Private Enterprise AI: Security, Compliance & Scale

Private Enterprise AI: Security, Compliance & Scale

Threat Model

• Model Exfiltration – Unauthorized access to proprietary weights.
• Prompt Injection – Malicious inputs causing unsafe model behavior.
• Data Leakage – Training data containing PII or confidential IP.

Defensive Architecture

1. 1Isolated Compute – Host LLM containers in a VPC with no internet egress; use SG rules to whitelist internal services.
2. 2Model Encryption at Rest – Cloud‑KMS‑managed keys protect model artifacts.
3. 3Secure Inference API – Authenticated, rate‑limited endpoint; input sanitization middleware.
4. 4Audit Logging – Record request payloads, responses, and user identity to immutable storage.

Compliance Controls

• Data Subject Rights – Request hooks that delete user‑provided prompts from logs.
• Security Testing – Regular penetration testing of the inference surface.
• AI‑Specific Guidance – Maintain model cards documenting training data provenance and bias assessments.

Scaling Strategies

• Horizontal Autoscaling – Kubernetes HPA based on GPU utilization.
• Chunked Inference – Split long prompts into manageable windows to keep latency < 500 ms.
• Caching – Store frequently requested completions for identical prompts (subject to privacy constraints).

Operational Playbook

• Change Management – New model versions deployed via canary rollout; monitor drift metrics.
• Incident Response – Automated alerts on abnormal request patterns; immediate isolation of affected pods.
• Continuous Monitoring – Use Prometheus/Grafana dashboards for GPU health, request latency, and error rates.

By combining strict network isolation, policy‑driven governance, and observability, enterprises can safely harness powerful LLMs at scale while meeting regulatory obligations.