AI Security Consulting for LLM Products
AI security consulting 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. The output is designed to support remediation, not just produce a report.
The review can also help teams decide where security controls should live outside the model. Useful controls often include scoped tools, explicit approvals, data minimization, logging rules, retrieval filters and tests that prove a mitigation still works after prompts or model providers change.
This page is maintained by Jonathan R Reed for teams evaluating private AI systems, local model workflows and security-sensitive implementation decisions. The material is written for operators, founders and engineering leads who need plain technical context before they choose vendors, share data or connect AI features to internal tools.
Each engagement is evaluated against the same practical questions: what information must stay private, which users need access, how answers will be checked, what logs are created, what tools the model can use and how the team will verify that the deployed workflow keeps working after handoff.
The emphasis is useful delivery with clear boundaries, tested assumptions, readable documentation and decisions that a technical owner can maintain after launch.