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Explore the critical security and privacy challenges facing Large Language Models in this comprehensive conference talk that examines vulnerabilities and defensive strategies for LLMs like GPT-4, Claude, and Gemini. Learn about the unprecedented risks organizations face when deploying LLMs for sensitive tasks such as processing medical records and analyzing financial documents. Discover the evolving landscape of LLM security through real-world case studies that demonstrate both attack vectors and proven defensive techniques. Gain practical implementation guidance using popular security tools including NVIDIA's NeMo Guardrails, LangChain's security tools, and Microsoft's guidance library. Focus on securing fine-tuned and domain-specific LLMs with live examples and hands-on demonstrations. Understand how to establish effective guardrails and protective measures for AI systems handling sensitive data across various industries and use cases.
