Enterprise RAG Security
RAG threat modeling with STRIDE, RBAC for retrieval, document-level permissions, query filtering, and audit logging — the security surface enterprise buyers will pen-test before they sign.
Phases
3
AI SystemPhase 1 in ProfessionalFull access in Expert
Enterprise AI Infrastructure
Secure RAG, PII detection, compliance, multi-tenant AI, and LLM safety guardrails.
Enterprise AI fails on security and governance, not on model quality. Knowing PII, RBAC, audit, and multi-tenancy is the difference between a regulated-industry deal you can close and a pilot that never leaves dev.
What you'll do
What you'll be able to do.
- Build enterprise RAG systems with security and access control
- Implement PII detection and data governance for AI systems
- Design compliant AI pipelines meeting regulatory requirements
- Deploy multi-tenant AI infrastructure with safety guardrails
Phase roadmap.
Phase 1PRO REQUIRED
Security & Compliance
The security and compliance floor every enterprise AI deployment lands on. RAG threat modeling, RBAC for retrieval, immutable audit, GDPR mechanics — get these wrong and the deal dies in legal review.
1.1
⊘Enterprise RAG Security
RAG threat modeling with STRIDE, RBAC for retrieval, document-level permissions, query filtering, and audit logging — the security surface enterprise buyers will pen-test before they sign.
1.2
⊘Compliant AI Systems
Compliance requirements analysis, immutable audit logging, retention policies, hands-on GDPR right-to-deletion, and the compliance dashboard auditors actually want to see.
Phase 2EXPERT REQUIRED
Data Governance for AI
PII, lineage, consent, and the agent attack surface. The governance spine that makes enterprise AI defensible to auditors and survivable in production.
2.1
⊘PII Detection & Redaction
Stand up Presidio (or equivalent), scan inputs and outputs, tune confidence scores and entity analysis, choose between mask/redact/hash, and ship a compliance dashboard.
2.2
⊘Data Governance for AI
Classification systems, lineage tracking, retention-policy engines, consent management, and the data-quality monitoring that keeps governance from becoming a quarterly fire drill.
2.3
⊘Secure Agent Workflows
Agent security architecture, workflow-level RBAC, secure tool execution, audit logging, and the deployment patterns that keep autonomous agents from leaking customer data.
Phase 3EXPERT REQUIRED
Production Enterprise AI
Observability, safety, and multi-tenancy under load. The production stack that keeps enterprise AI alive on-call without taking down all your tenants when one workload misbehaves.
3.1
⊘Production Monitoring
Prometheus metrics, Grafana dashboards, OpenTelemetry distributed tracing, SLO-driven alerting, and the cost-tracking layer that ties spend back to tenants and features.
3.2
⊘LLM Safety Guardrails
Prompt-injection detection, output filtering and PII-leakage prevention, jailbreak resistance, RAG-specific guardrails, and the policy-enforcement layer that gates every model call.
3.3
⊘Multi-Tenant AI Systems
Tenant isolation in RAG, namespace-separation strategies, cross-tenant leakage prevention, per-tenant rate limits and quotas, and tenant-aware observability — the multi-tenant playbook with Postgres RLS underneath.
What you'll ship
What you'll build.
- Secure RAG with RBAC + document-level permissions + audit log
- Presidio-based PII detection and redaction layer (input + output)
- Multi-tenant AI platform with Postgres RLS + per-tenant cost tracking
- Compliance dashboard (GDPR + audit + lineage) wired to your stack
Definition
What is Enterprise AI Infrastructure?
Enterprise AI infrastructure covers the security, compliance, and governance patterns required to deploy AI systems in regulated, multi-tenant environments. It includes secure RAG with access control, PII detection and redaction, compliance automation, and LLM safety guardrails. These patterns are essential for companies in finance, healthcare, and government deploying AI at scale.
Production context
Why this matters in production.
Enterprise AI deployments fail without proper security and compliance. At JPMorgan, AI systems must handle PII correctly, enforce document-level access control, and meet regulatory requirements. A single data leak or compliance violation can cost millions in fines and destroy customer trust.
Use cases
Common use cases.
- Building enterprise RAG with document-level access control and security
- Implementing PII detection and redaction in AI pipeline inputs and outputs
- Designing AI systems that meet SOC 2, GDPR, and HIPAA compliance requirements
- Deploying multi-tenant AI infrastructure with data isolation
- Building LLM safety guardrails for prompt injection and harmful content prevention
- Monitoring enterprise AI systems for security incidents and compliance violations
Compare
Enterprise AI vs alternatives.
Enterprise AIvsConsumer AI
Enterprise AI requires security, compliance, and multi-tenancy that consumer AI does not. Consumer AI optimizes for user experience; enterprise AI must also satisfy regulators, auditors, and security teams.
Enterprise AIvsOpen Source AI
Enterprise AI infrastructure adds security layers on top of open-source models and tools. Open-source provides the foundation; enterprise patterns add access control, auditing, and compliance automation.
Enterprise AIvsCloud AI Services
Enterprise AI infrastructure provides full control over data and compliance. Cloud AI services offer convenience but may not meet data residency or regulatory requirements. Most enterprises use hybrid approaches.
Why this matters
Why this skill matters.
Enterprise AI is where regulated-industry AI roles are hiring. This skill puts you in the room when legal, security, and ML eng all need someone who can speak STRIDE, Presidio, RLS, and SOC 2 in one sentence.
FAQ
Common questions about Enterprise.
Enterprise AI InfrastructureUpgrade to Professional