Every Attack Has an Identity

Every attack surface security teams manage today collapses into the same variable: identity. This guide combines four Abnormal AI perspectives — AI security, impersonation and insider risk, non-human identity, and identity threat protection — into one argument: whether the identity in question is a person, a service account, or a hijacked session, the only durable defense is knowing what normal looks like for that specific identity and catching the moment it changes. Built from conversations with seven Abnormal AI security leaders and practitioners.

[h3]Key takeaways[/h3]

[li indent=0 align=left]Four attack surfaces, one root cause: shadow AI, impersonation, insider risk, non-human identity, and post-login session hijacking all exploit the same gap — legitimate-looking access being used illegitimately.[li indent=0 align=left]Non-human identities and AI agents have quietly become the majority of most enterprise environments, and almost none of them have a behavioral baseline.[li indent=0 align=left]Attackers have shifted from breaking in to logging in: credential theft, impersonation, and session hijacking now outpace malware as the primary path to compromise.[li indent=0 align=left]Rules-based security asks whether an action is permitted; only behavioral AI asks whether the identity behind it is still behaving like itself — the distinction that catches all four attack surfaces in one model.[li indent=0 align=left]Drawn from firsthand accounts across seven Abnormal AI leaders, including a wire-transfer attack that started with a single compromised identity and a North Korean IT worker scheme spanning 320+ companies.