Regulated Enterprise AI
AI Infrastructure Consulting for Regulated Enterprises
In finance, healthcare, and the public sector, AI projects don't usually stall on the model — they stall on compliance and governance gaps that surface right before launch. I design AI infrastructure that's built to satisfy security review and audit from the start, not retrofitted after the fact.
An AI infrastructure consultant for regulated enterprises designs GPU-accelerated AI platforms — usually on OpenShift AI or Kubernetes — that meet the data isolation, audit-logging, and access-control requirements finance, healthcare, and public-sector organizations need before AI reaches production. Luca Berton, AI & Cloud Advisor, Docker Captain, and former Red Hat engineer with 18+ years of enterprise infrastructure experience, designs these platforms, specializing in multi-tenant GPU orchestration and MLOps governance aligned to SOC 2 and ISO 27001 controls.
What regulated-enterprise AI infrastructure requires
Five requirements come up in almost every regulated-enterprise AI engagement, whether the client is a bank, a hospital system, or a public-sector agency: data residency and isolation between tenants, an audit trail that survives a real review, role-based access control enforced by the platform rather than by convention, a model governance and approval workflow, and freedom from single-vendor lock-in. Get these wrong and the platform either fails an audit or never gets approval to go live; get them right and governance becomes a design property of the platform, not a process bolted on afterward.
Common gaps
- ❌ Tenant data and models share one cluster with no isolation boundary between business units
- ❌ Inference logs live only in ephemeral pod stdout, gone before an auditor ever asks for them
- ❌ Every data scientist has standing admin access to production model endpoints
- ❌ Models reach production because someone had kubectl access, not because anyone signed off
- ❌ The platform is hard-wired to one cloud's managed AI service with no exit path
What a compliant platform looks like
- ✓ Namespace-per-tenant isolation on OpenShift AI, enforced with network policies
- ✓ Centralized, immutable audit logging feeding a dedicated evidence store with defined retention
- ✓ RBAC scoped by namespace, enforced through SSO/OIDC group mapping — not shared credentials
- ✓ A model registry with a documented approval workflow gating every production promotion
- ✓ A Kubernetes-based architecture portable across on-prem, private, and public cloud
Reference architecture
Six layers, from the request hitting the network to the evidence an auditor eventually asks for.
Ingress / API gateway
Every request enters through an authenticated gateway that terminates TLS, applies rate limits, and identifies the calling application or user — the same front-door pattern used for any production API, just sitting in front of a model endpoint instead of a REST service.
Guardrails / policy layer
Before a prompt reaches a model, a policy layer checks it against rules — PII redaction, prompt-injection filtering, topic restrictions appropriate to the use case. Responses pass back through the same layer before reaching the caller, so both input and output are checked, not just one side.
Model serving
vLLM or TGI serves the model itself, running on OpenShift AI or RHEL AI with GPU scheduling handled through the NVIDIA GPU Operator. Autoscaling and MIG-based partitioning keep GPU cost proportional to actual traffic instead of static over-provisioning.
Data layer
Each tenant's data — prompts, retrieved context, fine-tuning datasets — lives in its own isolated namespace or database schema, encrypted at rest, with scoped service accounts rather than shared credentials. This is the boundary that keeps one business unit's data from leaking into another's model context.
Observability
Prometheus scrapes latency, throughput, GPU utilization, and error-rate metrics; Grafana turns them into dashboards and alerts. This is also where drift signals — degrading output quality, shifting input distributions — get surfaced before they become a customer-facing incident.
Audit log / evidence store
Every inference request, model version, access grant, and policy decision is written to an immutable, centrally retained log — separate from application logs, with its own retention policy. This is the artifact that turns "we think we're compliant" into "here's the evidence," and it's usually the single component missing when a regulated enterprise first asks for help.
