Secure LLM Operations: Compliance and Safety in LLMOps

Deploying Large Language Models into production introduces unique security vulnerabilities, from prompt injection to sensitive data leakage. Navigating these risks requires a structured, compliant approach to LLMOps that ensures safety without compromising performance. This text-based course guides you through establishing robust, layered defenses for your AI systems. You will transition from understanding basic security concepts to implementing practical guardrails and compliance strategies that protect your users and your organization's data. What you'll learn: - Understand the fundamental security risks in LLMOps, including the OWASP Top 10 vulnerabilities for LLMs. - Implement active guardrails to detect and mitigate prompt injection, jailbreaking, and toxic outputs. - Secure Retrieval-Augmented Generation (RAG) pipelines and vector database integrations against unauthorized data access. - Apply data privacy best practices, including anonymization and safe handling of personally identifiable information (PII). - Establish continuous monitoring, logging, and evaluation workflows for compliance and responsible AI operations. The course begins with foundational definitions of LLM vulnerabilities before guiding you through defensive architecture, policy enforcement, and production monitoring. It is designed for beginners in AI engineering, security compliance, or system administration, requiring no prior security experience. Read, practice with structured examples, and confidently secure your production LLM deployments today.