Ensuring the security of generative AI systems is critical, given their complex nature and potential vulnerabilities. In this blog, I talk about three emerging security considerations and highlight an AWS security service for generative AI applications and LLMs on Amazon Bedrock..
Three emerging GenAI Security areas for CISOs to consider
1/ Model Output Anomalies: Generative AI models may generate output anomalies, including hallucinations and biases. Given the probabilistic approach of word generation, these models might produce confident but inaccurate outputs. Moreover, implicit or explicit biases in training data necessitate effective mitigation strategies. Regularly updating and refining training data, along with implementing robust evaluation metrics, can help minimize these anomalies and improve model reliability.
2/ Data Protection: Protecting data is paramount to avoid leaks to third parties, safeguard intellectual property, and ensure legal compliance. Robust governance and legal frameworks are crucial, as data becomes a key differentiator in maintaining a competitive advantage. Encryption of data at rest and in transit, access controls, and continuous monitoring are essential practices. Additionally, implementing differential privacy techniques can help protect individual data points while still allowing useful insights to be extracted.
3/ Securing Generative AI Applications: It’s vital to defend AI applications against prompt injection attacks, where malicious inputs can bypass model constraints. For instance, attackers might evade instructions designed to block harmful activities. Implementing stringent security measures is essential to mitigate such threats. Regular security audits, penetration testing, and employing adversarial testing techniques can further strengthen defenses against such attacks.
Amazon BedRock
Amazon’s generative AI platform, BedRock, operates on an API-driven, token-based model for input and output. Supporting a range of large language models (LLMs), including Mistral, Anthropic’s Claude, and Meta’s LLaMA (3.1 and 4.0.5b), each model provider aims to ensure user security. BedRock’s architecture is designed to offer seamless integration with various AWS security services, ensuring a comprehensive security posture for generative AI deployments.
BedRock GuardRails
Amazon BedRock GuardRails enables customers to add a protective layer between the user’s prompt and the LLM, and between the LLM and the user’s response. Key features include:
- Content Filters: Block harmful content in input prompts or model responses. These filters are continuously updated to recognize and block new and evolving threats.
- Deny Topics: Prevent processing of specific topics. This feature ensures compliance with legal and ethical standards by preventing the AI from engaging with forbidden content.
- Word Filters: Block undesirable phrases or profanity. This maintains the integrity and professionalism of the AI outputs.
- Sensitive Information Filters: Block or mask sensitive data like Personally Identifiable Information (PII). By incorporating advanced pattern recognition, these filters can detect and redact sensitive information in real-time.
- Contextual Grounding: Detect and filter hallucinations and harmful actors. By leveraging context-aware algorithms, BedRock can discern when outputs deviate from expected behavior, enhancing the overall safety and reliability of the system.