Beyond Guardrails: How to Secure Your AI from Unsanitized Data and Deceptive Prompts

Beyond Guardrails: How to Secure Your AI from Unsanitized Data and Deceptive Prompts

A10 security experts, Jamison Utter, Diptanshu Purwar, and Madhav Aggarwal dive into a critical yet often overlooked aspect of AI security: the dangers of unsanitized training data. Jamison Utter presents a compelling case study of a beverage manufacturer's AI system, which was bypassed by a creative, deceptive prompt that described a copyrighted character without using the protected name. This incident exposes a fundamental flaw: despite having input guardrails, the AI's underlying training data was not truly "clean," allowing it to generate restricted intellectual property.

The discussion emphasizes that traditional security measures are not enough. Security architects and developers must implement a robust governance framework and advanced filtering mechanisms to protect against both intentional misuse and accidental data leakage, safeguarding the integrity of AI systems and intellectual property.

Learn how to secure AI and LLMs in your organization: https://bit.ly/4kOHmYd
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