enterprise-rag-patterns 0.5.1

Cross-industry compliance patterns for RAG pipelines: FERPA, HIPAA, GDPR, NIST AI RMF, OWASP LLM Top 10, and more. Vector store adapters, framework integrations, and audit logging.

enterprise-rag-patterns

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The problem this solves

Standard RAG implementations retrieve documents and pass them directly to an LLM — with no enforcement of who is allowed to see what. In regulated environments (higher education, healthcare, financial services, government), this creates a structural compliance failure: a student receives another student's records, a patient's ePHI leaks into an unrelated clinical query, prompt injection hides in a retrieved document, and no audit log is produced.

This library provides the missing compliance layer — a cross-industry framework of pre-filters, identity scopes, risk assessors, and audit records that enforce regulatory requirements at the retrieval layer, before any document reaches the LLM context window.

Regulations covered: FERPA · HIPAA · GDPR · NIST AI RMF · OWASP LLM Top 10

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Architecture

Session Token
     │
     ▼
StudentIdentityScope
(student_id + institution_id + authorized_categories + disclosure_reason)
     │
     ├─ Vector Store Pre-filter ──────────────────────────────────┐
     │   student_id + institution_id + categories checked here   │
     │   Only authorized documents enter the ranking stage       │
     │                                                            │
     ├─ Policy Layer Filter (defense-in-depth) ──────────────────┤
     │   Application-level identity re-check                     │
     │   Blocks any document that escaped the vector filter      │
     │                                                            │
     ├─ Audit Record ─────────────────────────────────────────────┤
     │   34 CFR § 99.32 Disclosure Log                           │
     │   Emitted before LLM sees any document                    │
     │                                                            │
     └─ LLM Context (authorized documents only) ─────────────────┘

Why pre-filter, not post-filter? Post-filtering is a UI concern, not a compliance control — the LLM has already processed the unauthorized record. FERPA and HIPAA require that disclosure not occur, not that unauthorized data be hidden after the fact. See docs/adr/ for the full architecture decision record.

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Installation

pip install enterprise-rag-patterns

With framework extras:

pip install 'enterprise-rag-patterns[langchain]'
pip install 'enterprise-rag-patterns[llama-index]'
pip install 'enterprise-rag-patterns[haystack]'

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60-second example

from enterprise_rag_patterns.compliance import (
    StudentIdentityScope,
    RecordCategory,
    FERPAContextPolicy,
    DisclosureReason,
)

# Build a verified scope from your session token — never from user input
scope = StudentIdentityScope(
    student_id="stu_001",
    institution_id="univ_abc",
    requesting_user_id="advisor_007",
    authorized_categories={RecordCategory.ACADEMIC_RECORD},
    disclosure_reason=DisclosureReason.SCHOOL_OFFICIAL,
)
policy = FERPAContextPolicy(scope=scope)

# Your retriever returns docs — filter before the LLM sees them
safe_docs = policy.filter_retrieved_documents(
    retrieved_docs,
    student_id_field="student_id",
    institution_id_field="institution_id",
    category_field="category",
)

# Emit a 34 CFR § 99.32 disclosure log entry
audit = policy.record_access(categories_accessed={RecordCategory.ACADEMIC_RECORD})
print(audit.to_log_entry())
# → {"record_id": "...", "student_id": "stu_001", "regulation": "FERPA",
#    "categories": ["academic_record"], "permitted": true, "timestamp": "..."}

See examples/ferpa_rag_pipeline.py for a complete runnable pipeline.

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Framework integrations

Framework	Integration Class	Install Extra
LangChain	FERPAComplianceCallbackHandler	[langchain]
LlamaIndex	FERPANodePostprocessor	[llama-index]
Haystack 2.x	FERPAHaystackFilter	[haystack]
Pinecone	PineconeComplianceFilter	[pinecone]
Weaviate	WeaviateComplianceFilter	[weaviate]
Qdrant	QdrantComplianceFilter	[qdrant]
ChromaDB	ChromaComplianceFilter	[chromadb]

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Cross-industry compliance coverage

Regulation / Framework	Status	Primary Sector	RAG Controls
FERPA (34 CFR § 99)	✅ Implemented	Education	Identity scoping, 34 CFR § 99.32 audit log
GDPR (Articles 17, 32)	✅ Implemented	EU / Global	Right-to-erasure, data subject rights
HIPAA (45 CFR §§ 164.312, 164.502)	✅ Implemented	Healthcare	ePHI minimum-necessary, audit controls
NIST AI RMF 1.0 + AI 600-1	✅ Implemented	All sectors	MAP/MEASURE/MANAGE risk assessment
OWASP LLM Top 10 (2025)	✅ Implemented	Software / AI	LLM01 injection, LLM02 PII disclosure
SOC 2 Type II	✅ Implemented	SaaS / Enterprise	Tenant isolation, CBAC, CC7.2 audit log
GLBA (16 CFR § 314)	🗓 Planned	Financial services	Customer record safeguards
EU AI Act	🗓 Planned	EU / Global	Article 12 tamper-evident audit logs

