This guide outlines the emerging risks introduced by large language models (LLMs), which traditional security controls cannot adequately address due to LLMs’ non-deterministic behavior, opaque training data, and susceptibility to prompt injection, poisoning, and data leakage.
It provides a practical, threat-mapped checklist for securing LLMs across the entire lifecycle—covering data input/output protection, model integrity, infrastructure hardening, governance, monitoring, and user access control. As organizations rapidly adopt LLMs, the document emphasizes implementing high-impact safeguards such as prompt filtering, training pipeline security, API hardening, and continuous monitoring. It concludes by highlighting how AI Security Posture Management (AI-SPM) can operationalize these best practices and defend against evolving AI-driven threats.
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