🛡️ OCULTAR Monorepo

Zero-egress PII refinery for AI pipelines. Runs in your infrastructure. Your data never leaves.

[!IMPORTANT] Featured Article: OpenAI shipped a model. We built the system. 📖 OpenAI shipped a model. We built the system. Read on dev.to

Quick Security Stats

Stat	Value
SSRF bypass vectors found + fixed	2
Fail-closed scenarios tested	6
Vault persistence	Named Docker volume
Tier 2 engine	OpenAI Privacy Filter (Apache 2.0)
Key management	Doppler

Welcome to the Unified OCULTAR Engine. This monorepo contains the core refinery, integrated applications, and enterprise security tiers.

Structure

• /apps/ - Applications (Proxy, Sombra Gateway, SLM Engine, Dashboard, Automation Bridge, Web)
• /services/ - Core backend logic (Refinery, Vault, Mock API)
• /enterprise/ - Enterprise security extensions & licensing logic
• /internal/pii/ - Centralized PII detection engine & registry
• /extensions/ - Third-party AI tool integrations (Goose MCP, etc.)
• /docs/ - Technical and product documentation
• /security/ - Regulatory policies and integrity manifests

Security Model

OCULTAR is built on a Zero-Trust for Data architecture. It is designed for senior security engineers who require verifiable guarantees before connecting internal data to external AI providers.

• Zero-Egress: A hard architectural guarantee. All PII detection and tokenization happen within your trust boundary. No network calls are made to third-party detection providers.
• Fail-Closed: 6 critical failure modes are rigorously tested (SLM timeout, vault failure, empty boot-guard, queue saturation, refinery internal error, re-hydration failure). In all cases, OCULTAR blocks the request rather than degrading to plaintext exposure.
• SSRF Protection: Hardened IP/DNS validation blocking RFC 1918 and 169.254.169.254 (IMDS) ranges with active DNS rebinding safety. 2 bypass vectors (including IPv6 loopback and non-standard decimal encoding) were identified and patched during adversarial testing.
• Secure Vault: AES-256-GCM encryption with keys derived via HKDF-SHA256. The vault is persisted via a named Docker volume to survive redeployments while keeping the master key in memory.
• Ed25519 Audit Logs: Tamper-proof, hash-chained audit trails signed with Ed25519. Every vault event (matching or vaulting) is logged for SIEM ingestion and compliance verification.

Multi-Tier Refinery Pipeline

Tokenization is handled via a defense-in-depth pipeline that runs before any payload reaches an upstream AI provider.

Tier	Shield	Technical Description
0.1	Base64 Evasion	Decodes, scans, and re-encodes PII hidden inside Base64/JWT blobs.
0	Dictionary	High-speed protection for VIPs, internal projects, and sensitive org names.
0.5	Pattern + Entropy	Shannon scoring for high-entropy strings, catching keys and tokens.
1	Rule Engine	EMAIL, SSN, IBAN (MOD97), CC (Luhn mod-10), 50+ national ID types.
1.1	Phone Shield	libphonenumber validation to reduce false positives on digit sequences.
1.2	Address Shield	Heuristic street address parser supporting EN/FR/ES/DE.
1.5	Greeting/Signature	Detects names in salutations ("Regards, Jean") and intro sentences.
2	AI NER	OpenAI Privacy Filter — 1.5B param, local inference. Optimized for French Finance.
3	Structural Heuristics	Proximity expansion: [TOKEN] ET Dupont → re-tokenized as single entity.

Why OCULTAR Is Different

Obfuscation-Resistant: Recursive Base64 Scanning

Most PII filters operate on plaintext. A sophisticated attacker can embed sensitive data inside a Base64-encoded blob inside a JSON field, bypassing naive pattern matching. OCULTAR decodes and recursively scans every Base64 segment, running the full pipeline on the decoded content.

Luhn-Validated Credit Card Detection

OCULTAR applies the Luhn algorithm (mod-10 checksum) to every credit card candidate before vaulting it. A match that fails Luhn is passed through without redaction or vault storage, eliminating the noise typical of regex-only filters.

Deterministic Tokens for Privacy-Safe Analytics

Tokens are derived from SHA-256(original_PII). The same input always produces the same token. This allows you to run aggregations, joins, and frequency analysis on fully tokenized data without de-tokenizing it — preserving analytical value while eliminating privacy risk.

Extensions

Mistral Le Chat Extension

Zero-egress PII protection for Mistral Le Chat workflows.

pip install ocultar-mistral-mcp

Claude Desktop Extension

Zero-egress PII protection for Claude AI workflows.

pip install ocultar-claude-mcp

Goose AI Workflow Integration

Zero-egress PII protection for Goose AI workflows.

pip install ocultar-goose-mcp

Read the launch story: OpenAI shipped a model. We built the system.

Integration Boundary

Ocultar's responsibility ends at POST /refine. It returns cleanText and a vault token map. It has no knowledge of downstream AI decisions. Callers must fail loudly if Ocultar is unavailable — never degrade gracefully by passing raw data.

Getting Started

1. Secrets Management: OCULTAR uses Doppler for secure secret injection.

   doppler setup
   

2. Go Workspace:

   go work sync
   

3. Build and Run:

   make build
   ./scripts/start.sh
   

Development

• Documentation: See /docs/reference for architecture details.
• Testing: Run go test ./... to verify all modules.

Discovery & Community

• Topics: privacy, gdpr, pii, golang, ai-security, zero-trust, llm, data-privacy
• License: Apache 2.0 (Open-Core)