I'm building a private inference endpoint for regulated data

Got it. If you’re building a private inference endpoint for regulated data, the main goal is to design for confidentiality, isolation, auditability, and minimal data exposure end to end.

Here’s a practical checklist to structure it:

1) Data handling

• Minimize what you send to the model.
• Redact or tokenize sensitive fields before inference when possible.
• Define whether data is:
  • transient only,
  • logged,
  • stored for retries/caching,
  • used for model improvement.
• Ensure no training on customer data unless explicitly intended and contractually allowed.

2) Network isolation

• Put the endpoint in a private network/VPC/VNet.
• Use private connectivity only:
  • PrivateLink / Private Endpoint / internal load balancer.
• Block public ingress and egress by default.
• Restrict outbound traffic to only approved dependencies.

3) Authentication and authorization

• Use strong service-to-service auth:
  • mTLS, signed tokens, workload identity, or IAM-based auth.
• Enforce least privilege:
  • per-app, per-team, per-environment.
• Separate admin and inference access.

4) Encryption

• Encrypt:
  • in transit with TLS 1.2+,
  • at rest using managed or customer-managed keys.
• If required, use customer-managed keys and key rotation.
• Consider field-level encryption for especially sensitive attributes.

5) Logging and observability

• Avoid logging raw prompts/outputs unless absolutely necessary.
• If logs are needed:
  • redact sensitive content,
  • keep short retention,
  • restrict access tightly.
• Audit:
  • who called the endpoint,
  • when,
  • from where,
  • model/version used,
  • request IDs and policy decisions.

6) Residency and retention

• Keep data in the required region.
• Set explicit retention windows for:
  • requests,
  • responses,
  • traces,
  • backups,
  • dead-letter queues.
• Make deletion workflows available.

7) Model and supply-chain security

• Pin model versions and dependencies.
• Scan container images and artifacts.
• Control who can deploy new model versions.
• Validate inputs to reduce prompt injection and malformed payloads.

8) Runtime hardening

• Run inference in isolated containers or VMs.
• Disable shell access unless needed.
• Use read-only filesystems where possible.
• Limit memory, CPU, and outbound network.
• Consider hardware-backed isolation if required by policy.

9) Compliance and governance

• Map controls to your obligations:
  • HIPAA, PCI, GDPR, SOC 2, ISO 27001, etc.
• Document:
  • data flows,
  • subprocessors,
  • incident response,
  • access reviews,
  • DPIA/PIA if applicable.

10) Validation

• Test for:
  • accidental logging,
  • data leakage across tenants,
  • prompt injection,
  • auth bypass,
  • retention failures,
  • misconfigured network rules.

If you want, I can help you turn this into one of the following:

1. a reference architecture,
2. a security control checklist,
3. a cloud-specific design for AWS/Azure/GCP,
4. or a deployment plan for a private LLM inference service.