I need AI infrastructure that works in a regulated environment

Absolutely — for a regulated environment, the key is to design AI infrastructure around security, auditability, governance, data control, and deployment flexibility.

What “regulated” usually requires

Depending on your industry (finance, healthcare, pharma, public sector, insurance, etc.), you typically need:

• Data residency / sovereignty
• Access controls and least privilege
• Full audit logs
• Encryption in transit and at rest
• Change management / model versioning
• Explainability and traceability
• Private networking / no public internet exposure
• Retention and deletion controls
• Human review / approval workflows
• Vendor risk management
• Compliance mapping to standards like SOC 2, ISO 27001, HIPAA, GDPR, PCI DSS, FedRAMP, or industry-specific rules

Recommended infrastructure pattern

For regulated environments, the safest pattern is usually:

1) Private deployment

Run the AI stack in one of these forms:

• On-premises
• Private cloud / dedicated VPC
• Single-tenant managed environment
• Hybrid setup where sensitive data stays in your environment

Avoid architectures that send sensitive data to public SaaS AI tools unless they are explicitly approved.

2) Control the model layer

Use one of:

• Self-hosted open-source models
• Privately hosted commercial models
• Vendor-managed but isolated deployments

This gives you better control over:

• where prompts and outputs go
• what gets logged
• whether data is used for training
• latency and availability

3) Add an AI governance layer

You’ll want:

• Prompt and response logging
• Policy enforcement
• PII/PHI redaction
• Content filtering
• Model approval workflows
• Versioned prompt templates
• Evaluation and drift monitoring

4) Secure the data pipeline

Use:

• Encrypted object storage
• Private databases
• Data classification
• Tokenization or masking
• Retrieval-augmented generation (RAG) with access controls
• Row-level or document-level security

5) Build for auditability

Make sure you can answer:

• What data was used?
• Which model version produced the output?
• Who accessed it?
• What policy checks were applied?
• Was a human reviewer involved?

Reference architecture

A common regulated AI architecture looks like this:

• User interface
• API gateway
• Identity provider (SSO, MFA, RBAC)
• Policy engine
• PII/PHI detection and masking
• RAG layer connected to approved internal documents
• Model inference service in a private environment
• Logging and SIEM integration
• Model registry and evaluation system
• Human approval queue for sensitive use cases

Platform options

Depending on your needs, options include:

Cloud-native with controls

• AWS Bedrock in private/VPC-aligned setup
• Azure OpenAI / Azure AI with enterprise controls
• Google Cloud Vertex AI with restricted networking

Private/self-hosted

• Kubernetes-based deployment of open-source models
• On-prem inference servers with GPU infrastructure
• Dedicated appliance-style deployments

Hybrid

• Sensitive data processed internally
• Non-sensitive tasks routed to managed models
• Federated or segmented access by use case

Questions to narrow this down

If you want, I can recommend an architecture based on your specific constraints. Helpful details:

1. Industry/regulatory regime
   Example: healthcare, finance, government, legal, insurance

2. Deployment preference
   On-prem, cloud, hybrid, or single-tenant managed

3. Data sensitivity
   PII, PHI, payment data, trade secrets, classified info

4. Use case
   Chatbot, document extraction, coding assistant, knowledge search, decision support

5. Compliance targets
   HIPAA, GDPR, SOC 2, ISO 27001, PCI DSS, FedRAMP, etc.

6. Scale Number of users, expected throughput, latency needs

If you share those, I can give you a specific compliant AI infrastructure blueprint.