AI agents are now some of your busiest “workers”, accessing databases, executing workflows, and making decisions around the clock. But many organizations are deploying them without proper identity controls.

Unlike a human user, a compromised AI agent credential can cause automated, widespread damage at machine speed. This growing security gap means we need to rethink our IAM strategy.

To secure AI agents effectively, especially on Google Cloud, here are a few essentials:

✔️ Assign each AI agent a unique digital identity. Sharing service accounts breaks auditing and accountability.

✔️ Apply least-privilege access—agents should only get permissions for their exact tasks.

✔️ Manage the full lifecycle of AI identities, from onboarding and secret rotation to decommissioning when projects pause.

✔️ In Google Cloud, use Workload Identity for GKE workloads, letting pods authenticate securely without static credentials.

✔️ For serverless AI, follow service account best practices in Cloud Functions and Cloud Run, limiting permissions and enabling automatic key rotation.

✔️ And when it comes to data access, control AI connections to services like AlloyDB through identity-based policies.

Identity is the key control point for securing autonomous agents throughout cloud infrastructure.

I’m interested in hearing how teams are evolving their identity strategies to handle this new wave of autonomous AI agents.