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Securing the Future of AI Through Rigorous Safety, Resilience, and Zero-Trust Design Principles
As foundational AI models grow in power and reach, they also expose new attack surfaces, vulnerabilities, and ethical risks. This white paper by the Secure Systems Research Center (SSRC) at the Technology Innovation Institute (TII) outlines a comprehensive framework to ensure security, resilience, and safety in large-scale AI models. By applying Zero-Trust principles, the framework addresses threats across training, deployment, inference, and post-deployment monitoring. It also considers geopolitical risks, model misuse, and data poisoning, offering strategies such as secure compute environments, verifiable datasets, continuous validation, and runtime assurance. The paper proposes a roadmap for governments, enterprises, and developers to collaboratively build trustworthy AI systems for critical applications.
What Attendees will Learn
- How zero-trust security protects AI systems from attacks
- Methods to reduce hallucinations (RAG, fine-tuning, guardrails)
- Best practices for resilient AI deployment
- Key AI security standards and frameworks
- Importance of open-source and explainable AI
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