BeyondTrust’s messaging that the endpoint’s most powerful actor is “no longer human” — framed around securing AI and automated agents — captures a problem that privileged access management teams are only beginning to grapple with operationally: machine-driven privilege elevation is not an edge case anymore, it’s becoming routine infrastructure, and traditional PAM models built around human administrator oversight are straining to accommodate it.
The problem is architectural. Legacy PAM systems were designed with the assumption that privileged access would be requested by a human, approved (ideally) by a manager, granted for a defined session, and then released when the human logged off. That model breaks down when the “actor” requesting privilege elevation is an AI agent scheduled to run a task, a CI/CD pipeline needing database admin access to run migrations, or an automation script requiring cloud console credentials to orchestrate infrastructure changes. These machine actors don’t fit neatly into the “request-approval-session-release” cycle because they operate at machine speed and can be spawned or destroyed in minutes.
The security implications are significant. A human administrator requesting sudo access at 3am on a Sunday triggers anomaly detection — it’s unusual. An AI agent requesting the same privilege at 3am on a Sunday might be perfectly normal from an operational standpoint, but it’s exactly the kind of invisible, high-privilege activity that traditional privileged access governance struggles to monitor. If the AI agent is compromised, has its credentials stolen, or is operating under a supply-chain compromise in its training model or dependencies, a PAM system that treats it as a routine, approved actor won’t catch it.
BeyondTrust’s framing of “securing the AI actor” requires privilege access management systems to do several things they traditionally haven’t done well. First, continuous reassessment of whether a machine actor *should* have the privilege it was granted — not just at request time, but continuously during its execution. Second, behavioural monitoring at the machine level — understanding what a “normal” set of API calls or privileged operations looks like for an AI agent, and flagging deviations in real time. Third, decoupling machine authentication from human-style interactive sessions — AI agents need credential vaulting and automated refresh, not interactive approval flows.
For IAM teams building PAM strategies that account for AI agents and automation, the lesson is clear: your PAM platform needs to support first-class, non-human privileged identities with their own lifecycle, monitoring, and governance rules — not treating machine actors as edge cases retrofitted onto a human-centric design.
Source: Intelligent CISO