Use AI for clarity. Keep the evidence in charge.

Separate the two meanings of AI API security

An AI-assisted API review uses a model to help organize or explain assessment evidence. A review of an LLM-powered API examines an application that uses a model. These are different tasks. Define which one you are performing, what evidence is available, and who makes the final decision.

This resource focuses on practical review design. It does not offer a hosted LLM scanner, connect to a model provider, or claim that a prompt can certify an application. Begin with a narrow, verifiable task such as summarizing sanitized findings.

Give the assistant a bounded job

Ask for finding identifiers, expected behavior, observed behavior, evidence references, and missing information. Require absent facts to remain absent. Separate classification suggestions from confirmation and remediation proposals from verified repairs.

OpenAI's safety guidance recommends human review where practical and access to the information needed to verify outputs. Apply that principle regardless of the assistant used: a reviewer should be able to compare the draft with the original evidence.

Treat model names as workflow choices, not integrations

Teams may evaluate OpenAI and ChatGPT, Anthropic and Claude, Grok, Cursor, or other tools. This site does not connect to those products or claim partnerships, identical capabilities, or current feature compatibility. Check your chosen product's documentation and approved data-handling arrangements before sharing assessment material.

Redact before the model sees the report

Remove credentials, session cookies, unnecessary response bodies, and personal records. Preserve useful relationships with stable synthetic labels. The reviewer needs to understand the boundary between Tenant A and Tenant B, not the real identities behind those labels.

Keep instructions separate from source material. An application response or document may contain text that looks like a command. Treat it as evidence, and avoid giving a summarization workflow tools it does not need.

Add tools only after authority is designed

A bot that drafts a ticket and an agent that executes network checks have different permissions and consequences. Enforce approved targets, operation limits, and stop conditions outside the model. Make consequential actions subject to explicit review.

Explore the six copyable security prompts, read the agent guardrails, or follow the evidence-based triage article. Start with assistance that is easy to verify before considering more autonomy.

Primary references

Read the documentation that matches your tool, framework, and deployment. These sources provide context; they do not establish coverage of your environment.

• OpenAI safety best practices
• OWASP LLM06:2025 — Excessive Agency