Growth, regulated, and enterprise teams want useful AI without sending sensitive data into systems they cannot explain or govern. We design private AI architecture, deploy secure self-hosted or isolated LLM patterns, and support managed AI operations with monitoring, maintenance, change review, and evidence your team can defend.
Private AI is not a single control. It is a deployment pattern: a model family matched to your data boundary, identity and access control on every call, audit logging of every prompt and completion, change control on the model and its prompts, and an operating model for monitoring, maintenance, and vendor or model changes. We build that pattern, test it against AI-specific failure modes, and hand you the evidence package.
A secured self-hosted or private LLM deployment: model selection, infrastructure, RBAC and ABAC, audit logging, model change control, and a compliance-evidence package mapped to your framework.
A retained senior owner for the AI-specific risk surface: system inventory, AI risk register, policy and evidence upkeep, exception decisions, and board-ready reporting as the private AI program changes.
A fixed-fee diagnostic that inventories your AI systems, classifies risk, and maps each one to NIST AI RMF and the supervisory expectations you answer to, so private AI ships on a defensible foundation.
Secure LLM deployment for financial services and healthcare is an architecture problem before it is a policy problem. Here is what a Private AI, Secured engagement actually stands up, and how each piece becomes compliance evidence.
Open-weight families such as Llama, Mistral, and Qwen, served in your own VPC or on-premise on a governed inference stack, so prompts and completions never leave your control boundary. No public API egress for nonpublic personal information or PHI.
Private deployment in your AWS account or data center: isolated networking, secrets management, encryption in transit and at rest, and a retrieval layer that keeps your documents inside the boundary. Repeatable infrastructure as code, not a one-off.
Role-based and attribute-based access control on every model call and every document the retrieval layer can reach, so access scope is the blast radius and least privilege is enforced, not assumed. Tied to your existing identity provider.
Every prompt, completion, tool call, and retrieved document logged and attributable to a user, with tamper-evident retention. This is the record SOC 2, the HIPAA Security Rule, and a federal audit all expect you to be able to produce.
A documented change process for the model, the system prompts, and the retrieval corpus, with versioning and approval, so a model update is a controlled change under your existing change-management discipline rather than a silent drift in behavior.
Each control mapped to SOC 2 criteria, the HIPAA Security Rule safeguards, GLBA obligations for nonpublic personal information, and CMMC practices for the defense industrial base, so one control set answers multiple frameworks. Document once, tag twice.
Prefer to start from a product instead of a ground-up build? The same pattern ships as PrivateStack, our governed AI workspace platform: open-weight models, every request logged and exportable, and an Enterprise BYOC tier deployed inside your own AWS or Azure account.
Securing a private model is not finished when it is deployed. We red-team the deployment against the failure modes specific to LLM systems, prompt injection, tool and agent abuse, retrieval poisoning, and data-leakage paths, mapped to the OWASP Top 10 for LLM Applications and the MITRE ATLAS threat model. The point of a private deployment is to keep your data inside your boundary. Testing is how we prove the boundary holds.
| Tier | Scope | Investment |
|---|---|---|
| Private AI Architecture Brief | Data-flow review, hosting pattern, model and access design, governance evidence requirements, and an implementation plan leadership can approve before a build. | $15,000–$35,000 |
| Private AI Stack | Dedicated or isolated AI environment, identity and access controls, model gateway, logging, cost controls, data boundaries, and security testing before launch. Complex regulated or multi-environment programs are scoped to the estate. | $50,000–$150,000 for lighter deployments |
| Managed AI Operations | Ongoing monitoring, maintenance, re-testing, evidence upkeep, model/vendor change review, and operations support for systems already in production. | $5,000–$25,000+ / mo |
Fixed-fee and scoped in writing before work starts. Managed AI Operations is the runtime and evidence-upkeep lane for private AI systems; Managed AI Governance and vCISO work are the governance-owner lane. DSE prepares your program for audit and does not certify; no engagement guarantees passing a specific examination. DSE does not operate a 24/7 SOC or MDR; continuous monitoring, where required, is delivered through a vetted partner you contract.
Private AI runs on cloud and on-premise infrastructure built by AWS-certified architects: isolated networking, IAM and least-privilege design, secrets management, and encryption as a default, not an afterthought.
A federal practice fluent in the NIST Risk Management Framework, authority-to-operate documentation, and CMMC-aware delivery for the defense industrial base, where controls are not optional and an audit clock is always running.
We publish the security tooling behind the practice: a multi-model adversarial review CLI and an MCP supply-chain integrity gate, both public.
github.com/DataScience-EngineeringExperts/conclave ↗We document ownership, escalation, partner responsibilities, evidence upkeep, and handoff expectations in writing. Where private AI needs legal, MSP, monitoring, or specialized infrastructure support, those responsibilities are named so the program is not dependent on a single calendar or an undocumented handshake.
Service levels in writing. Retainer tiers define response expectations, maintenance cadence, evidence refreshes, model/vendor change review, and escalation paths. Every engagement produces documented artifacts, a risk register where applicable, policies, an evidence trail, and a runbook, so the program survives handoff.
