Enterprise AI Security: 12 Best Practices for Deploying LLMs in Production

The guide provides a comprehensive overview of 12 actionable security practices specifically designed for securing Large Language Model (LLM) deployments, aligning them with OWASP's LLM Top 10 (2025) and Agentic Top 10 (2026) risk frameworks. These practices address unique attack vectors not covered by traditional security measures, such as prompt injection, data exfiltration, and agent goal hijacking, emphasizing the need for robust AI infrastructure security. Each practice is detailed with implementation guidance, code examples, and threat contexts, underscoring the importance of input validation, output filtering, access control, authentication, audit logging, and runtime monitoring. The guide also highlights the need for secure retrieval-augmented generation (RAG) pipelines, data residency compliance, and model supply chain verification, advocating for a defense-in-depth approach to mitigate potential security risks in AI systems.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.