sovereign-vault

sovereign-vault

Reversible PII tokenization for LLM pipelines.

Send documents containing real names, SSNs, emails, and account numbers to any cloud AI — Claude, Gemini, GPT — without exposing the actual values. The AI reasons about relationships and patterns on placeholder tokens. You reconstruct the real values locally after the response comes back.

pip install sovereign-vault

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The problem

You have documents with names, SSNs, emails, and account numbers. You need a cloud AI to analyze patterns, identify anomalies, or summarize findings. But you can't send the raw PII — compliance, legal, or common sense says no.

Standard redaction destroys the data permanently. The AI then can't reason about cross-entity relationships — "the same person appears in both transactions" becomes impossible once everything is [REDACTED].

Why not just redact?

Approach	Cross-entity reasoning	Reversible	Cloud-safe	Compliance-ready
Raw PII	✅	N/A	❌ Violation	❌
[REDACTED]	❌ All identity destroyed	❌	✅	⚠️ Partial
Synthetic data	⚠️ Inconsistent mappings	❌	✅	⚠️ Partial
Differential privacy	❌ Noise destroys fidelity	❌	✅	✅
sovereign-vault	✅ Same person = same token	✅	✅	✅

The solution

Sovereign Vault replaces PII with stable, HMAC-bound tokens per session. The same value always maps to the same token, so AI can track relationships across a document. You reconstruct locally after the cloud call.

from sovereign_vault import VaultSession

with VaultSession() as vault:
    abstract = vault.tokenize(
        "John Doe (SSN: 123-45-6789) transferred funds to "
        "Jane Smith (SSN: 987-65-4321) via john@firm.com on 2024-01-15."
    )
    # abstract:
    # "[[PERSON_A1B2C3D4_e5f6a7]] (SSN: [[SSN_B8C9D0E1_f2a3b4]]) transferred
    #  funds to [[PERSON_F5G6H7I8_j9k0l1]] (SSN: [[SSN_J2K3L4M5_n6o7p8]])
    #  via [[EMAIL_N9O0P1Q2_r3s4t5]] on 2024-01-15."

    response = your_llm_client.complete(abstract)  # cloud sees only tokens

    result = vault.reconstruct(response)  # real values restored locally
    # VaultSession.destroy() called automatically on context exit

No disk writes. No persistence between sessions. The mapping lives in RAM and is wiped on destroy().

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Detection layers

Three layers run in sequence. Each is optional — the system never falls below Layer 1 reliability.

Layer	Method	Confidence	Requires
1 — Regex	Deterministic structural patterns	1.0	Nothing (always active)
2 — GLiNER	Probabilistic NLP NER	0.85× model score	pip install sovereign-vault[ner]
3 — Ollama	Contextual LLM sweep	0.65	Local Ollama + pip install sovereign-vault[llm]

Layer 3 triggers only when GLiNER finds fewer than 3 entities — handles implicit identifiers and role references that regex and NER miss.

Regex catches: SSN, phone, email, IP address, credit card, passport, Michigan DL, court case numbers

GLiNER catches: person names, organizations, locations, addresses, DOB, financial accounts, government IDs, medical record numbers

Ollama catches: contextual identifiers — "the defendant", "Account #XYZ", implicit role-based references

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Installation

# Core (regex only — no dependencies)
pip install sovereign-vault

# With NLP entity recognition
pip install sovereign-vault[ner]

# With local LLM sweep (requires Ollama running locally)
pip install sovereign-vault[llm]

# Everything
pip install sovereign-vault[all]

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Usage

Basic round-trip

from sovereign_vault import VaultSession

raw = "Alice (alice@corp.com, SSN 123-45-6789) authorized the transfer."

with VaultSession(use_gliner=False, use_ollama=False) as vault:
    abstract = vault.tokenize(raw)
    # Send `abstract` to cloud AI
    cloud_response = call_your_cloud_ai(abstract)
    restored = vault.reconstruct(cloud_response)

LENIENT mode — cloud paraphrased some tokens

with VaultSession(recon_mode=ReconMode.LENIENT) as vault:
    abstract = vault.tokenize(raw)
    cloud_response = call_cloud(abstract)
    # Won't raise even if cloud dropped or paraphrased some tokens
    restored = vault.reconstruct(cloud_response)

SEALED mode — abstract output only, no reconstruction

with VaultSession(seal_mode=SealMode.SEALED) as vault:
    abstract = vault.tokenize(raw)
    # Reconstruction is intentionally disabled
    # Use when the abstract output IS the final product

Audit log — chain of custody, no real values

vault = VaultSession()
vault.tokenize(raw)
for entry in vault.audit_log():
    print(entry["label"], entry["source_layer"], entry["confidence"])
vault.destroy()

Multi-session / server use

from sovereign_vault import new_session, get_session, drop_session

sid, vault = new_session()
abstract = vault.tokenize(raw)
# ... pass sid to the next step in your pipeline ...
vault2 = get_session(sid)
restored = vault2.reconstruct(cloud_output)
drop_session(sid)  # destroys and deregisters

