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

TL;DR : This guide covers 12 actionable security practices for production LLM deployments, mapped to OWASP's LLM Top 10 (2025) and Agentic Top 10 (2026). Each practice includes implementation code, threat context, and prioritization guidance. Enterprise AI security requires more than wrapping an LLM in a firewall. Production deployments face attack vectors that traditional security frameworks don't address: prompt injection, data exfiltration through context windows, embedding inversion, and agent goal hijacking. The OWASP Top 10 for LLM Applications (2025) documents these risks. The OWASP Top 10 for Agentic Applications (2026) adds autonomous system concerns. Together, they define the threat model for secure AI infrastructure. This guide provides 12 actionable practices for LLM security in production. Each practice maps to specific OWASP risks, includes implementation guidance, and provides working code. The Enterprise AI Security Threat Model Enterprise AI security faces threats that