Lilly Tech Systems
AI Agent GCP Guardrails
Prevent AI agents from accidentally deleting or destroying Google Cloud resources. Learn IAM best practices, Organization Policies, resource protection mechanisms, audit logging, backup strategies, and production checklists — everything you need to run AI agents safely on GCP.
Your Learning Path
Follow these lessons in order to build a complete GCP guardrails strategy for your AI agents.
1. Introduction
GCP's unique architecture, real deletion scenarios, Resource Manager hierarchy, and an overview of protection mechanisms.
2. IAM & Service Accounts
Custom roles, service accounts with minimal permissions, Workload Identity Federation, IAM Conditions, and Deny Policies.
3. Organization Policies
Organization Policy Service, custom constraints, tag-based policies, policy inheritance, and YAML definitions.
4. Resource Protection
Project Liens, deletion protection for Compute, Cloud SQL, GKE, Storage retention, and Terraform prevent_destroy.
5. Audit Logging & Monitoring
Cloud Audit Logs, log-based alerting, Monitoring policies, Pub/Sub notifications, and alert pipelines to Slack.
6. Backup & Recovery
Backup and DR Service, disk snapshots, Cloud SQL PITR, GCS versioning, cross-region strategies, and recovery procedures.
7. Best Practices & Checklist
Complete GCP guardrails checklist, project isolation, VPC Service Controls, Assured Workloads, and emergency response.
What You'll Learn
By the end of this course, you'll be able to:
Lock Down IAM
Create custom roles and service accounts that prevent agents from executing destructive operations on GCP.
Enforce Org Policies
Use Organization Policy Service constraints to block resource deletion at every level of your hierarchy.
Protect Resources
Apply Project Liens, deletion protection, retention policies, and Terraform lifecycle rules to critical resources.
Monitor & Recover
Set up audit logging, real-time alerts, automated backups, and disaster recovery for agent-managed environments.