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.
A practical 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.
This work is useful 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 useful delivery with clear boundaries, tested assumptions, cost-aware model routing, readable documentation and decisions that a technical owner can maintain after launch.