Mapping platform controls to SOC 2 and ISO 27001
| Control area | SOC 2 / ISO 27001 reference | Platform control | Evidence artifact |
|---|---|---|---|
| Logical access control | SOC 2 CC6.1 / ISO 27001 A.8 | RBAC + namespace isolation on OpenShift AI | Access review export |
| Monitoring & logging | SOC 2 CC7.2 / ISO 27001 A.8.16 | Prometheus/Grafana + centralized audit log | Log retention report |
| Change management | SOC 2 CC8.1 / ISO 27001 A.8.32 | GitOps-managed model/config deployment | Git history + approval record |
| Model governance | ISO 27001 A.5.1 (policy) | Model registry + approval workflow | Model card + sign-off record |
| Incident response | SOC 2 CC7.3-CC7.5 / ISO 27001 A.5.24-A.5.28 | Runbooks + alerting on drift/anomalies | Incident log |
This maps platform controls to common framework areas — it is not a certification and does not replace a formal audit.
Engagement phases and rough timeline
This follows the same three-phase engagement model used across every service on this site — Discovery & Scorecard, Build & Implement, Measure & Handover — scoped specifically to a regulated-enterprise AI platform.
Discovery & Scorecard
2-4 weeks
Assess current infrastructure, data, and governance maturity against the requirements above. You get a scorecard with gaps, risks, and quick wins before any build work starts.
Pilot / reference architecture build
4-8 weeks
Hands-on delivery of the six-layer reference architecture on one scoped, non-critical workload — proving the isolation, RBAC, and audit logging hold up under real traffic.
Production hardening & handover
8-12 weeks
Extend the pilot to production scale, map controls to SOC 2/ISO 27001, and hand over the platform, runbooks, and audit evidence to your team. No vendor lock-in, no dependency.
Common mistakes
- ✗
Bolting governance on after the platform is already in production, instead of designing audit logging and approval workflow in from day one.
- ✗
Treating model approval as a one-time gate rather than a continuous process that revisits a model as it drifts or as the underlying data changes.
- ✗
Building on a single cloud's managed AI service with no portability plan, then discovering the lock-in only when a security review or contract renegotiation forces the issue.
- ✗
Skipping chargeback and cost attribution, so GPU spend stays invisible until the monthly bill arrives and nobody can explain which team drove it.
- ✗
Giving every data scientist standing admin access to production model endpoints because RBAC felt like something that could wait until "later."
- ✗
Assuming an in-house Kubernetes team can absorb AI-specific governance patterns without dedicated design time, then discovering the gap during the first compliance review instead of before it.
When to bring in outside help
Most platform and DevOps teams already have strong Kubernetes skills — that's rarely the gap. What's often missing is the AI-specific pattern: how to structure multi-tenant GPU scheduling, how to design an audit trail that satisfies a SOC 2 or ISO 27001 reviewer, or how to build a model approval workflow that doesn't just slow everyone down. Conversely, teams with strong MLOps or data science backgrounds sometimes lack the platform engineering depth to harden a Kubernetes cluster for production traffic and compliance.
Outside help earns its keep at two specific points: designing the reference architecture the first time, and getting the first regulated workload through production hardening into an audit-ready state. After that, the goal is for the client's own team to own the platform, extend it to new use cases, and run it day to day — not to depend on an outside consultant indefinitely. A team that already has both the Kubernetes depth and the governance patterns in place likely doesn't need this kind of engagement at all.
Frequently asked questions
What does an AI infrastructure consultant do?
Do I need SOC 2 certification before deploying AI?
What is the difference between AI readiness and AI governance?
Can this run on a single cloud or does it need multi-cloud?
How long does a compliant AI platform take to reach production?
Related pillar pages
AI Readiness Assessment
Score your infrastructure, data, and governance maturity before you commit budget.
Enterprise MLOps Governance
Model registry, approval workflow, drift monitoring, and audit-ready evidence.
OpenShift AI Multi-Tenant GPU Architecture
Reference architecture for isolating and scheduling GPUs across teams.
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Written by Luca Berton — AI & Cloud Advisor, Docker Captain, former Red Hat engineer, 18+ years in enterprise infrastructure. Speaker at KubeCon EU 2026 and Red Hat Summit 2026. More about Luca →
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