We help healthcare, government, financial-services, and other sensitive-data teams adopt AI safely, prove the controls under review, and move critical workloads into private or dedicated environments. Start with a fixed-fee readiness or security sprint, expand into implementation, and keep the program current through retained oversight.
the private AI we build and run for clients is also a product. PrivateStack is our secure, multi-tenant LLM platform, now opening in a controlled, approval-gated beta.
Most teams do not need a giant governance program on day one, and most private AI deals should not start with infrastructure first. We land with a clear readiness package, expand into implementation, and operate sensitive AI systems only when the trust and use case justify it.
Use-case inventory, acceptable-use policy, vendor review, lightweight risk register, owner matrix, and an evidence pack. This is the fastest entry point for startups, growth companies, and regulated teams that need structure before AI use gets messy.
See governance readiness → Read the AI Governance Starter Kit →Workflow design, AI copilots, agent use cases, data integration, control implementation, red-team findings, and remediation plans. The point is to make governance usable inside the systems people actually work in.
See implementation path → See an anonymized implementation case note →Private architecture design, dedicated hosting patterns, air-gapped options where appropriate, MLOps support, monitoring, maintenance, cost controls, and evidence upkeep. This is the premium path after the use case and risk model are clear.
Explore private AI → Read: private AI architecture vs public API →We do not fill the site with invented testimonials, logo walls, or vanity counts. We show proof four ways instead: anonymized operator references where permission exists, shipped-system case studies, sample artifacts and runbooks, and public practitioner work you can inspect yourself.
Where a client quote appears, it stays anonymized and attached to the role and operating context, not a borrowed logo. If attribution is not public, we say “reference on request” and leave it there.
We prefer concrete build proof: what shipped, what controls were implemented, what handoff artifacts existed, and how the system was operated after launch.
See selected work → Regulated SaaS: secure multi-tenant LLM platform →When we cannot name a client, we can still show the work: sample deliverables, managed-operations runbooks, control frameworks, and public tools that reflect how we operate.
Inspect the public footprint → Healthcare: clinical documentation AI framework → Federal: contract intelligence pipeline →Start with the pressure you can name: moving fast without policy, scaling across teams, answering a regulator, or needing private AI infrastructure. Each route leads to a tighter service page or scoping path.
Use-case inventory, acceptable-use rules, vendor review, a lightweight risk register, and a short roadmap workshop. Built for teams adopting AI before the policy catches up.
Startup Launch Pack →Policy set, risk tiering, owner matrix, vendor controls, basic evidence pack, and quarterly governance support for teams with AI already spreading across functions.
Growth governance pack →Healthcare, public-sector, and financial-services teams get framework mapping, risk evidence, and control design tied to HIPAA, NIST AI RMF, GLBA, and program-specific security expectations.
Compare regulated entry paths → Compare the regulated lanes → See the readiness sample deliverable → Healthcare-specific governance → Finserv-specific governance → Federal capability →Private cloud, dedicated environments, on-prem patterns, and managed evidence upkeep for AI systems where privacy, compliance, and vendor control matter.
Private AI security →Federated governance model, committee support, documentation framework, monitoring design, and an audit-ready process for cross-functional AI oversight.
Enterprise control pack →Pre-deployment AI architecture review, AI risk register, NIST AI RMF mapping, and proposal-ready governance narratives for public-sector programs.
Federal AI Readiness Brief →If you are not sure which service name fits, start here. Pick the sentence that sounds most like the conversation happening inside your team.
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. 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 security and quality system you already run. The result is readiness evidence built for auditor, regulator, privacy-office, and enterprise-buyer review. Readiness and alignment, not certification.
Providers, payers, and digital-health teams usually need one fixed-scope first engagement before rollout expands: a Healthcare AI Readiness Snapshot covering AI inventory, risk classification, human review, vendor controls, and the vendor-versus-private-boundary decision for PHI or patient-facing workflow.
We inventory the AI you actually run, classify model risk, and map the gaps to the framework your regulator cares about, SR 26-2, GLBA, NYDFS Part 500, or the NIST AI RMF, then layer it onto the SOC 2 or ISO 27001 program you already run. You get defensible readiness evidence for examiner and board review.
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.
We start with architecture and risk first: what data is involved, which users need access, which models can run where, and what evidence the environment must produce. Then we scope private cloud, dedicated hosting, on-prem, or partner-led managed operations without pretending every buyer needs a custom platform on day one.
Federal and public-sector buyers usually need a named first engagement before a larger delivery path: a Federal AI Readiness Brief covering AI use-case inventory, NIST AI RMF risk classification, system and tool-boundary decisions, and a short readiness roadmap for unclassified programs moving toward production.
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. Explore the full AI security & cyber risk pillar for regulated teams.
We 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, with signed releases and active test coverage.
Inspect mcp-warden →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. For regulated teams, the red team finds what an examiner's third-party-risk review, a fair-lending challenge, or a Reg S-P incident would surface, before any of them do. Findings map to GLBA, NYDFS Part 500, and the NIST AI RMF, not just the OWASP LLM Top 10.
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.
The first sale should be easy to understand and easy to scope. We use fixed-fee governance packages to create the AI inventory, policy set, vendor controls, owner matrix, evidence pack, and roadmap that make implementation or private AI work defensible later.
Use-case inventory, acceptable-use policy, vendor review, lightweight risk register, and one roadmap workshop for teams adopting AI quickly.
See the Launch Pack →Policy set, risk tiering, owner matrix, vendor controls, basic evidence pack, and practical governance support before AI use spreads across teams.
See the Growth Pack →Federated governance model, committee support, documentation framework, monitoring design, and audit-ready process for complex AI oversight.
See enterprise control →Private AI and managed operations are higher-value offers, but they require trust, scope discipline, and delivery capacity. We sell the path in stages: architecture first, implementation second, managed operations once the system and risk model are clear.
Data-flow review, hosting pattern, model and access design, governance evidence requirements, and an implementation plan your leadership can approve.
Explore private AI →Dedicated or isolated AI environment, identity and access controls, model gateway, logging, cost controls, data boundaries, and security testing before launch.
Ongoing monitoring, maintenance, retesting, evidence upkeep, model/vendor change review, and governance support for systems already in production.
GLBA information security program templates, the full NYDFS Part 500 gap assessment, the Reg S-P broker-dealer workbook, the SEC AI exam guide for advisers, a master control matrix across GLBA, NYDFS, CCPA and the NIST AI RMF, and a 2023 to 2026 regulatory deadline tracker. Take what you need to your next risk committee meeting.
Not ready for a call? Score your AI governance readiness in 10 questions. Take the scorecard →
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 governance, security, implementation, or operation 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 10We are useful when the work requires technical testing, control mapping, and accountable remediation. We are not the right fit for certificates, dashboard reselling, staff augmentation, or compliance theater.
The same senior practitioners govern, secure, implement, and operate AI for startups, growth companies, regulated financial institutions, and federal programs putting AI into production.
For founders and operators who need acceptable-use rules, vendor review, an AI use-case inventory, and a realistic roadmap before AI work becomes scattered across every team.
Startup Launch Pack →For teams whose AI use now has owners, vendors, policies, model risk, customer evidence, board attention, or examiner pressure attached. We create the control map and help turn it into operating practice.
AI governance readiness → Financial-services library →For organizations that need dedicated hosting, private architecture, stronger data boundaries, or unclassified AI governance review before putting sensitive workloads into production.
Private AI security → Federal capability →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.