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Security model

• RAM-only, session-scoped — no disk writes, no persistence between sessions
• HMAC-bound tokens — each token carries an HMAC tag derived from a 32-byte session secret; tampered or injected tokens raise VaultSealBreach
• Injection prevention — input containing pre-existing [[...]] vault token format is rejected immediately
• Entropy leak detection — reconstruct() flags high-entropy tokens in cloud output that may be inferred identifiers
• Best-effort memory wipe — destroy() overwrites real values with random bytes before clearing

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Reconstruction modes

Mode	Behavior
ReconMode.STRICT (default)	Raises VaultReconstructionDegraded if cloud dropped any vault token
ReconMode.LENIENT	Allows partial reconstruction — logs missing tokens as warnings
SealMode.SEALED	Disables reconstruction entirely — raises VaultSealBreach if attempted

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Use cases

• Forensic e-discovery — send document patterns to cloud AI without exposing real names or case numbers
• HIPAA pipelines — analyze medical records cross-entity without raw patient identifiers leaving your perimeter
• Financial fraud detection — transaction pattern analysis without raw account numbers
• Gov/defense document processing — reason about relationships in sensitive case files
• Cross-agent PII passing — sanitize data moving between local and cloud agents in an agentic pipeline

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Part of the LexiPro Sovereign OS Ecosystem

sovereign-vault is the privacy layer of LexiPro Sovereign OS — a local-first agentic AI platform running 15 MCP servers, 228 tools, and 20 agent personas on sovereign hardware. In the full OS, it powers Workflow O (Privacy Bridge): tokenize before any cloud call, reconstruct locally after, audit trail preserved.

sovereign-vault can also function as an MCP middleware proxy — intercepting tools/call, resources/read, and prompts/get JSON-RPC 2.0 traffic to tokenize PII before it reaches any third-party LLM. See examples/mcp_tool_server.py for a reference implementation.

Powered by:

• Anthropic Claude — Tier 5 reasoning backbone for multi-file analysis
• Google Gemini — OSINT, research, and long-context processing
• Ollama — Layer 3 local LLM sweep (Gemma, Llama) for contextual entity detection
• GLiNER — Layer 2 zero-shot NLP NER for named entity recognition

📖 Documentation: Architecture · Threat Model · Compliance · Contributing · Changelog

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Contributing

See CONTRIBUTING.md for development setup, testing guidelines, and security boundaries.

Issues and PRs welcome. The detection layer system is designed for extension — add new regex patterns to REGEX_PATTERNS, new GLiNER entity types to _GLINER_TYPES, or swap the Ollama model via ollama_model parameter.

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Known Limitations

Limitation	Impact	Mitigation
RAM-only storage	Vault lost if process crashes mid-pipeline	Call vault.destroy() in a finally block; checkpoint vault keys externally if needed
Probabilistic NER (GLiNER/Ollama)	Novel PII formats may not be detected	Use coverage_report() after tokenize to assess detection quality
Regex layer only on plain text	HTML entities, encoded chars may slip through	Pre-normalize input with html.unescape() before tokenizing
Session-scoped tokens	Same real value gets different token in different sessions	Design your pipeline to tokenize once per document, not per chunk
Not a legal compliance layer	Sovereign Vault assists compliance; it cannot replace legal review	Combine with your organization's data classification policy

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Comparison: Sovereign Vault vs. alternatives

Feature	sovereign-vault	Microsoft Presidio	AWS Comprehend PII	Simple regex redaction
Reversible tokenization	Yes	No (replace only)	No	No
HMAC integrity on tokens	Yes	No	No	No
Offline capable	Yes (regex layer)	Partial	No (API)	Yes
Named entity detection	Yes (GLiNER + Ollama)	Yes (spaCy)	Yes (cloud)	No
STRICT mode audit trail	Yes	No	No	No
Cloud cost	$0 (local)	$0 (local)	Per-call	$0
Setup complexity	pip install	pip + models + server	AWS credentials	None

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Compliance Disclaimer

Sovereign Vault is a technical tool that assists with PII handling in LLM pipelines. It is not a legal compliance product and does not constitute legal advice.

GDPR / HIPAA / CCPA: Tokenizing PII before sending it to a cloud model reduces exposure but does not by itself satisfy the requirements of any data protection regulation. Your compliance obligations depend on your specific use case, data classification, and organizational policies. Consult qualified legal counsel before deploying in regulated environments.

What sovereign-vault does:

• Replaces PII with HMAC-bound tokens so cloud AI never receives raw values
• Provides an audit trail of all vaulted entities (no real values in log)
• Wipes vault from RAM on destroy() call

What sovereign-vault does NOT do:

• Guarantee detection of all PII in all languages and formats
• Provide legal indemnification or certification
• Replace a data classification policy or DPO review
• Encrypt data at rest (vault is RAM-only by design)

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License

MIT — see LICENSE.

Built by Broken Arrow Entertainment LLC · Sovereign Intelligence Systems Group