Why Traditional Evaluation Frameworks Fall Short
RFPs built for legacy security tools cannot adequately evaluate AI-native services. When your evaluation criteria were designed for rule-based systems, you will systematically undervalue the capabilities that matter most in modern security operations.
The CISO's challenge is not choosing between vendors. It is building an evaluation framework that actually measures what matters.
Core Capabilities to Assess
Start with detection efficacy, but measure it differently. An AI-native service should demonstrate adaptive detection that improves with your environment's data, not just a larger rule library.
- Behavioral baselines that adapt to your environment within 30 days
- Transparent model performance metrics, not just alert volume
- Explainable detections: every alert should answer 'why now, why this'
- Integration depth beyond syslog forwarding
The Build vs. Buy Calculus
Organizations like Daylight Security have demonstrated that AI-native approaches can reduce mean time to detect by 60% compared to traditional SIEM-based detection. But the real value is in the compound effect: each investigation improves future detection accuracy.
Building this in-house requires a data engineering team alongside your security team. Most organizations underestimate the data pipeline investment by an order of magnitude.
Red Flags in Vendor Conversations
Be wary of any vendor that cannot explain how their AI models are trained, what data they use, and how they handle model drift. Black-box AI in security is not innovation. It is a liability.
Building Your Evaluation Scorecard
Weight your evaluation toward operational outcomes, not feature checklists. The vendor who can show you a measurable reduction in analyst workload within a 90-day pilot is worth more than the vendor with the longest feature comparison matrix.