By Suman Madugula
Publication Date:
Security operations have been based on a stable enemy model for decades. Attackers exploit vulnerabilities, escalate privileges, move laterally, exfiltrate data, or disrupt availability. SOC processes, tools and metrics are optimized to detect these patterns.
AI attacks don’t work this way.
Instead of exploiting software bugs, they can also manipulate data. Instead of stealing databases, they extract models through inference abuse. Instead of crashing systems, they subtly influence outcomes. The goal is not disruption; it is humiliation. The quality of decisions decreases while the system appears healthy.
From the SOC’s perspective, there is nothing wrong. The logs look normal. Access is authorized. The operating time remains unaffected. From the company’s perspective, the system’s algorithmic results are silently corrupted.
For this reason, AI compromises rarely reach the SOC as a security incident. When models behave unexpectedly, the problem is almost always presented as…
