This case study presents PredictionGuard's experience and framework for deploying secure and accurate AI systems in enterprise environments, with a particular focus on managing risks associated with Large Language Models (LLMs) in production. The presentation outlines five major challenges that enterprises face when deploying LLMs, along with practical solutions developed through real-world implementation experience: **Hallucination Management** One of the most critical challenges addressed is the problem of model hallucinations. While many organizations initially attempt to solve this through RAG (Retrieval Augmented Generation) systems, PredictionGuard highlights that this alone is insufficient. They've developed a more robust approach that combines: * Ground truth data insertion through RAG * A specialized factual consistency detection system using fine-tuned models * An ensemble of models like UniEval and BART-score to detect factual inconsistencies * A scoring system that provides quantitative measures of output reliability A real-world example is presented involving a medical assistance application for field medics in disaster relief and military situations, where accuracy is literally a matter of life and death. **Supply Chain Security** The case study addresses the often-overlooked aspect of AI supply chain security. PredictionGuard's approach includes: * Implementation of a trusted model registry system * Verification of model sources and licensing * Hash checking for model files to prevent tampering * Use of industry-standard libraries with appropriate security settings * Careful consideration of third-party code dependencies **Server Infrastructure Security** The presentation emphasizes that LLM deployments ultimately run on servers that need traditional security measures plus AI-specific considerations: * Endpoint monitoring and security protocols * File integrity monitoring * Regular penetration testing * SOC 2 compliance considerations * Proper scaling and resilient infrastructure design **Data Privacy and Protection** A significant portion of the case study focuses on data privacy, particularly in the context of RAG systems and prompt handling. PredictionGuard's solution includes: * Advanced PII detection systems * Configurable filtering options (blocking, stripping, or replacing sensitive data) * Implementation of confidential computing technologies (like Intel SGX/TDX) * Third-party attestation for environment verification * Encrypted memory management **Prompt Injection Protection** The framework includes a sophisticated approach to prevent prompt injection attacks: * Custom-built protective layer * Continuously updated database of prompt injection examples * Ensemble of classification models * Vector search and semantic comparison techniques * Configurable filtering systems **Performance and Latency Considerations** The case study also addresses the critical aspect of maintaining performance while implementing these security measures: * Strategic use of smaller, CPU-based NLP models for auxiliary tasks * Efficient vector search implementations for semantic comparisons * Careful consideration of latency impact for each security layer * Focus on minimizing additional LLM calls **Enterprise Integration Aspects** The framework includes considerations for enterprise integration: * Role-based access control integration * Database security integration (particularly for vector stores) * SIEM integration for security monitoring * Anomaly detection for token usage and server load * Document classification and access management **Human-in-the-Loop and Agent Considerations** The case study concludes with insights on more advanced implementations: * Managed permission systems for autonomous agents * Dry-run approaches for agent actions * Human approval workflows for critical operations * Balance between automation and security Throughout the presentation, there's a strong emphasis on practical implementation and real-world considerations. The framework demonstrates how traditional security practices can be adapted and enhanced for AI systems while introducing new AI-specific security measures. The approach is comprehensive yet flexible, allowing organizations to implement security measures appropriate to their specific use cases and risk profiles. The case study reveals that successful LLM deployment in enterprise environments requires a careful balance between functionality, security, and performance. It's not just about implementing individual security measures, but about creating a cohesive system where different security components work together while maintaining usability and performance.