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Secure AI systems are built by controlling what crosses trust boundaries. Anthale gives you a runtime policy layer at those boundaries, but your application still owns authorization, approvals, validation, and response handling.

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The boundary model

An AI system becomes risky when one kind of content is treated as if it carried more authority than it should. In practice that usually happens at six boundaries:
  • user input entering the model on the input path,
  • retrieved context before it is appended to a prompt,
  • memory or conversation state before it is replayed,
  • tool output before it is trusted or fed back into the model,
  • model output before it reaches a user or another system,
  • side effects before the application executes a tool action.
These boundaries matter whether you are building a chatbot, a RAG system, an internal copilot, or a multi-step agent.

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The control stack

No single control solves the whole problem. Safer systems usually combine:
  • business authorization and identity checks,
  • Anthale policy evaluation at content boundaries,
  • least-privilege tool permissions,
  • deterministic validation for structured outputs and arguments,
  • approval flows for high-impact actions,
  • logging and review for runtime signals and incidents.
Anthale covers the policy-evaluation layer. Your application still decides who can retrieve a document, call a tool, approve a payment, or send an email.

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Where Anthale fits

Anthale is most useful where risky content moves between actors or systems. That includes model input, retrieved context, tool output, and final responses. Anthale evaluates the policy you configure and returns one action: allow, detect, redact, or block. That runtime decision makes the surrounding controls easier to keep consistent. You do not need each service to guess how prompt injection, data leakage, or unsafe links should be handled at the last minute.

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How to use this docs area

Use Secure AI Systems Overview for the task-oriented guides. Then move into the guardrail deep dives when you need a more detailed threat model:

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Move from concept to implementation

If you want an AI coding agent to review and harden code for prompt injection and unsafe boundaries while it works, use Prompt Injection Hardening Skill. If you want live documentation lookup inside an AI client, use Documentation MCP.

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Next steps

Continue with Secure AI Systems Overview or How Anthale Works.
Last modified on April 12, 2026