LLM Safeguards: Security Privacy Compliance Anti Hallucination: Daniel Whitenack
Dec 31, 2024 · 34:10
Daniel Whitenack of Prediction Guard outlines a practical checklist for deploying secure and accurate LLMs in enterprise, addressing hallucination, supply chain vulnerabilities, flaky model servers, data breaches, and prompt injection. He proposes a factual consistency model fine-tuned to detect inconsistencies between AI output and ground truth, rather than relying solely on RAG. For supply chain risks, he recommends a trusted model registry and industry-standard libraries. Data breaches are mitigated via PII detection filters and confidential computing (e.g., Intel SGX) to encrypt server memory. Prompt injection is countered with a custom firewall layer using ensembled classification models. In Q&A, he discusses latency trade-offs using smaller NLP models, pre-production testing for visibility, data access controls via role-based database queries, SIEM integration for monitoring new artifacts like model caches, and additional challenges with agents such as excessive agency, proposing dry-run approvals.
