DSE is an AI governance and AI security consulting firm for regulated industries: healthcare, government, and financial-services teams that need to prove their AI systems are controlled, not just working. We build the inventory, policy, risk tiers, and technical testing evidence that survive an examiner, a board, or an enterprise buyer's review, then move sensitive workloads into private AI environments when the risk justifies it. Every engagement starts fixed-fee and scoped in writing, never an open-ended retainer.
We help healthcare, government, financial-services, and other sensitive-data teams in three ways. We help them 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. Then keep the program current through retained oversight.
The private AI we build and run for clients is also a product. PrivateStack, our governed AI workspace platform, runs open-weight models like Llama, Mistral, and DeepSeek. It logs every request for export. It costs $99 per user per month, flat.
Most teams do not need a giant governance program on day one. Most private AI deals should not start with infrastructure first. We land with a clear readiness package. We expand into implementation. We 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. They 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. It has to work inside the systems people actually use.
See implementation path → See an anonymized implementation case note →Private architecture design, dedicated hosting patterns, and air-gapped options where appropriate. Plus 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. First, anonymized operator references where permission exists. Then shipped-system case studies, sample artifacts and runbooks, and public practitioner work you can inspect yourself.
Where a client quote appears, it stays anonymized. We attach it 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 we hand back evidence-backed findings and a remediation roadmap. It is 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 engagement. It is a point-in-time threat model and adversarial test of one AI system: prompt injection, tool abuse, and data-leakage pathways. The work is mapped to the OWASP LLM Top 10 and MITRE ATLAS. You get severity-ranked findings with remediation and a runbook. It is the fastest way to know where it can be abused before an attacker does.
We inventory your AI, classify risk, and find the gaps. We check against the framework you care about: the NIST AI RMF, the EU AI Act, or ISO/IEC 42001. Then we 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. That is a Healthcare AI Readiness Snapshot. It covers 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. We check against the framework your regulator cares about: SR 26-2, GLBA, NYDFS Part 500, or the NIST AI RMF. Then we 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. The MCP supply chain underneath them drifts silently. We review the tool and agentic layer. Then we apply the same integrity checks shipped in mcp-warden, our open-source MCP supply-chain lockfile and CI gate. That catches 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. We do not pretend 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. That is a Federal AI Readiness Brief. It covers 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 takes over. They keep the risk register, the framework interpretation, and the audit-ready evidence current. They report to your board and insurer. It is 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. It is an open-source MCP supply-chain lockfile and CI gate. It pins an MCP server's declared tool surface into a signed lock. It 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. It finds them first. 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. For when a two-week X-Ray is not enough.
Request a scoping call →Ongoing security oversight once the system is live. We keep 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. They create the AI inventory, policy set, vendor controls, owner matrix, evidence pack, and roadmap. That work makes implementation or private AI defensible later.
Use-case inventory, acceptable-use policy, vendor review, lightweight risk register, and one roadmap workshop. Built for teams adopting AI quickly.
See the Launch Pack →Policy set, risk tiering, owner matrix, vendor controls, and a basic evidence pack. Plus 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, and managed operations once the system and risk model are clear.
Data-flow review, hosting pattern, and model and access design. Plus governance evidence requirements and an implementation plan your leadership can approve.
Explore private AI →A dedicated or isolated AI environment with identity and access controls, a model gateway, and logging. Plus cost controls, data boundaries, and security testing before launch.
Ongoing monitoring, maintenance, retesting, and evidence upkeep. Plus model/vendor change review and governance support for systems already in production.
The AI Governance Operating Stack is a free, 100% client-side toolkit. It is built on one shared, browser-local AI register. Nothing you enter is uploaded. Build your AI inventory, tier the risk, score a Gen-AI use case, and get cited answers drawn only from DSE's published writing. No sign-up, no upload, no paywall.
Add each AI system once: type, owner, data sensitivity, and frameworks in scope. You get a computed risk tier, the controls it calls for, and a transparent score. It exports to CSV and JSON, and feeds the rest of the stack.
Open the wizard →Score a generative-AI use case across hallucination, prompt injection, data leakage, third-party model dependency, and human-oversight gaps. Exports a board-ready Markdown summary.
Score a use case →Ask a governance, model-risk, or finserv-compliance question. You get a concise answer drawn only from DSE's published writing. Every claim links to the exact article behind it. A readiness aid, not legal advice.
Ask a question →Twelve free workbooks. They cover GLBA information security program templates, the full NYDFS Part 500 gap assessment, the Reg S-P broker-dealer workbook, and the SEC AI exam guide for advisers. You also get a master control matrix across GLBA, NYDFS, CCPA, and the NIST AI RMF, plus 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. Then we go deep.
Coverage is organized against the OWASP LLM Top 10 and MITRE ATLAS. 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. They are 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. They serve startups, growth companies, regulated financial institutions, and federal programs putting AI into production.
For founders and operators who need acceptable-use rules, vendor review, and an AI use-case inventory. They also need a realistic roadmap before AI work becomes scattered across every team.
Startup Launch Pack →For teams whose AI use now has owners, vendors, policies, and model risk attached. It may also carry customer evidence, board attention, or examiner pressure. 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, or stronger data boundaries. Some need unclassified AI governance review before putting sensitive workloads into production.
Private AI security → Federal capability →Straight answers on cost, scope, regulated fit, and what we do not claim.
DSE helps regulated teams do three things with AI: adopt it safely, prove the controls hold up under review, and move sensitive workloads into private AI environments. Most engagements start with a fixed-fee readiness or security sprint, then expand into implementation or retained oversight as the program matures.
Engagements are priced as fixed fees, not hourly billing, and confirmed in writing after a scoping call. Non-binding market-estimate ranges run from about $1,500 for a small-business security posture check to $300,000 or more for a full implementation or private AI build. See the full breakdown on the pricing page.
Yes. Healthcare, financial services, and federal or public-sector programs are a primary focus, alongside growth and enterprise teams outside regulated industries. Regulated engagements map to the frameworks that already apply: HIPAA, NIST AI RMF, SR 26-2, GLBA, NYDFS, and CMMC-aware federal requirements.
No. DSE provides readiness and technical evidence, not certification. We are not an accredited certification body and do not issue ISO/IEC 42001 certificates or certify EU AI Act or NIST AI RMF compliance. We get you ready and assemble the evidence; the certificate, where one exists, comes from the accredited body.
DSE is a small firm of senior practitioners. The people who scope the engagement are the people who do the work; there is no junior bench running the actual testing or governance build. Engagements are fixed-fee and scoped in writing before work starts, not open-ended time-and-materials retainers.
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.