This guide provides a practical, lifecycle-based framework for securing large language models, addressing risks unique to AI such as prompt injection, data leakage, model poisoning, and API abuse. It organizes security across five domains: data input/output, models and code, infrastructure, governance, and user access. Recommended practices include prompt validation, dataset auditing, model integrity checks, API hardening, continuous monitoring, and strict RBAC controls. It also emphasizes the need for AI-specific governance and threat modeling, as traditional security tools are insufficient for LLM risks. The key takeaway is that securing LLMs requires a holistic, end-to-end approach—often operationalized through AI security posture management (AI-SPM).

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