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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