Back to profile

Secure Cloud Platforms (AWS + HIPAA-grade)

Secure cloud platform for training and inference on user data with HIPAA-grade compliance and strong isolation.

Overview

Problem: teams need to train and serve models on sensitive user data without compromising compliance or privacy.

Why it matters: AI is only valuable if it is safe, auditable, and deployable at scale.

For: healthcare, regulated industries, and privacy-sensitive products.

Vision & Roadmap

• Phase 0: baseline zero-trust reference stack.
• Phase 1: training/inference isolation standards.
• Phase 2: reusable compliance automation.
• Done means: deploy in days with policy guardrails.

Architecture

Diagram placeholder: IAM -> isolated data zones -> secure training -> inference gateway.

• Zero-trust IAM with least privilege.
• Encrypted data zones and audit trails.
• Isolated training and inference workloads.

Data & AI

• Sources: secure data lakes, regulated datasets.
• Models: privacy-safe training and inference pipelines.
• Evaluation: audit readiness, data leakage risk, latency.

Progress

• Zero-trust IAM baseline
• Audit logging automation
• AI workload templates
• Secure training/inference reference flows

Blockers: org-wide policy alignment.

Next 30/60/90: expand templates, add governance metrics, validate training/inference isolation.

Learnings

• Security adoption improves when tooling is seamless.
• Isolation is the foundation for trusted AI delivery.