With Okta for AI Agents launching on April 30, our threat models are about to get a big shift, moving from human-speed reconnaissance to machine-speed attacks.

A compromised agent isn't limited by how fast a person can type. It can run thousands of API calls per second with legitimate credentials. This speeds up risks like privilege creep and the classic "confused deputy" problem, where logs show what a service account did, but not why or on whose behalf.

Getting ahead of this requires a hands-on approach. Here are the critical controls teams bridging Okta and GCP should be implementing now:

✔️ Use GCP IAM Conditions to enforce granular restrictions. For agents, this means going beyond source IP and using attributes like `https://lnkd.in/etAzbG4A to link actions back to a specific user, directly solving the 'confused deputy' audit gap.

✔️ Create separate BigQuery log sinks for agent vs. human activity. This helps baseline "normal" agent behaviour now, so you can spot anomalies the moment they happen.

✔️ Implement agent-specific lifecycle management. This means setting hard expiration dates, enforcing 90-day access reviews, and having automated ways to disable accounts.

✔️ Enforce least privilege with finely-tuned roles. An agent that only needs to read data should have a bigquery.dataViewer role, not bigquery.admin.

Okta for AI Agents will handle the identity governance, while GCP's IAM provides the crucial infrastructure guardrails. Real success in this new era depends on having the expertise to implement security across both ecosystems.

I keep asking my teams: If an AI agent were compromised tomorrow, how would we find out?

Would love to hear your thoughts on preparing for this shift.