AWS IAM Access Analyzer vs Google Cloud IAM Recommender: Agent Permission Rightsizing

AWS IAM Access Analyzer excels at policy validation and external access visibility because it mathematically proves what resources are shared outside your trust boundary. For example, it generates findings for public S3 buckets or cross-account roles in under 5 minutes, and its custom policy checks use automated reasoning to guarantee no unintended access paths exist—a critical capability when agents assume roles across multiple accounts.

Google Cloud IAM Recommender takes a different approach by analyzing actual permission usage over time. It uses machine learning to examine the last 90 days of an agent's API calls and generates rightsizing recommendations that remove unused permissions. This results in a 60-90% reduction in excess permissions for most service accounts, but unlike Access Analyzer, it does not mathematically prove the safety of the resulting policy.

The key trade-off: If your priority is mathematically provable least privilege and external access visibility, choose AWS IAM Access Analyzer. If you prioritize usage-driven, automated rightsizing based on real agent behavior, choose Google Cloud IAM Recommender. For multi-cloud environments, consider running both in their respective clouds and normalizing findings through a centralized CIEM platform like Ermetic or Authomize.

AWS IAM Access Analyzer excels at generating mathematically provable least-privilege policies based on actual activity logs from CloudTrail. Its core strength is automated policy generation: it analyzes access history over a trailing window (default 90 days) and produces a new IAM policy that grants only the actions used. This results in a highly accurate, ready-to-deploy policy that eliminates the manual guesswork of writing JSON. For example, a DevOps team running a multi-account CI/CD agent fleet can automatically reduce a permissive AdministratorAccess policy to a tightly scoped set of s3:GetObject, ec2:DescribeInstances, and sts:AssumeRole actions, directly reducing the blast radius of a compromised agent credential.

Google Cloud IAM Recommender takes a different approach by using machine learning to identify over-permissions and suggest incremental role removals. Instead of generating a new policy from scratch, it analyzes an agent's service account permissions and recommends removing specific roles that haven't been used in the last 90 days. This strategy is deeply integrated with Google's pre-defined and custom roles model, making it exceptionally effective for organizations that have standardized on role-based access control (RBAC). The trade-off is that it provides a list of roles to remove rather than a complete, bespoke policy document, which may still leave some unused permissions within a retained role.

The key trade-off: If your priority is a one-click, mathematically precise least-privilege policy for a specific agent, choose AWS IAM Access Analyzer. If you prioritize continuous, ML-driven hygiene across a fleet of agents managed through an RBAC model and want to avoid policy sprawl, choose Google Cloud IAM Recommender. For a hybrid environment, consider using AWS's policy generation for critical, high-risk agents while applying Google's recommender for broad, ongoing organizational governance.