AI Infrastructure for Regulated Industries: What Selection Criteria Actually Matter

The question "What AI infrastructure should we use?" is wrong. The right question is "What constraints does our regulatory environment impose, and which infrastructure options satisfy them while still delivering business value?"

In regulated industries — financial services, healthcare, energy, public sector — infrastructure selection isn't a technology decision. It's a compliance decision with technology implications. Get the order wrong, and you'll spend 18 months building a platform that your compliance team won't let you use.

The Seven Criteria That Actually Matter

After dozens of AI infrastructure assessments across regulated enterprises, these are the criteria that determine success or failure — in order of importance:

1. Data Residency and Sovereignty

Where does your data live? Where does inference happen? These aren't the same question, and both matter.

What to evaluate: Does the infrastructure provider offer EU-only deployment options? Can you guarantee that inference requests containing sensitive data never leave your jurisdiction? Are model weights stored in your region? Can you prove all of this to an auditor?

2. Audit Trail and Observability

In regulated environments, "it works" isn't enough. You need to prove it works, prove how it works, and prove what it did on Tuesday at 14:32 when it processed that customer's loan application.

• Input/output logging — every inference request and response, with timestamps, user context, and model version
• Decision traceability — for agentic systems, the full chain of reasoning: what was retrieved, what tools were called, what decisions were made at each step
• Model lineage — which model version produced which output. When models update, historical outputs must remain attributable to the correct version
• Retention compliance — logs must be retained for the period your regulator requires (often 5-7 years for financial services) and must be deletable when GDPR requires it. These two requirements conflict — your infrastructure must handle both

3. Security Architecture

AI infrastructure introduces attack surfaces that traditional infrastructure doesn't have:

• Model serving endpoints — API endpoints that accept arbitrary text input are inherently high-risk. Rate limiting, input validation, authentication, and DDoS protection are baseline requirements
• Training data security — training datasets often contain the organisation's most sensitive information. Encryption at rest, access controls, and data loss prevention are non-negotiable
• Model weight protection — proprietary models are intellectual property. Storage encryption, access logging, and exfiltration detection
• Supply chain security — ML frameworks, model dependencies, and container images need the same vulnerability scanning and signing as application code

4. Operational Resilience

Under DORA, financial entities must demonstrate operational resilience for critical ICT services. If AI is part of a critical business process, it falls under these requirements:

• Availability SLAs — what uptime does the infrastructure guarantee? Is it sufficient for your use case? (A chatbot can tolerate 99.5%. AI-assisted fraud detection cannot.)
• Failover and redundancy — what happens when the primary region goes down? Is there automatic failover? How quickly?
• Disaster recovery — can you restore AI services from backup? Including model weights, configuration, and state? Have you tested it?
• Exit strategy — can you migrate away from this infrastructure provider within a reasonable timeframe? DORA explicitly requires exit planning for critical ICT services

5. Cost Predictability

AI infrastructure costs are notoriously unpredictable. Token-based pricing, GPU spot instances, and usage-dependent scaling make budgeting difficult:

• Cost model transparency — can you predict monthly costs within 20% based on expected usage? If not, the cost model is too opaque for enterprise budgeting
• Cost controls — hard spending limits, budget alerts, automatic scaling ceilings. A runaway agentic loop shouldn't bankrupt the department
• Cost attribution — can you allocate costs to specific teams, projects, and use cases? Finance needs this for chargebacks and ROI calculation
• Total cost of ownership — include people costs. Self-hosted GPU clusters are cheap per-token but expensive in platform engineering effort. Managed services are expensive per-token but cheap in operational overhead. The right answer depends on your team's capabilities

6. Model Flexibility

The worst infrastructure decision is one that locks you to a single model provider:

• Multi-model support — can you run different models for different use cases? Switch models without re-architecting?
• Open-source compatibility — can you deploy open-source models (Llama, Mistral, etc.) alongside proprietary ones?
• Fine-tuning support — can you fine-tune models on your own data within the infrastructure, keeping data in your environment?
• Version management — can you run multiple model versions simultaneously for A/B testing and gradual rollouts?

7. Integration Capabilities

AI infrastructure doesn't exist in isolation. It connects to your data, your applications, and your governance tools:

• Data source connectivity — can it connect to your data warehouse, document stores, and APIs without moving data to a third-party environment?
• Identity integration — does it support your identity provider (Azure AD, Okta, etc.) for SSO and role-based access control?
• Observability integration — can metrics flow into your existing monitoring stack (Datadog, Grafana, Splunk)?
• CI/CD integration — can model deployments be automated through your existing pipelines?

The Evaluation Framework

Common Mistakes

• Choosing based on benchmarks — model performance benchmarks are irrelevant if the infrastructure can't meet your compliance requirements
• Ignoring the exit — vendor lock-in is a strategic risk. Evaluate how hard it is to leave, not just how easy it is to start
• Underestimating operational costs — the platform is 30% of the cost. The people, processes, and governance around it are 70%
• Evaluating in isolation — IT selects infrastructure, compliance reviews it after. This is backwards. Compliance, security, legal, and IT should evaluate together from day one
• Assuming cloud means compliant — major cloud providers offer compliant regions and configurations, but compliance is YOUR responsibility, not theirs. "It's on AWS" is not a compliance statement

The infrastructure you select will constrain every AI decision for the next 3-5 years. In regulated industries, getting this wrong isn't just expensive — it's potentially a regulatory violation. Take the time to evaluate properly.