Four-layer defense-in-depth model

Layer 0: Query-time security    → OWASP (PII redaction, injection scanning)
Layer 1: Identity scoping       → FERPA / HIPAA (namespace + metadata filter)
Layer 2: Compliance filtering   → FERPA / HIPAA / GDPR (document-level rules)
Layer 3: Risk assessment + audit→ NIST AI RMF / HIPAA (structured audit records)

See docs/architecture.md for the full layered model.

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Repository structure

src/enterprise_rag_patterns/
├── compliance.py               # FERPA identity scoping + 34 CFR § 99.32 audit
├── context.py                  # Multi-source context envelope assembly
├── session.py                  # Cross-channel session continuity
├── policy.py                   # Escalation and action-boundary policy objects
├── async_compliance.py         # Async wrappers for asyncio/FastAPI environments
├── regulations/
│   ├── gdpr.py                 # GDPR Article 17 right-to-erasure patterns
│   ├── hipaa.py                # HIPAA ePHI minimum-necessary + audit (NEW)
│   ├── nist_ai_rmf.py          # NIST AI RMF 1.0 + AI 600-1 risk assessment
│   ├── owasp_llm.py            # OWASP LLM Top 10 (2025) — LLM01/LLM02
│   └── soc2.py                 # SOC 2 Type II CBAC — CC6.1/CC6.6/C1.1/CC7.2 (NEW)
├── vector_stores/
│   ├── pinecone_adapter.py     # PineconeComplianceFilter + namespace isolation
│   ├── weaviate_adapter.py     # WeaviateComplianceFilter
│   ├── qdrant_adapter.py       # QdrantComplianceFilter
│   └── chroma_adapter.py       # ChromaComplianceFilter
└── integrations/
    ├── langchain.py            # FERPAComplianceCallbackHandler (LangChain 0.3+)
    ├── langchain_lcel.py       # FERPAFilterRunnable + make_ferpa_chain (LCEL)
    ├── llama_index.py          # FERPANodePostprocessor (LlamaIndex)
    ├── llama_index_workflow.py # FERPAWorkflowStep (LlamaIndex 0.12+ Workflows)
    ├── haystack.py             # FERPAHaystackFilter (Haystack 2.x)
    └── maf.py                  # FERPAAgentMiddleware (Microsoft Agent Framework)
docs/
├── architecture.md             # Four-layer defense-in-depth model
├── adr/                        # Architecture decision records
└── implementation-note-*.md    # Implementation notes
examples/
└── ferpa_rag_pipeline.py       # Complete runnable FERPA-compliant pipeline

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Published notes

• Implementation Note 01 — Cross-channel continuity problem and solution
• Implementation Note 02 — FERPA boundaries in retrieval-augmented generation
• Production-Grade RAG in Regulated Enterprise Environments

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Near-term roadmap

• regulations/eu_ai_act.py — EU AI Act Article 12 tamper-evident audit log with cryptographic signing
• regulations/glba.py — GLBA Safeguards Rule financial record access controls
• integrations/crewai.py — CrewAI policy-gated tool wrapper
• Async vector store adapters for FastAPI/asyncio environments
• ECOSYSTEM.md: compatibility matrix with current ecosystem versions

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Contributing

Contributions are welcome. Please read CONTRIBUTING.md for guidelines and GOVERNANCE.md for the governance model. Run pytest tests/ -v to verify your changes before opening a pull request.

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Citation

If you use these patterns in research or production, please cite:

@software{rana2026erp,
  author    = {Rana, Ashutosh},
  title     = {enterprise-rag-patterns: FERPA-compliant retrieval-augmented generation patterns},
  year      = {2026},
  url       = {https://github.com/ashutoshrana/enterprise-rag-patterns},
  license   = {MIT}
}

Or use GitHub's "Cite this repository" button above (reads CITATION.cff).

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Part of the enterprise AI patterns trilogy

Library	Focus	Compliance
enterprise-rag-patterns	What to retrieve	FERPA, HIPAA, GDPR, NIST AI RMF, OWASP LLM
regulated-ai-governance	What agents may do	FERPA, HIPAA, GLBA policy enforcement
integration-automation-patterns	How data flows	Event-driven enterprise integration

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License

MIT — see LICENSE.