both?

Data residency matters for compliance. If AI processing happens in the vendor's cloud, where are those processing nodes located? Do you have control over the geography? For organizations subject to GDPR, CCPA, or industry-specific regulations, this isn't optional — it's a compliance requirement.

12. Has the AI tool itself been penetration tested?

You're deploying a tool that has access to your security data and can make automated decisions. Has that tool been pen tested by a credible third party? Ask for the report. If the AI tool has an API, has it been tested for prompt injection, model manipulation, or adversarial inputs? AI-specific attack vectors are real and not all vendors are testing for them.

Section 5: Vendor Viability and Support

13. What's the vendor's funding status and customer count?

The AI security space is crowded and consolidating. Startups will get acquired or go under. Before you commit to a tool, understand the vendor's financial stability. Ask how many paying customers they have, what their ARR growth looks like, and whether they've raised recent funding. You don't need audited financials — you need confidence they'll exist in two years.

14. What does support look like after the sale?

Pre-sales support is always great. Post-sales support is where vendors differentiate. Ask for the support SLA. Ask current customers about their support experience. Specifically: when you need help tuning the AI or resolving a false positive pattern, how long does it take to get someone knowledgeable on a call? "Knowledgeable" means an engineer, not a tier-1 support rep reading from a script.

15. Can I talk to three customers who deployed in the last 12 months?

Not the customer success stories on the website. Three real customers you can call and ask hard questions. Ask them: what surprised you after deployment? What's the real false positive rate? How much staff time does tuning require? What would you do differently? If a vendor can't produce three willing reference customers, that tells you something important.

How to Use This Checklist

Don't treat this as a scorecard where 12 out of 15 is passing. Every item matters for different reasons. Items 1-3 determine whether the tool works. Items 4-6 determine whether you can afford it. Items 7-9 determine whether it's operationally viable. Items 10-12 determine whether it's a security risk itself. Items 13-15 determine whether the vendor will be around to support it.

If a vendor pushes back on any of these questions, pay attention to which questions they dodge. That tells you where the weaknesses are. A confident vendor will answer all 15 directly. An honest vendor will say "that's a fair concern, here's how we mitigate it." A vendor worth avoiding will dismiss the questions as unnecessary or try to redirect to features you didn't ask about.

I've used this checklist to evaluate over 20 AI tools in the past three years. It's saved me from at least four contracts that would have been expensive mistakes. Take the time to ask every question. Your future self will thank you.