This page uses the same proof pattern we expect clients to trust: anonymized operator references where permission exists, deployment artifacts and runbooks that show what got built, and public practitioner work that demonstrates the security depth behind the offer.
When we use a quote or a delivery reference, it stays tied to role, environment, and problem shape. If public attribution is not approved, we keep it anonymized or say “reference on request.”
Private AI proof should show the artifact trail: architecture diagrams, logging model, change control, runbooks, evidence maps, and the operating cadence after launch.
Where client confidentiality limits attribution, we rely on public open-source AI security work and detailed technical writing instead of broad trust language.
A practitioner checklist for scoping a secure self-hosted or private LLM deployment before you build, covering the controls a HIPAA, GLBA, SOC 2, or CMMC review will ask about. It is a self-assessment, not a certification.
Also available: a private AI decision matrix for teams deciding whether a public API path is still sufficient or whether a stronger private control boundary is justified.
A one-page, shareable matrix for internal review: when a public model API is still sufficient, when a private control boundary is justified, the minimum evidence a private AI system should produce, and the signals that say "not yet." Built to circulate before you commit budget.
Want the reasoning behind it? Read Private AI Architecture vs Public API or scope a path on the Private AI Stack.
Private AI work is delivered by Data Science & Engineering Experts, Inc. for teams that need more control over model access, data boundaries, logging, cost, and operational evidence. The practice combines cloud security architecture, AI governance, federal contracting fluency, and authored open-source AI security tooling.
We prepare organizations for review, audit, examination, and internal governance; we do not certify, and we do not guarantee any audit or examination outcome.
It is the set of controls that let you run a large language model inside your own environment and prove to an auditor or examiner that the deployment meets your obligations. In practice that means keeping prompts, completions, and your documents inside your control boundary, enforcing access control on every model call, logging everything in an attributable and tamper-evident way, controlling changes to the model and its prompts, and mapping each control to the framework you answer to such as SOC 2, the HIPAA Security Rule, GLBA, or CMMC.
For regulated data, the deciding factor is the control boundary. A public API sends your prompt, and often your retrieved documents, to a third party you do not control, which is a problem for nonpublic personal information under GLBA and protected health information under the HIPAA Security Rule. A private or self-hosted deployment keeps the model, the data, and the audit trail inside your perimeter, so the evidence you produce describes a system you actually govern.
Each control in the deployment is tagged to the criteria that apply: access control and logging map to SOC 2 common criteria and the HIPAA Security Rule technical safeguards, data-boundary controls map to GLBA obligations for nonpublic personal information, and the full control set maps to CMMC practices for defense industrial base work. One control operated once can answer multiple frameworks, which is the document-once, tag-twice principle. DSE prepares the evidence; we do not issue certifications.
No. US examiners supervise AI through the supervisory guidance you already answer to: SR 26-2, a non-binding guidance, for in-scope model risk, plus third-party risk guidance for vendors, fair lending statutes, and UDAP prohibitions. Generative and agentic AI fall outside SR 26-2's scope but remain subject to those same legal and regulatory regimes, and the agencies have signaled a forthcoming AI-specific request for information that will address them directly. ISO/IEC 42001 is a voluntary management-system standard, and no US prudential guidance currently designates it as an examiner benchmark for your deployment. We build the examiner-facing posture on NIST AI RMF plus SR 26-2 and use ISO 42001 where procurement calls for it.
Private AI starts with an Architecture Brief at $15,000 to $35,000. Lighter Private AI Stack deployments typically run $50,000 to $150,000, while complex regulated or multi-environment programs are scoped to the estate. Managed AI Operations runs $5,000 to $25,000+ per month for monitoring, maintenance, re-testing, evidence upkeep, and model/vendor change review. Every fee is fixed and scoped in writing before work starts.
No, and we will not claim otherwise. DSE prepares your private AI program for audit and examination and assembles the evidence a reviewer expects, but no engagement guarantees passing a specific examination or avoiding enforcement. What you get is a defensible, documented, review-ready program with ownership, escalation, and handoff expectations in writing.
The practitioner deep-dive behind this page: the architecture, controls, and framework mapping for secure self-hosted AI in finserv and healthcare.
Read the guide →How to choose the right control boundary for sensitive data, logging, vendor dependence, operational evidence, and the cases where you should not buy private AI yet.
Read the guide →A shareable matrix for internal review: public API versus private control boundary, minimum evidence, and the signals that justify a private stack.
Get the matrix →What has to be monitored, maintained, reviewed, and documented after a private AI system goes live.
Read the runbook →How banks and fintechs operationalize the four NIST AI RMF functions on top of an existing SR 26-2 model risk program (which replaced SR 11-7).
Read the guide →Bring us the model you want to run and the data you cannot expose. We will scope a private deployment, secure it against the failure modes specific to AI, and own the governance and evidence so you can move fast without flying blind. No pitch, just a scoped path.