Audit the AI system before it audits you.
I review AI systems the way an attacker would, repair broken automations, build safe ways for assistants to use your product, and validate new ideas on real inputs.
Four engagements. Each ends in something usable.
No generic retainers. Each one produces something you can act on: findings with fixes, a repaired workflow, a working connection, or an evidence-backed go / no-go.
A review of the AI system you already run - prompts, tools, agent permissions, data paths, and failure behaviour - against the ways these systems actually get abused. You get findings ranked by real exposure, with fixes.
Audit and repair one automation that fails, leaks data, or needs too much manual rescue. The engagement ends with a working, tested workflow - the same delivery approach used for Cargenta's freight operations automation across email and Telegram.
Give AI assistants an official way to use your product instead of scraping it from the outside. You get an owned, branded, and trackable connection, backed by per-user access, carefully limited tools, and clear boundaries.
One workflow, tested on your real inputs before you fund a build. You leave with a working slice, a security and cost read, and a go / adjust / stop decision backed by evidence.
Evidence beats a roadmap deck.
Most AI initiatives stall between a promising demo and a system nobody can defend. This work is built to produce proof - a reviewed system or a running slice - inside the engagement.
Security first, not security later
The audit runs before the rollout, not after an incident. Permissions, data paths, and failure behaviour are reviewed as part of the build, not bolted on.
Fast, not theoretical
Working proof inside the engagement - a reviewed system or a running slice - instead of a roadmap document nobody reads.
Offensive-security background
The review is done by someone who looks for the abuse path first: over-scoped tools, injectable context, and anything an agent can reach that it shouldn't.
Built for real stacks
Production cloud environments, existing auth, existing data. Work fits the systems you already run rather than a greenfield ideal.
Vendor-neutral
No platform to resell. Recommendations follow your constraints and your stack, not a licensing quota.
You keep the method
Findings, checks, and templates are handed over so your team can run the next review without me.
Week 1: reviewed on real systems.
Week 2: something shipped.
No slide deck. No roadmap PDF nobody reads. A reviewed system or a scoped workflow, run against real inputs, with enough evidence for leadership to decide the next step.
Straight answers.
What exactly is an AI security audit?
A structured review of a running AI system: what the model can see, which tools it can call, what those tools can do, where data goes, and how the system behaves when it is unsure or attacked. Output is a ranked list of findings with concrete fixes.
What happens in the 5-day discovery sprint?
We narrow the problem, inspect the current system and real inputs, define what success means, and test the risky assumptions. You leave with a written scope of work and a clear recommendation on whether to proceed, adjust, or stop.
Can you repair an automation that already exists?
Yes. I can trace one unreliable or unsafe workflow, fix the failure points, and test it on realistic inputs. The outcome is a working automation with its limits documented, not just a diagnosis.
Why an MCP server instead of a normal API?
It gives assistants an official, trackable way to use your product instead of scraping around it. Underneath, each user gets only the access they should have, and every tool has a clear limit on what it can do.
Do you only work on AI?
No. Most of the work is ordinary engineering - integrations, data handling, permissions, and interfaces. AI is one layer, applied where it earns its place.
What happens after the engagement?
You get the code, the findings, and the known-limits list. Ongoing support is optional and bounded - not a managed service with round-the-clock coverage.
Stop guessing whether your AI system is safe to ship.
Start with five focused days to test the risky assumptions and turn the problem into a written scope of work and a clear next decision.