The security vendor landscape is crowded and the marketing claims are bold. Every product promises AI-powered, real-time, complete protection. Cutting through this noise requires a structured evaluation approach.
Start with your requirements, not vendor capabilities
Before looking at any product, document what you actually need. What are your detection gaps? What operational problems are you trying to solve? What does your team look like in terms of size and skill level?
A vendor's feature list is irrelevant if it does not address your specific challenges. The best detection platform in the world adds no value if your real problem is alert fatigue from a poorly tuned existing tool.
Run a proof of value, not a proof of concept
A proof of concept shows that a product works. A proof of value shows that it delivers measurable improvement in your environment. The difference matters.
Design your evaluation around outcomes:
- Detection coverage: Deploy the product alongside your existing tools. Does it find threats your current stack misses?
- Operational impact: Does it reduce the time analysts spend on triage? Does it decrease false positive rates?
- Integration: Does it fit into your existing workflows and toolchain without creating new operational overhead?
Run the evaluation in your environment, with your data, for at least two weeks. Vendor demo environments are curated to showcase strengths and hide limitations.
Questions that reveal the truth
"How do your detections handle false positives?" Look for specifics. Vague answers about AI suggest the vendor does not have a strong tuning methodology.
"Can I see the logic behind a detection?" Transparency matters. If you cannot understand why something was flagged, you cannot trust it or tune it.
"What does your detection coverage look like against MITRE ATT&CK?" This forces a concrete answer about what the product can and cannot detect. Gaps are expected; dishonesty about gaps is a red flag.
"What operational overhead does your platform require?" Some products deliver strong detection but demand constant care and feeding. Understand the total cost of ownership, not just the licence fee.
"Can I speak to customers in similar environments?" Reference calls with organisations of similar size, industry, and maturity level are invaluable.
Red flags
- Vendors who resist proof-of-value testing in your environment.
- Claims of "100% detection" or "zero false positives."
- Inability to explain how their AI works in practical terms.
- Pricing models that penalise data growth or add hidden costs for features you need.
- Sales processes that escalate pressure rather than providing information.
Making the decision
The right vendor is the one that solves your specific problems with acceptable operational overhead and a business model that works for your budget. Not the one with the most impressive demo or the longest feature list.
At SenseOn, we encourage thorough evaluation. Our platform is designed to deliver measurable detection improvement, and we are confident it performs well in side-by-side comparisons.
