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LLM Security Best Practices [Cheat Sheet]

By • July 21, 2025
LLM Security Best Practices [Cheat Sheet]

After reading this cheat sheet, you can:

Identify and mitigate new threats such as rapid injection, model addiction and use of shadow AI.

Apply practical security controls throughout the LLM lifecycle, from training pipelines to user access.

Build detailed defenses for LLMS including data verification, API hardening and continuous monitoring.

Operate LLM security using policy, threat modeling, and role-based access control.

Key takeoutTraining data is a threat vectorPoisoned or unnetworked datasets can introduce logic defects or noncompliance.Infrastructure is part of the attack surfaceA false API or public cloud assets can undermine even the best model security.Governance is essentialWithout visibility and policy enforcement, Shadow AI and misuse are not checked.

Is this cheat sheet for me?

This guide is for security teams, AI/ML engineers, DevSecops practitioners, and GRC leaders responsible for securing generated AI in real-world environments. Whether you deploy an internal copylot or a chatbot for your customers, this cheat sheet offers clear and practical steps to reduce your risk.

What is included?

Over 20 security best practices across five domains: data I/O, model, infrastructure, governance and access

Actual attack scenarios (e.g. API abuse, model addiction, rapid injection)

Checklist for implementation of each control

Red Team, Threat Modeling, and AI Policy Enforcement Guidance


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