ZENTRIC PROTOCOL

PII Integrity · Deterministic Infrastructure · Secure Protocol

The protocol layer between intent and execution in AI systems.
Every signal examined. Every verdict signed. Nothing passes without record.

→ Request Access · Documentation · Integrity Report v1.0

This repository exists for transparency and contribution — not as a deployable alternative to the hosted service.

Cloning this repository does not give you access to the Zentric processing service. The detection engine that inspects prompts, detects PII, and generates signed audit reports runs on Zentric's infrastructure and requires an active license.

Because trust is infrastructure. You should be able to verify how authentication works, how your API key is validated, and how subscription state is checked before your requests reach the engine. We believe in auditability at every layer — including our own enforcement code.

We accept contributions to the middleware, webhook handler, and Supabase schema. Open a PR or file an issue. For security-related contributions, see the Security section.

Zentric Protocol is an infrastructure integrity layer for AI systems. It sits between your application and your LLM, examining every signal — prompts, responses, user inputs — and returning a cryptographically-signed verdict before execution continues.

It is not a filter. It does not guess. It applies deterministic rules across a standardized pipeline and returns a structured, auditable JSON report for every request.

Input Signal
     │
     ▼
┌─────────────────────────────────────────┐
│           ZENTRIC PROTOCOL              │
│                                         │
│  ┌─────────────┐  ┌─────────────────┐  │
│  │IntegrityGuard│→│  PrivacyGuard   │  │
│  │ 22 injection │  │  12 PII types   │  │
│  │  signatures  │  │  7 languages    │  │
│  └─────────────┘  └────────┬────────┘  │
│                             ▼           │
│                    ┌──────────────┐     │
│                    │ ZentricReport│     │
│                    │ UUID+SHA-256 │     │
│                    │  GDPR Art.30 │     │
│                    └──────────────┘     │
└─────────────────────────────────────────┘
     │
     ▼
Verdict + Certificate → Your System

Zentric uses deterministic signature matching — not an ML classifier. Every block is a known pattern match, which means 100% precision on known patterns and zero false positives: nothing is ever blocked unless it matches a catalogued signature. Verdicts are returned in sub-millisecond time (no model in the hot path), so the same input always produces the same verdict.

Any published metric is reproducible: run npm run benchmark (benchmarks/run.mjs) against the public deepset/prompt-injections dataset to verify the numbers yourself.

Detects prompt injection, jailbreak attempts, and instruction overrides before they reach your LLM.

• 22 catalogued injection signatures
• 7 supported languages (EN, ES, FR, DE, PT, ZH, JA)
• Multilingual NLP classification layer
• Mean server-side processing: <0.1ms (sub-millisecond; no model in the hot path)

Identifies and anonymizes PII in prompts and responses. Regional standards treated as first-class entities.

• 12 PII entity types, format-validated (Luhn, IBAN mod-97, mod-11, NIF/NIE checksum): SSN, NIF, CPF, CURP, IBAN, SWIFT, passport, email, phone, and more
• Regional pattern recognition (EU, US, LATAM)
• Anonymization operators: redact, mask, tokenize, pseudonymize

Every request that passes through the protocol generates a signed, immutable audit record.

{
  "report_id": "zp_01HXYZ...",
  "uuid": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "timestamp_utc": "2026-05-14T22:00:00.000Z",
  "sha256": "e3b0c44298fc1c149afb...",
  "verdict": "CLEARED",
  "integrity": {
    "injection_detected": false,
    "signatures_matched": [],
    "confidence": null
  },
  "privacy": {
    "pii_detected": true,
    "entities": [
      { "type": "EMAIL", "action": "REDACTED", "position": [42, 61] }
    ]
  },
  "compliance": {
    "audit_record": true,
    "ccpa": true,
    "eu_ai_act_s52": true
  },
  "latency_ms": 0.05
}

curl -X POST https://api.zentricprotocol.com/v1/analyze \
  -H "Authorization: Bearer zp_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Your prompt or user input here",
    "modules": ["integrity", "privacy"],
    "options": {
      "anonymize": true,
      "language": "auto"
    }
  }'

{
  "status": "ok",
  "verdict": "CLEARED",
  "report": { ... },
  "anonymized_input": "Your prompt or user input here",
  "latency_ms": 0.05
}

Zentric Protocol is designed from the ground up for regulated AI deployments.

→ See plans · → Get API key · → Contact for Enterprise

Deterministic. The same input always produces the same verdict. No probabilistic black boxes in the critical path.

Stateless. The protocol does not store your data. Each request is processed and returned. The audit record is yours.

Composable. Deploy the full stack, a single guard, or wire only the audit layer into existing infrastructure.

Auditable. Every verdict is signed with SHA-256, timestamped in UTC, and assigned a UUID. Your compliance team will thank you.

Agent attacks don't arrive through the chat input. They arrive through tool call responses, RAG chunks, and memory retrievals — any external content that enters the prompt window. Your system prompt doesn't protect you here: it doesn't run until after the input is already parsed.

Wire Zentric at every ingestion point, not just on user messages:

• LLM input — user messages before they reach the model
• Tool output — external API responses before they re-enter the context window
• RAG retrieval — document chunks before they are assembled into the prompt
• Memory reads — stored context before it is injected into the next turn

One POST to /v1/analyze. The verdict comes back in sub-millisecond time. The agent continues or halts based on the result. Nothing else changes in your pipeline.

curl -X POST https://api.zentricprotocol.com/v1/analyze \
  -H "Authorization: Bearer zp_live_..." \
  -H "Content-Type: application/json" \
  -d '{"input": "<tool_output_or_rag_chunk_here>", "modules": ["integrity", "privacy"]}'

Zentric Protocol ships a native Model Context Protocol (MCP) server that integrates directly with Claude Desktop and any MCP-compatible agent runtime.

The MCP server exposes Zentric's detection engine as a native MCP tool. When wired into Claude Desktop, the agent automatically calls analyze_prompt before sending any input to the LLM — user messages, tool responses, RAG chunks, and memory retrievals are all checked.

analyze_prompt(text: string) -> ZentricReport

Returns: verdict (CLEARED / BLOCKED), risk_score, matched signatures, pii_entities, report_hash (SHA-256), latency_ms.

npx zentric-protocol-mcp

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "zentric-protocol": {
      "command": "npx",
      "args": ["zentric-protocol-mcp"],
      "env": {
        "ZENTRIC_API_KEY": "your_api_key"
      }
    }
  }
}

Get your API key at zentricprotocol.com/quickstart — free tier is 10,000 requests/month, no credit card required.

The MCP server source code is in /mcp-server. It is built with the Model Context Protocol SDK and published to npm as zentric-protocol-mcp.

We take the security of this protocol seriously. If you discover a vulnerability, please report it responsibly.

• Email: core@zentricprotocol.com
• Subject: [SECURITY] <brief description>
• Response SLA: 48 hours acknowledgement, 7 days resolution target

We do not operate a public bug bounty program at this time. Responsible disclosure is acknowledged in our changelog.

• IntegrityGuard v1.0 — 22 signatures, 7 languages
• PrivacyGuard v1.0 — 12 PII types, EU/US/LATAM
• ZentricReport v1.0 — SHA-256, UUID, GDPR Art.30
• REST API (production)
• Python SDK — Q3 2026
• Node.js SDK — Q3 2026
• Streaming support (SSE) — Q3 2026
• Webhook callbacks — Q4 2026
• Self-hosted deployment option — 2027