AI floods security teams with flaws — business context sets priorities

A security researcher scanning a large B2B company discovered an internet-facing database with weak authentication. Initially, this appeared to be a high-severity issue, but researchers found it was a resettable test database for candidate interviews. The problem lies in scanners and researchers being unable to understand the real impact of a compromise.

Companies now rely on external partners to handle vulnerability triage, but scanners generate excessive noise that engineers must sift through to understand alerts, tune out false positives, and remediate issues, leaving less time for other tasks like building features or scaling the tech environment. Product and engineering teams face overwhelming workloads, with engineering teams often carrying technical debt.

Security teams are now drowning in a different type of data, as programs collect various types of events and logs, producing more analysis data. Security leaders must decide where limited capacity can make the greatest impact, as a technical severity score alone cannot determine the business priority of a fix. The Common Vulnerability Scoring System (CVSS) provides a baseline severity classification but lacks context on asset exposure or business relevance.

Decisions should consider whether a vulnerable component is exposed to the public internet, the potential consequences if exploited, and whether attackers are actively targeting it. The Exploit Prediction Scoring System offers a forward-looking estimate of the likelihood of exploitation. However, the gap between known vulnerabilities and actively exploited ones is shrinking due to accelerating AI-driven exploit development.