Building Trust in AI: A Zero-Trust Approach to Foundational Models

Rapid Summary

• White paper Topic: A extensive framework for ensuring security,resilience,and safety in large-scale AI models has been outlined by the Secure Systems Research center (SSRC) under the Technology innovation Institute (TII).
• Key Focus Areas: The paper leverages Zero-Trust principles to combat threats across various AI lifecycle stages-training, deployment, inference, and post-deployment monitoring. It identifies risks like data poisoning, model misuse, ethical concerns, and geopolitical implications.
• Strategies Suggested: Includes secure compute environments, verifying datasets for integrity, continuous validation processes during execution cycles, and runtime assurance methods.
• Target Audience: Governments, enterprises, and developers are encouraged to adopt collaborative efforts for trustworthy AI systems across sectors with critical applications.
• Learning Highlights for Attendees:

– How Zero-trust models enhance security against attacks.
– Techniques like Retrieval-Augmented Generation (RAG), fine-tuning algorithms & guardrails to reduce hallucinations.- Resilient deployment methodologies for foundational AI systems.
– Overview of emerging security standards/frameworks within AI ecosystems.
– Emphasizing the importance of open-source initiatives paired with explainable advancement approaches.

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