Governing Access in Amazon Bedrock

Generative AI has become a mainstream tool in enterprises, with platforms like Amazon Bedrock enabling organizations to build and scale AI applications using foundational models such as Claude, Mistral, and Amazon's Titan through AWS APIs. Although Bedrock facilitates experimentation, customization, and deployment of AI, it also introduces potential security challenges related to access control. Mismanagement of Bedrock permissions can lead to unauthorized access, data exposure, and increased AI usage costs, highlighting the need for careful governance. Key risks include runtime access, model configuration, cross-account operations, and auditability, necessitating robust identity management and permission controls. Effective governance requires limiting standing access, separating duties, ensuring identity provenance, and managing cross-environment risks by aligning policies with data classification and residency rules. Ultimately, security maturity in AI deployments will be measured by the ability to enable innovation through controlled access and well-defined permissions.