Deploying secure AI services on Apple Private Cloud Compute

Secure AI on Apple Private Cloud Compute

Private Cloud Compute only works if the controls are boring and tight. Keep the model nodes off the public internet, put a gateway in front of them, and treat retention, telemetry and key handling as things to decide before anything goes live.

Privacy settings

Set the privacy rules first. Decide what is retained, how long it stays around, and whether telemetry is allowed out of the private cloud. If a third-party model is involved, check that its processing stays inside the private compute boundary. Record the model version and the retention policy for each endpoint.

• Set retention windows for logs and query traces.
• Turn off model training on production inference endpoints.
• Block outbound access from inference nodes except to approved control hosts.

Initial setup

A minimal setup is enough to start, but it needs proper separation.

Network layout

• Create a private VPC for inference. Do not give model nodes public IPs.
• Put a public load balancer or API gateway in a separate DMZ subnet.
• Allow port 443 from the gateway to the inference pool only.

Identity and access

• Use short-lived service credentials for services calling models.
• Store secrets in an HSM-backed KMS. Do not bake keys into container images.

Storage and encryption

• Encrypt data at rest with AES-256 keys managed by KMS.
• If model snapshots are kept, mark them read-only and lock write access.

Minimal DevOps

• Use infrastructure as code for repeatability. Tag builds with model name, hash and deploy timestamp.
• Add a pipeline step that runs a smoke test against the private endpoint.

Verification

openssl s_client -connect model-gw.internal:443 -servername model-gw.internal

nmap -Pn <inference-node-ip>

Check that only the required internal ports are open.

Third-party models

If a third-party model such as Google Gemini is hosted in the private cloud, treat it as an external model provider that you are operating yourself.

1. Obtain an image or model artefact from the vendor under a private deployment agreement.
2. Run the model in an isolated namespace or project. Use resource limits for CPU, memory and GPU where available.
3. Put an internal API gateway in front of the model. The gateway terminates TLS and handles authentication.

Example API gateway policy:

• Path: /v1/infer/{model}
• Auth: JWT signed by internal auth service
• Rate-limit: 50 requests/minute per client
• Strip PII from logs before writing to storage

Treat every request as sensitive. If logging is needed, redact personal identifiers at ingestion. Turn off unsolicited outbound inference telemetry from the model runtime.

Data security controls

Key management, network rules and storage controls need to line up.

Key management

• Use HSM-backed KMS for all model keys.
• Keep master keys off the same hosts as inference nodes.

Network security

• Use mutual TLS between gateway and inference nodes.
• Use firewall rules that limit egress from inference nodes to control-plane hosts only.

Storage

• Mount inference model volumes read-only using the OS mount options.
• If object storage is used for datasets, enable bucket-level encryption and object locks for audit retention.

From the gateway host:

curl --cert gateway.crt --key gateway.key https://inference.internal/health

Rotate service keys every 7–30 days depending on exposure. Automate rotation with CI/CD and KMS hooks.

Siri integration

If Siri is a target integration point, keep the Siri-facing layer thin and treat it as another consumer of the private model endpoints.

1. Siri front-end calls a policy service in the DMZ.
2. Policy service applies user consent checks and privacy settings.
3. Requests are forwarded to the private model via the internal gateway.

Attach a consent token to requests that records scope and retention. Use short-lived tokens and enforce scope at the gateway.

Cache non-sensitive model responses at the gateway for repeated identical queries. For personal data, bypass cache and route direct to the model.

Testing and monitoring

Run synthetic traffic from a bastion host that matches expected load. Check error rates against the SLO and watch latency percentiles rather than averages. Test key rotation by triggering a rotation pipeline and confirming the service keeps working.

• Collect metrics separately from traces. Push metrics to a locked metrics cluster.
• Store access logs in an immutable store for audit. Rotate access logs by retention policy.
• Run periodic vulnerability scans on the container images.
• Run a permission audit to confirm no broad roles exist.

Smoke test:

curl -H "Authorization: Bearer <token>" https://api-gw.example/v1/infer/model -d '{"prompt":"test"}'

Confirm HTTP 200 and that the model_hash in the response matches the deployed artefact.

Future changes

• Review privacy settings after any model update.
• Keep a playbook for revocation of models and keys.
• Track provenance: record where model artefacts came from and the hash for each deployment.
• Build an automated deploy and smoke-test pipeline with explicit retention and consent toggles.
• Add an incident runbook for key compromise and accidental data leakage.
• Schedule quarterly audits of all endpoints and policies.

Run models in Apple Private Cloud Compute only after retention, key handling and network isolation are set properly. Gate access with short-lived credentials and mutual TLS. Treat third-party models such as Google Gemini as privately hosted services with the same controls as any in-house model.