AI security boundaries for real applications

AI features introduce new trust boundaries around instructions, data, tools, retrieval and outputs. This guide is a practical engineering reference, not a penetration test, compliance assessment, certification or guarantee that a system is secure.

Prompt and instruction boundaries

Treat model instructions, retrieved content and user input as different trust levels. Do not assume text is safe because it appears inside a prompt.

Sensitive data exposure

Minimize personal, confidential and secret data sent to an AI workflow. Decide what data may leave the application boundary before implementation.

Tool and action permissions

AI-triggered tools should use the minimum permission required, with explicit authorization before sensitive or irreversible actions.

Retrieval trust

Retrieved documents can be outdated, malicious or inappropriate for the current user. Apply authorization and source-quality checks before using them as context.

Output validation

Model output is untrusted application input. Validate structure, destination, business rules and security-sensitive fields before using it.

Human oversight

High-impact outputs need an appropriate review or approval path instead of treating model confidence as a guarantee.

Logging and privacy

Capture enough information to debug and investigate without recording secrets, unnecessary personal data or full sensitive prompts by default.

Provider and model boundary

Document which provider or model receives data, how credentials are protected, and what changes when a provider, model or region changes.

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