When an AI SOC Misses a Threat, What Happens?

With organizations adopting AI-powered SOCs, much of the attention focuses on reducing false positives. False negatives where AI falsely clears an attack or its early phases is far less discussed, but far more problematic.

Yasir Zahid, Cybersecurity Leader and Product Builder with Dubai-based Secure.com, has just published “When an AI SOC Gets It Wrong: False Negatives, Risk, and What Comes Next.

Yasir’s detailed analysis recognizes that AI SOCs miss real threats more often than SOC teams and their organizations expect, and lays out the costs of false negatives to the average organization.

  • AI detection tools lose ~between 45 and 50 percent of their tested accuracy when deployed in real environments because of differences in data, infrastructure and dynamic, evolving threats.
  • Up to 40 percent of alerts in a standard SOC go completely uninvestigated – and that slow detection is a strong potential driver for escalating estimates of the cost of a data breach.
  • An effective SOC is a  well-governed one that logs what it missed, flags gaps, routes uncertain signals to human review, and escalates ambiguous as well as high-risk cases to supervising humans.

You can read more here: What Happens When an AI SOC Misses a Threat

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