Most teams come to us mid-problem, not shopping for a service. Pick the line that sounds like your quarter, and we will point you at the fix, the proof it works, and something worth reading on the way.
We red-team your LLM and agent system the way a real attacker would, prompt injection, tool abuse, and data leaks across users, then hand back evidence-backed findings and a remediation roadmap mapped to the OWASP LLM Top 10 and MITRE ATLAS. Fixed-fee, fixed-scope, principal-run.
The AI Security X-Ray is a two-week, fixed-fee point-in-time threat model and adversarial test of one AI system, prompt injection, tool abuse, and data-leakage pathways, mapped to the OWASP LLM Top 10 and MITRE ATLAS. You get severity-ranked findings with remediation and a runbook, with first findings inside 48 hours. The fastest way to know where it can be abused before an attacker does.
We inventory your AI, classify risk, and find the gaps against the framework you care about, the NIST AI RMF, the EU AI Act, or ISO/IEC 42001, then layer it onto the SOC 2 or ISO 27001 program you already run. The result is readiness evidence your auditors, regulators, and enterprise buyers will accept. Readiness and alignment, not certification.
Excessive agency, tool abuse, and confused-deputy chains are the new attack surface, and the MCP supply chain underneath them drifts silently. We review the tool and agentic layer, then apply the same integrity checks shipped in mcp-warden, our open-source MCP supply-chain lockfile and CI gate, to catch drift before it ships.
Unclassified AI governance readiness, pre-deployment AI architecture review and threat modeling, AI and ML supply-chain security review, and a NIST AI RMF mapping for the systems your program is putting into production. The same senior team, with full ownership handed to you. Start with the federal capability.
After the first engagement, a fractional AI compliance officer keeps the risk register, the framework interpretation, and the audit-ready evidence current, reporting to your board and insurer. High-value advisory with a runbook on exit, not a headcount you rent and not a 24/7 SOC.
The tooling we deploy in an assessment is tooling we wrote and open-sourced. The posture we bring to your system is the posture we use on our own.
A principal authored it. An open-source MCP supply-chain lockfile and CI gate that pins an MCP server's declared tool surface into a signed lock and fails CI when that surface drifts. The kind of tooling we deploy in assessments. MIT licensed, on PyPI as mcp-warden-cli, signed releases, 400+ tests.
Inspect mcp-warden on GitHub ↗A multi-model adversarial council we built to pressure-test our own designs. It is the same posture we bring to your AI system: assume the model is hostile and prove otherwise.
Engagements run on a published method, the OWASP LLM Top 10 and MITRE ATLAS, by senior practitioners. No junior hand-off, no rented dashboard. The person who scopes is the person on the keyboard.
We red-team the whole stack an attacker sees, not just the prompt box. Then we hand you evidence-backed findings and a remediation roadmap, mapped to the OWASP LLM Top 10 and MITRE ATLAS. Fixed-fee, fixed-scope, principal-run.
A point-in-time threat model and adversarial test of one AI system, with severity-ranked findings, remediation, and a runbook. First findings inside 48 hours.
Scope the X-Ray →A full adversarial campaign across the system, multi-turn attacks, chained exploits, and agent steering, when a two-week X-Ray is not enough.
Request a scoping call →Ongoing security oversight once the system is live, keeping the red-team harness, the findings backlog, and the AI inventory current as the system changes.
A fixed-fee readiness engagement for teams putting AI into production. We inventory your AI, classify risk, find the gaps against the framework you care about, the NIST AI RMF, the EU AI Act, or ISO/IEC 42001, and layer it onto the SOC 2 or ISO 27001 program you already run. Readiness and alignment, not certification.
AI inventory, risk classification, and a gap assessment against one framework, crosswalked onto your existing SOC 2 or ISO 27001 program, with a prioritized remediation roadmap.
Scope the Snapshot →Recurring ownership of the risk register, framework interpretation, and audit-ready evidence once the readiness work is done, reporting to your board and insurer.
An AI risk and compliance narrative for a specific proposal or task order, mapped to NIST AI RMF, frequently funded as bid and proposal.
We are not a pyramid. There is no junior hand-off, no rented dashboard, and no thesis to push. We pick a narrow problem, the security and governance of the AI you are actually shipping, and we go deep.
Coverage is organized against the OWASP LLM Top 10 and MITRE ATLAS, and governance is mapped to the NIST AI RMF. You can audit the method, not just trust it.
mcp-warden is open-source, MIT, with signed releases, on PyPI as mcp-warden-cli. conclave is the adversarial council we built to pressure-test our own designs.
The five attack surfaces are enumerated above, input and output, retrieval, the tool and agentic layer, the model and supply chain, and runtime and ops. You know exactly what we test before you pay.
The person who runs the senior-led scoping call is the person who delivers the work. No hand-off to a team you never met.
We publish how we test, in detail, including a walkthrough of how we run an OWASP LLM Top 10 assessment.
ReadHow we test the OWASP LLM Top 10Nobody publishes this. We do. If you are looking for one of the engagements below, we will happily refer you elsewhere, and the rest of this site means more because of it.
The same senior practitioners secure and govern AI for commercial teams shipping to enterprise buyers and for federal programs putting AI into production.
Teams shipping RAG apps, copilots, and agent platforms who need to know what is exploitable and prove to enterprise buyers that the AI is governed. SOC 2 / ISO 27001 crosswalk, EU AI Act and ISO 42001 readiness, fixed-fee.
Unclassified AI governance readiness, pre-deployment AI architecture review and threat modeling, AI/ML supply-chain security review, an AI risk register and policy, NIST AI RMF mapping, and an AI governance narrative for proposals (B&P).
Federal capability →Atlanta-based, working with regional SaaS, fintech, and the partner network (MSPs, fractional CFOs, post-funding and M&A readiness). Local senior help on the AI attack surface.
Most firms hide their method and their code. Ours is on GitHub and in the Refinery Report. Read why AI projects stall, what the real ROI looks like, and how we run an OWASP LLM Top 10 assessment.