AWS Bedrock Security Best Practices: Building Secure Generative AI Applications

Source: Dev.to

Introduction

Security is one of the biggest concerns when adopting generative AI in production. Amazon Bedrock addresses this by providing a highly secure managed service, but, like all AWS services, security is a shared responsibility. AWS secures the underlying infrastructure, while customers are responsible for how Bedrock is used within their applications.

Shared Responsibility Model

AWS Responsibility (the Cloud)

• Physical data centers and global infrastructure
• Network architecture and availability
• Managed service security for Amazon Bedrock
• Compliance programs and third‑party audits

AWS regularly validates its controls through industry‑recognized compliance frameworks, giving customers a secure foundation to build on.

Customer Responsibility (in the Cloud)

• IAM roles and permissions
• Network access configuration
• Data sensitivity and regulatory compliance
• Application‑level security (including prompt‑injection protection)

Understanding this distinction is critical when deploying AI workloads with Bedrock.

Data Handling Guarantees

• Prompts and completions are not stored.
• Customer data is not used to train AWS models.
• Data is not shared with model providers or third parties.

Bedrock uses Model Deployment Accounts, isolated AWS accounts managed by the Bedrock service team. Model providers have no access to these accounts, logs, or customer interactions, ensuring strong data confidentiality by design.

Encryption

All communication with Amazon Bedrock is encrypted using:

• TLS 1.2 (minimum), with TLS 1.3 recommended
• Secure SSL connections for API and console access

All API requests must be signed using IAM credentials or temporary credentials from AWS STS.

Amazon Bedrock encrypts:

• Model customization jobs
• Training artifacts
• Stored resources associated with customization

This protects sensitive data even when it is at rest.

Network Isolation

For workloads requiring strict network isolation, Bedrock integrates with Amazon VPC and AWS PrivateLink.

Best practices:

• Run Bedrock‑related jobs inside a VPC.
• Use VPC Flow Logs to monitor network traffic.
• Avoid public internet exposure by using interface endpoints.

Supported VPC integrations:

• Model customization jobs
• Batch inference
• Knowledge Bases accessing Amazon OpenSearch Serverless

This approach is especially valuable for regulated industries and internal enterprise applications.

IAM Best Practices

• Follow the principle of least privilege.
• Use dedicated IAM roles for Bedrock access.
• Prefer AWS STS temporary credentials over long‑lived credentials.
• Restrict access at both the service and resource level.

IAM is provided at no additional cost and integrates seamlessly with Bedrock.

Cross‑Account Model Import (from Amazon S3)

• The bucket owner must grant explicit permissions.
• Access policies should be scoped tightly to required actions only.
• Review cross‑account access carefully to avoid unintended exposure.

Compliance

Amazon Bedrock participates in multiple AWS compliance programs. To verify whether Bedrock meets your compliance requirements:

1. Review AWS Services in Scope by Compliance Program.
2. Cross‑reference with your regulatory obligations (HIPAA, SOC, ISO, etc.).

Compliance is a shared responsibility, so proper configuration on the customer side is essential.

Incident Response

• AWS handles incident response for the Bedrock service itself.
• Customers are responsible for:
  • Detecting incidents within their applications.
  • Responding to misuse or data exposure.
  • Monitoring logs and access patterns.

A clear incident‑response plan should be part of any production AI deployment.

Prompt‑Injection Defenses

Prompt injection is a common risk in generative AI systems. Application‑level defenses are the customer’s responsibility.

Mitigation techniques:

• Sanitize and validate all user inputs.
• Enforce strict input formats where possible.
• Reject or escape unsafe content before sending it to Bedrock.
• Avoid dynamic prompt construction via string concatenation.
• Separate system prompts from user input.
• Restrict permissions using least‑privilege IAM roles.
• Perform penetration testing on AI workflows.
• Use static and dynamic application security testing (SAST/DAST) targeting prompt‑manipulation scenarios.
• Keep SDKs and dependencies up to date.
• Monitor AWS security bulletins and follow official Bedrock documentation.

Bedrock Guardrails

• Detect prompt‑injection attempts.
• Enforce content boundaries.
• Apply consistent safety rules across applications.

Guardrails should be considered a baseline security control for any Bedrock‑based application.

Additional Protections for Bedrock Agents

• Associate guardrails directly with agents.
• Enable default or custom pre‑processing prompts to classify user input.
• Clearly define system prompts to restrict agent behavior.
• Use Lambda‑based response parsers for custom enforcement logic.

These features significantly reduce the risk of malicious or unintended behavior.

Conclusion

Amazon Bedrock provides a strong, secure foundation for generative AI, but security does not stop at the service boundary. AWS protects the infrastructure, while customers must secure their applications through careful design, guardrails, and ongoing monitoring. By combining IAM best practices, network isolation, encryption, and prompt‑injection defenses, organizations can confidently deploy AI solutions that are both powerful and secure. Security in generative AI is an ongoing responsibility, not a one‑time setup.