AI Exposes Attacks Traditional Detection Methods Can't See
SiliconANGLE, Sunday, May 3rd, 2026
Traditional rule-based security detection systems fail to identify modern attacks like side-channel attacks that operate through encrypted channels and legitimate tools.
This guest column by Evan Powell argues that current security detection systems have fundamental architectural limitations in identifying advanced attacks, particularly side-channel attacks that exploit physical factors like power consumption and timing rather than software vulnerabilities.
Rule-based detection systems are designed to match discrete indicators and known patterns, but modern attacks - including those using encrypted channels, legitimate tools, and AI-assisted techniques - leave no matchable signals for traditional tools to detect.
While AI is increasingly deployed in security operations, most systems are applied after detection occurs and improve response rather than expanding what detection can actually observe. Powell contends that closing this detection gap requires moving beyond rule-based systems to behavior-focused approaches that evaluate sequences of actions and system relationships over time, enabling organizations to detect threats before attackers achieve their objectives.