LLM Security Best Practices in 2026: A Checklist
LLM security best practices checklist: prompt injection, data handling, key management, output validation, and governance for AI apps in 2026.
LLM security isn't one fix — it's a set of practices across data, prompts, tools, and keys. Use this checklist to harden any AI application in 2026.
Data handling
- Know what data reaches the model and which providers it goes to.
- Redact PII and secrets before sending to third-party APIs.
- Define retention: log prompts/responses with a clear policy.
- Encrypt at rest and in transit; control who can export logs.
Prompt security
- Treat external content as data, never as instructions.
- Sandbox untrusted text away from system prompts.
- Validate model output against schemas and allowlists.
- Red-team prompt injection regularly.
Key and secret management
- Never hardcode API keys in code or client-side.
- Use a secrets manager or a gateway with central key storage.
- Rotate keys and scope them per team/project.
- Revoke keys instantly on suspicion of exposure.
Tool and agent permissions
- Least privilege: agents can only call what they need.
- Human approval for destructive or external actions.
- Validate tool arguments before execution.
- Log every tool call with inputs and outputs.
Governance and audit
- Map which models process what data.
- Track usage per project, model, and key.
- Set budgets so runaway jobs fail safe.
- Run periodic security reviews of the whole pipeline.
FAQ
What is the biggest LLM security risk?+
Prompt injection and over-privileged tool access. Both let attackers redirect the model into actions or leaks you didn't intend.
Should I send sensitive data to LLM APIs?+
Only if the provider's data policy allows and you've redacted what you can. Prefer self-hosted models for sensitive data where possible.
How often should I review AI security?+
At least quarterly and after major features. Also after every model or provider change, since policies differ.
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