From a secured RAG endpoint to a battle-tested enterprise platform. Build, govern, defend, and scale a compliance-first AI system — with full observability and multi-tenant isolation.
fig 1 — finsecure enterprise ai platform
TENANTS
50+
Isolated Clients
COMPLIANCE
HIPAA
Audit Trail
DETECTION
99.2%
Guardrail Accuracy
RECOVERY
$8,760
Annual Chargeback
# Clone the project & launch the enterprise AI stack$ git clone https://github.com/aide-hub/finsecure-enterprise-ai.git$ cd finsecure-enterprise-ai# Core path: FastAPI + pgvector (Quick Start)$ docker-compose -f docker-compose.core.yml up -d# Full mode: + Redis + Prometheus + Grafana$ docker-compose -f docker-compose.full.yml up -d# Run the setup script$ python scripts/setup.py --tenant demo --mode core
You can complete this project with FastAPI + pgvector (core path). Everything else is a production enhancement.
A complete enterprise AI platform for FinSecure Health — a HIPAA-compliant, multi-tenant SaaS company. This is what AI platform engineers build at real healthcare and fintech companies.
RBAC, audit logging, PII masking, GDPR deletion — compliance-first from day one
HIPAA-compliantLineage tracking, policy enforcement, secure agent sandboxing, approval gates
Full provenance graphDetect → Prevent → Observe → Respond: prompt injection, jailbreak, output filter
99.2% detection ratePrometheus metrics, Grafana dashboards, OpenTelemetry tracing, incident runbooks
P99 latency trackedPer-tenant RLS isolation, rate limits, cost attribution, leakage prevention
50+ isolated tenantsPer-tenant token tracking, billing reports, $8,760/year chargeback recovery
Revenue-grade billingEach part builds directly on the previous. Parts 1–2 are included with Pro. Parts 3–4 unlock with Expert.
Build a HIPAA-compliant RAG pipeline with RBAC, STRIDE threat modeling, audit logging, PII detection, and a full LLM response layer with fallback strategy.
Add data lineage tracking, Redis-backed policy enforcement, secure agent sandboxing with approval gates, and automated compliance report export.
Parts 1–2 prove you can build a governed AI system. Parts 3–4 prove you can defend, scale, and own it — when real users, real data, and real compliance requirements are on the line.
This is where most AI systems fail in enterprise environments:
These failures don't just break systems — they create legal and financial risk. If your system handles real users, these problems will happen.
Engineers who design compliant, multi-tenant AI systems are among the highest-paid in regulated industries. These are the exact controls required at fintech, healthcare, and regulated SaaS companies — the same patterns used at teams building HIPAA-compliant AI at scale.
Unlock Parts 3–4
Structure safety as Detect → Prevent → Observe → Respond. Add prompt injection detection, output filtering, jailbreak prevention, Prometheus/Grafana, and incident runbooks.
Implement per-tenant isolation (RLS), rate limits, cost attribution, cross-tenant leakage prevention. Break the system. Present to CTO, Security, and Product. Own this platform.
Security, compliance, and multi-tenancy are treated as afterthoughts. This project makes them the foundation.
HIPAA audit trails, GDPR deletion, RLS isolation — the same controls required at fintech and healthcare companies.
System tradeoffs, executive explanations, ownership narratives — this is what Staff engineers do, not seniors.
Add these bullet points to your resume after completing the project:
An enterprise AI platform is a production-grade system that provides secure, governed, and compliant AI capabilities across an organization. This project builds a HIPAA-compliant RAG pipeline with RBAC, STRIDE threat modeling, PII detection with Presidio, agent sandboxing with approval gates, LLM safety layers including prompt injection detection, and multi-tenant scaling -- the complete stack for organizations deploying AI with enterprise-grade security and governance.
Build a HIPAA-compliant RAG pipeline with RBAC, audit logging, and PII detection
Add data lineage tracking, Redis-backed policy enforcement, and agent sandboxing
Implement LLM safety with prompt injection detection, output filtering, and jailbreak prevention
Build Prometheus/Grafana observability with incident runbooks and response procedures
Scale to multi-tenant with isolation, rate limiting, and compliance report generation
Every enterprise deploying AI faces the same challenge: how to give employees AI capabilities without exposing sensitive data or violating regulations. Companies like Microsoft, Salesforce, and ServiceNow invest heavily in enterprise AI governance. Healthcare, finance, and government sectors require HIPAA/SOC 2 compliance, making this the critical infrastructure for regulated industries adopting AI.
Start with Part 1 — secure the RAG foundation. Everything else builds from there.