Expanding AI Benchmarks in Cybersecurity Beyond Vulnerability Discovery

CrowdStrike argues that AI cybersecurity evaluation should extend beyond vulnerability discovery and exploit generation, which are easy to measure but address only one route into an organization, noting that vulnerability exploitation accounted for 31% of breaches in Verizon’s 2026 dataset while credential abuse, phishing, social engineering, and trusted relationships remain major entry points. It contends that meaningful assessments should test whether AI can support the broader defensive lifecycle, including alert triage, investigation, detection engineering, threat hunting, remediation, and response after attackers gain access. The company says public benchmarks are limited by their focus on creator priorities, score saturation among leading models, potential training-data contamination, and insufficient connection to real-world telemetry and adversary tradecraft. It proposes task-relevant, telemetry-grounded, customer-specific evaluations using real intrusion intelligence and organizational threat profiles, and states that it plans to demonstrate this approach at Fal.Con 2026.

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