Insider activity looks ordinary right up until it is not, and the window to act closes before most tools register anything.
Dangerous insider attacks look ordinary until it is too late. The activity is authorized, the systems are the ones the person uses every day, and no individual action crosses a threshold worth alerting on.
Personam’s behavioral AI identified insider sabotage patterns that credential-based and signature-based tools did not surface, while narrowing analyst review to a small fraction of total activity. The detection came from continuous peer comparison rather than rule authoring, which is what makes it work against behavior nobody wrote a rule for.
An insider case is also an organizational problem, not just a technical one. The evidence has to be strong enough to act on before anyone is willing to act, which is why the investigation record matters as much as the detection.
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The fastest way to evaluate Personam is to run it against your own traffic and see what it surfaces.
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