AWS Bedrock Identity Management 101: Controlling Who Can Access What in AI Workloads

Read full article here: https://www.p0.dev/blog/governing-access-in-amazon-bedrock/?utm_source=nhimg

As generative AI becomes a core part of enterprise operations, Amazon Bedrock is emerging as a powerful platform for securely building and scaling AI applications with foundation models like Anthropic Claude, Mistral, and Amazon Titan. But with great flexibility comes new identity and access challenges. Each model invocation, customization, or cross-account configuration represents a potential security exposure point — and traditional IAM controls often fail to keep up.

This article examines how identity governance, not just cloud operations, must evolve to manage Bedrock’s access surfaces responsibly. Misconfigured bedrock:* permissions or unrestricted InvokeModel rights can lead to sensitive data exposure, uncontrolled AI usage, or shadow model creation. The post maps the identity risks inherent in Bedrock usage — from runtime access and lifecycle management to cross-account data governance — and provides actionable guidance to reduce them.

Security and IAM leaders are urged to:

• Replace standing model access with just-in-time invocation and short-lived credentials

• Enforce separation of duties between model administration and runtime use

• Ensure identity provenance by linking all Bedrock events to federated, auditable identities

• Apply organizational SCPs and region restrictions to align model sharing with data residency rules

Ultimately, effective Bedrock governance will define enterprise AI maturity. Organizations that secure who can invoke, customize, and share models — and when — will be those best positioned to innovate safely in the era of agentic and generative AI.