
According to a report by Abnormal AI, 46% of security professionals say they are not at all effective at establishing what normal behavior looks like for AI agents, even as 43% say agents will receive their greatest behavioral-monitoring investment over the next 24 months, more than three times that of API integrations (14%).
Behavioral security uses AI to learn what’s normal for each identity, account, and system, then flags activity that deviates, rather than relying on known threat signatures. Without a clear picture of normal behavior, a compromised or misbehaving agent can look like any other. More than 81% of respondents now rely on behavioral anomaly detection to a major or extensive degree, but that maturity hasn’t yet extended to AI agents or machine identities.
While 72% of respondents say they are better prepared to detect anomalous behavior from non-human identities than from human ones, 53% do not define normal behavior for machine identities at all. And even when anomalies are caught, response is slow: 90% say knowing why an anomaly triggered is critical, but 51% need four hours or more to investigate and remediate one.
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