AI Security Review for LLM Products
AI security review from Hello.World Consulting focuses on the failure modes that appear when language models connect to private data, internal tools and customer workflows.
The review looks at prompt injection, indirect prompt injection, unsafe tool calls, authorization gaps, data leakage, excessive logging, weak evals and unclear incident ownership.
The review maps what the model can see, what it can do, where sensitive information can move and which controls exist outside the model. Findings are written for engineering teams, with prioritized fixes, affected components, retest criteria and notes about residual risk.
The point is to separate model behavior from control failure. A strange answer is not always a security issue, but data crossing the wrong boundary, a tool acting without review or a log storing sensitive content is.
A useful review also explains which issues are product risks, which are policy gaps and which need engineering controls before the feature can be trusted with real users.
This work matters most before launch, after a prototype has started touching production-like data or when a team needs a clear security baseline for an AI feature.
This page is maintained by Jonathan R. Reed for teams evaluating AI enablement, private workflows, existing-tool optimization and security-sensitive implementation decisions.
Each engagement is evaluated against practical questions: which tools and subscriptions already exist, what information must stay private, which users need access, how answers will be checked, what the workflow costs and how the team will verify that the deployed system keeps working after handoff.
The emphasis is delivery with clear boundaries, tested assumptions, cost-aware model routing, readable documentation and decisions that a technical owner can maintain after launch.