Navigating the Security Landscape of GenAI

The rapid evolution of Generative AI presents unprecedented opportunities, but also raises new concerns around security and data governance. As organizations increasingly leverage GPTs and other LLMs for innovative applications, ensuring their robust security posture becomes paramount. This is particularly true for applications built on platforms like Amazon Bedrock, where the interplay between pre-trained or fine-tuned models and the knowledge bases they connect to introduces unique security considerations.

At its core, securing GenAI applications on Bedrock isn’t a radical departure from established cloud security principles. Fundamentals like WAF for DoS protection, stringent user authentication and least-privilege IAM policies on bedrock:InvokeModels actions remain critical. And AWS GuardDuty continues to be an invaluable tool for monitoring suspicious activities across your environment. However, the generative nature of these applications necessitates a deeper, more specialized approach to mitigate emerging threats.

Our recent deep dive into securing GenAI applications on AWS Bedrock highlighted key areas where traditional security measures must be augmented. The focus extended to Scopes 3 and 4 of the AWS Generative AI Security Scoping Matrix, addressing the security of pre-trained and fine-tuned models.

Here’s a glimpse into the proactive strategies essential for safeguarding your GenAI application deployments:

Combating Prompt Attacks with Bedrock Guardrails

• Prompt injection is a significant concern, where malicious inputs can manipulate an LLM to ignore system instructions, reveal sensitive data or generate unintended outputs, which is why it is first on the OWASP LLM Top 10. Amazon Bedrock Guardrails offer a powerful defense, allowing you to configure filters for prompt attacks, harmful content categories and even redact PII. This proactive filtering at the inference layer is crucial for maintaining control over model behavior.

Protecting Sensitive Information Disclosure (OWASP LLM02)

• GenAI applications often interact with vast knowledge bases, making sensitive information disclosure a critical risk. We explored how to implement robust access controls within S3 Vector Stores by leveraging metadata filtering. By associating metadata with documents, Bedrock Knowledge Bases can dynamically filter retrieved content, ensuring users only access information relevant to their authorized roles. This adheres strictly to the principle of least privilege, preventing unauthorized access to confidential data. As always, customer data within Bedrock is encrypted at rest and fine-tuned models can be encrypted using AWS KMS keys, reinforcing data security.

Real-time Detection and Automated Response

• Effective security isn’t just about prevention; it’s also about rapid detection and response. We delved into configuring AWS CloudWatch custom metrics and alarms to monitor Bedrock Guardrail triggers. When an alarm threshold is met, like repeated prompt attacks from a specific user, automated responses can be executed via AWS Lambda functions to disable the offending user in the app. This near real-time feedback loop is vital for containing threats and maintaining application integrity.

Continuous Verification with Automated Testing

• The dynamic nature of GenAI applications demands continuous security validation. Tools like promptfoo enable repeatable, automated testing of prompt attacks and content filtering policies, which can be integrated into your app’s CI/CD pipeline. This allows for systematic evaluation of your application’s resilience against various adversarial inputs, ensuring that security controls remain effective as your application evolves. Additionally, AWS Config Rules can be employed to detect and flag misconfigured Bedrock Agents ensuring that guardrails are consistently applied.

Partnering for Secure GenAI Innovation

The journey to securing GenAI applications on AWS Bedrock is multifaceted, requiring a blend of established cloud security practices and specialized GenAI-specific safeguards. By proactively implementing robust guardrails, stringent access controls, real-time monitoring and continuous validation, organizations can unlock the full potential of generative AI while mitigating its inherent risks.

Are you looking to secure your GenAI applications on AWS Bedrock? Connect with us to explore how we can partner to build a resilient and secure foundation for your AI innovations.