How do AWS Bedrock Guardrails and IAM controls secure LLM applications?

AWS Bedrock Guardrails and IAM controls secure LLM applications by providing a layered defense that manages access, filters content, and monitors for anomalous behavior. Guardrails offer a policy layer for content filtering, topic denial, and sensitive information redaction, while IAM controls enforce least privilege for Bedrock resources and associated services.

Here are concrete controls:

• NIST AI RMF - Govern, Manage; OWASP LLM08 Excessive Agency, OWASP ASI03 Identity & Privilege Abuse: Create dedicated, service-specific IAM roles for Bedrock invocation, knowledge-base retrieval, and each agent action group, avoiding broad bedrock:* permissions on production principals. These roles should use least-privilege and distinct credentials per agent action group, prohibiting shared admin tokens in tool configurations.
• NIST AI RMF - Govern, Manage; OWASP LLM01 Prompt Injection, OWASP LLM02 Sensitive Information Disclosure, OWASP LLM09 Overreliance: Attach Bedrock Guardrails to all production agents and direct model endpoints, configuring content filters, denied topics, sensitive information filters, and contextual grounding checks. These guardrails should be tested with adversarial prompts and poisoned knowledge-base documents.
• NIST AI RMF - Map; OWASP LLM02 Sensitive Information Disclosure: Encrypt knowledge-base sources and agent memory with AWS KMS Customer Managed Keys (CMKs) and minimize sensitive fields before they enter prompts or retrieval indexes.
• NIST AI RMF - Measure: Enable CloudTrail and CloudWatch logging for model invocations, guardrail interventions, and tool executions, and configure alerts for anomalies and authorization failures. Integrate AI telemetry into SIEM and SOAR workflows, logging prompt templates, retrieval queries, tool calls, model versions, and output safety filter triggers.
• NIST AI RMF - Manage; OWASP LLM08 Excessive Agency: Require human approval workflows for irreversible or high-value agent actions, displaying proposed actions with evidence summaries. Design action groups as small, schema-validated APIs and run Lambdas with least-privilege roles isolated per business function.
• NIST AI RMF - Manage: Bind Bedrock Agent sessions to authenticated users, enforcing Time-To-Live (TTL) and secure session ID generation to prevent cross-tenant reuse.