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Abnormal Extends Behavioral AI to Identity and AI SecurityLearn more

Infiltration Prevention

Stop malicious actors before they get in.

Nation-state actors have industrialized a new attack vector: the fake hire. Using stolen identities, fabricated resumes, and AI-generated interview personas, threat actors are applying for and landing legitimate jobs inside your organization, then using that access to steal data, plant malware, or fund sanctioned weapons programs.

Most identity and access tools were never built to catch this. They're designed to flag suspicious behavior after someone becomes an employee, not to verify who's actually behind a candidate before they're hired. By the time a background check turns up something wrong, or a first login trips an alert, the attacker already has a badge, a laptop, and legitimate access.

Organizations need a way to catch fraudulent identity at the moment it matters most: when it enters the hiring pipeline.

Abnormal Has the Solution

  • Security tool designed to prevent fraudulent candidates from entering your organization.
  • Integrates with your Applicant Tracking System (ATS), so every new identity is analyzed once it enters the pipeline, before anyone is provisioned.
  • Accurately identifies fabricated credentials, VoIP burner numbers, VPN-masked locations, and more to uncover large-scale campaigns.
  • Sends your SOC clear evidence of impersonations for further review.

The Abnormal Advantage at a Glance

Behavioral AI to discover campaigns

Correlates candidate signals across your entire ATS to reveal when the same actor is applying to multiple roles or departments at once.

Evidence-backed briefs

Cited, evidence-backed briefs that turn a suspicious application into a documented case your SOC can act on in minutes, not days.

Federated detection network

Fraud patterns identified in one organization's hiring pipeline sharpen detection across Abnormal's entire customer base.

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See Abnormal in Action

See how behavioral AI detects the attacks that legacy defenses miss.