Banks, lenders, healthcare operators, and defense suppliers want the productivity of large language models without sending nonpublic data to a public API. We secure private and self-hosted AI deployments end to end, then own the governance and compliance evidence that an examiner, an auditor, or a customer security review will ask for. Senior-only delivery, fixed-fee, with every engagement leaving you a documented, audit-ready program your own team can run.
Self-hosted LLM compliance is not a single control. It is a deployment pattern: an open-weight model family you run in your own environment, identity and access control on every call, audit logging of every prompt and completion, change control on the model and its prompts, and a mapping from each of those controls to the framework your regulator actually supervises you against. We build that pattern, secure it against the failure modes specific to AI systems, and hand you the evidence package. Physical security, where an engagement needs it, is delivered through a licensed partner and stays a supporting line, not the headline.
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 vCISO and security office for regulated organizations: risk register ownership, policy maintenance, managed vulnerability triage, vendor risk, incident response planning, and quarterly board reporting.
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.
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 |
|---|---|---|
| Pilot | Up to two models in a single environment, core access control, audit logging, and an evidence starter package. 8 to 12 weeks. | $40,000–$45,000 |
| Foundation | High availability, monitoring, full RBAC and ABAC, complete governance documentation, and a compliance-evidence package mapped to your framework. | $75,000–$95,000 |
| Enterprise | Multi-environment, multi-model, or multi-framework programs. Scoped to the estate. Contact for scoping. | Custom |
| Managed retainer | Ongoing operation, monitoring, re-testing, and evidence upkeep of the deployed program. 12-month minimum. | $8,000–$15,000 / mo |
Fixed-fee and scoped in writing before work starts. DSE prepares your program for audit and does not certify; no engagement guarantees passing a specific examination.
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/ernestprovo23/conclave ↗The right question to ask a boutique security partner is what happens if the one person gets hit by a bus. We answer it in writing. DSE is operator-led and senior-only, and every engagement is delivered against a named partner ecosystem so the work is never single-threaded on a single calendar.
Service levels in writing. Same-day reply on business days, and a 24-hour response target for critical incidents on retainer tiers. Every engagement produces standardized, documented artifacts, a risk register, policies, an evidence trail, and a runbook, so any competent security professional can pick up the work. You are buying a program that survives a handoff, not a dependency on one person.
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.
Private AI Security is delivered by Data Science & Engineering Experts, Inc., a senior-only firm established in 2026 serving regulated organizations across financial services, healthcare, and the defense industrial base. The practice combines AWS-certified cloud security architecture, a federal contracting background fluent in the NIST Risk Management Framework and CMMC-aware delivery, and authored open-source AI security tooling.
The same senior practitioner who scopes the work delivers it. We prepare organizations for audit and examination; 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 11-7 for model risk, third-party risk guidance for vendors, fair lending, and prohibitions on unfair, deceptive, or abusive practices. ISO/IEC 42001 is valuable for procurement signaling and board assurance, and a vendor's certificate is an upstream attestation, but it is not how a US prudential examiner reads your own deployment. We build the examiner-facing posture on NIST AI RMF plus SR 11-7 and use ISO 42001 where procurement calls for it.
The Private AI, Secured build runs $40,000 to $45,000 for a Pilot of up to two models in a single environment over 8 to 12 weeks, and $75,000 to $95,000 for a Foundation deployment with high availability, monitoring, full RBAC and ABAC, governance documentation, and a compliance-evidence package. Enterprise programs are scoped to the estate. An optional managed retainer to operate and re-test the program runs $8,000 to $15,000 per month on a 12-month minimum. 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, audit-ready program and a senior owner who can answer the auditor, the board, and the customer security review.
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 banks and fintechs operationalize the four NIST AI RMF functions on top of an existing SR 11-7 model risk program.
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.