LLM Security Best Practices [Cheat Sheet]

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Key Takeaways

Training data is a threat vectorPoisoned or unvetted datasets can introduce logic flaws or compliance violations.
Your infrastructure is part of the attack surfaceMisconfigured APIs or public cloud assets can undermine even the best model security.
Governance is essentialWithout visibility and policy enforcement, shadow AI and misuse go unchecked.

Is this cheat sheet for me?

This guide is for security teams, AI/ML engineers, DevSecOps practitioners, and GRC leaders tasked with securing generative AI in real-world environments. Whether you're deploying internal copilots or customer-facing chatbots, this cheat sheet gives you clear, actionable steps to reduce risk—fast.

What’s included?

20+ security best practices across five domains: data I/O, models, infrastructure, governance, and access
Real-world attack scenarios (e.g., API abuse, model poisoning, and prompt injection)
Implementation-ready checklists for each control
Guidance on red teaming, threat modeling, and AI policy enforcement

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