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Amazon has introduced new security features and best practices for its Bedrock platform, focusing on both API key management and the protection of generative AI applications. The release of Amazon Bedrock API keys provides developers with new options for accessing the Bedrock API, but also introduces potential security risks if not managed properly. AWS recommends using temporary security credentials from AWS Security Token Service (STS) as the preferred authentication method, as these credentials are short-lived and reduce the risk of long-term exposure. In cases where API keys must be used, AWS advises choosing short-term API keys over long-term ones, as they have built-in expiration mechanisms that limit the window of vulnerability if a key is compromised. For scenarios requiring long-term API keys, such as with certain third-party software, AWS suggests implementing strict controls and monitoring to mitigate risks. Service control policies (SCPs) can be used to restrict the creation and use of API keys, further reducing the attack surface. In addition to credential management, AWS has launched Amazon Bedrock Guardrails, a suite of configurable safeguards designed to protect generative AI applications from sophisticated attacks, including encoding-based threats. These guardrails provide six key protections: content filters, denied topics, word filters, sensitive information filters, contextual grounding checks, and automated reasoning checks. Encoding-based attacks, where malicious actors encode harmful content to bypass detection, are a growing concern for organizations deploying generative AI. Bedrock Guardrails address this by implementing a defense-in-depth strategy that detects and blocks encoded threats, such as base64 or hexadecimal representations of forbidden words. The safeguards are designed to work across multiple foundation models, including those hosted outside of Amazon Bedrock, ensuring broad applicability. AWS provides detailed guidance on implementing these protections, emphasizing the importance of balancing robust security with usability to avoid friction for legitimate users. Monitoring and auditing of API key usage, as well as regular review of guardrail configurations, are recommended to maintain a strong security posture. The combination of secure API key management and advanced guardrails positions Amazon Bedrock as a platform capable of supporting secure, scalable, and responsible generative AI deployments. Organizations are encouraged to adopt these best practices to protect sensitive data and maintain compliance with internal and external security standards. By leveraging these new features, enterprises can mitigate the risks associated with both credential exposure and advanced AI-specific attack vectors. The comprehensive approach outlined by AWS reflects the evolving threat landscape and the need for proactive, layered defenses in modern cloud environments. Security teams should stay informed about updates to Bedrock's security capabilities and continuously adapt their strategies to address emerging threats. The integration of these controls into development and operational workflows is essential for safeguarding AI-driven applications and maintaining trust in automated systems.
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AWS published a security blog post outlining implementation and management best practices for securing Amazon Bedrock API keys.
AWS published a security blog post describing how to protect generative AI applications against encoding-based attacks using Amazon Bedrock Guardrails.
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AWS has introduced Amazon Bedrock API keys in addition to its broader push away from long-lived IAM access keys toward modern, temporary authentication mechanisms. Bedrock supports locally generated short-term keys from presigned URLs as well as long-term bearer tokens backed by IAM users; the latter can be created without an expiration date and are submitted directly in HTTPS headers. By default, long-term keys receive the `AmazonBedrockLimitedAccess` policy, which may grant more access than is necessary for narrowly scoped model-invocation workloads. Researchers found long-term Bedrock API keys exposed in public GitHub repositories within two weeks of the feature's launch. AWS added the keys to its secret-scanning process and now quarantines associated IAM users and notifies affected customers when exposures are detected. Organizations should favor temporary, role-based credentials, restrict IAM-user creation through service control policies, and consider denying `bedrock:CallWithBearerToken` where Bedrock API keys are not required, blocking both long- and short-term API-key use.
Sep 3, 2026
Organizations are increasingly leveraging generative AI and large language models (LLMs) to accelerate software development and enhance security operations, but this adoption introduces new security risks. Experts emphasize that while AI can boost productivity and augment security teams, it can also generate code with embedded vulnerabilities, especially if models are trained on flawed or insecure codebases. To mitigate these risks, security leaders recommend a combination of human oversight, upskilling developers in secure coding practices, and implementing robust verification processes for AI-generated outputs. Industry guidance, including a new whitepaper from AWS and SANS Institute, highlights the importance of responsible AI practices, automated reasoning to verify LLM outputs, and scaling security best practices across the AI lifecycle. Key recommendations include architecting secure generative AI solutions, balancing automation with human review, and adapting compliance strategies to address evolving regulatory and threat landscapes. As AI adoption accelerates, organizations must proactively address both the opportunities and challenges posed by AI in cybersecurity to maintain a strong security posture.
Jun 29, 2026
AWS published a reference architecture for **Amazon Bedrock AgentCore** that limits AI agents to the requesting user’s permissions by propagating end-user authorization context to downstream services instead of enforcing access control in agent code. In the design, users authenticate through **Amazon Cognito**, custom claims such as department are added to JWTs, and **AgentCore Runtime** validates those claims before the agent runs. AWS said this approach reduces the risk of unauthorized data exposure from prompt injection or application bugs because the agent acts as an orchestrator rather than a trusted gatekeeper. The architecture demonstrates three enforcement patterns across common enterprise data sources: user-scoped temporary AWS credentials for **Amazon DynamoDB** using `STS AssumeRoleWithWebIdentity` and session tags, metadata-based filtering for **Amazon Bedrock Knowledge Bases**, and OAuth 2.0 on-behalf-of token exchange for **Salesforce** through AgentCore Identity. In the example CRM workflow, Sales and Finance employees can use the same agent while only receiving records allowed by their own roles, with AWS also recommending stronger isolation such as separate knowledge bases and IAM policies where stricter separation is required.
Aug 20, 2026