🛡️ Enterprise AI Compliance Firewall & Benchmark Harness

Powered by TypeSafe AI Jev "System One" Decision Engine (RLCD)

In modern enterprise AI pipelines, up to 40% of incoming LLM requests do not require slow, generative token-by-token reasoning. Instead, they require fast, deterministic, and calibrated decision-making (e.g., intent routing, compliance gating, PII filtering, prompt injection defense, or model orchestration).

Relying on heavy System Two LLMs (like GPT-4, Claude, or LLaMA) for simple gating tasks causes unnecessary latency (1.5–8.7 seconds), high inference token costs, and uncalibrated text outputs. Conversely, static PII detectors like Microsoft Presidio miss 100% of prompt injection and malware threats.

This project introduces a high-performance Enterprise Compliance Firewall leveraging TypeSafe AI's Jev model trained via Reinforcement Learning for Calibrated Decisions (RLCD).

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⚡ Empirical 3-Way Head-to-Head Performance

Metric	Microsoft Presidio	Local Ollama LLM (qwen2.5:14b)	Jev RLCD Two-Tier Gateway	Impact
Mean Latency (P50)	13.05 ms	1,175.37 ms	2.73 ms	⚡ 652x Faster than Ollama LLM
Tail Latency (P95)	23.99 ms	8,732.82 ms (8.7s)	4.43 ms	🚀 Sub-5ms Fast-Path Execution
PII Detection	✅ (SSNs, Cards, Emails)	⚠️ (Requires verbose prompt)	✅ (Instant Tool + Jev Primitive)	Complete PII & Token Scanning
Prompt Injection Defense	❌ 0% Detection (Blind)	✅ (Accurate but 1.8s–8.7s)	✅ (Detects Injection in < 3ms)	Blocks DAN / Hacks / Overrides
Inference Cost	$0	High Token Inference Cost	$0 / Micro-pass	💰 > 99% Token Cost Savings

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🏗️ Architecture & Data Flow Diagram (DFD)

flowchart TD
    User["Client / User Payload"] --> Gateway["API Entry Gateway"]
    
    subgraph SystemOne["System One Gating Tier (< 3ms Local)"]
        Gateway --> PIITool["PII Scanner"]
        Gateway --> InjTool["Prompt Injection Detector"]
        Gateway --> ToxTool["Toxicity Scanner"]
        
        PIITool --> JevEngine["Jev RLCD Decision Engine"]
        InjTool --> JevEngine
        ToxTool --> JevEngine
        
        JevEngine --> Primitives["Typed Primitives Output<br/>(Choice, Score, Noul)"]
    end
    
    Primitives --> CalibCheck{"Calibrated Confidence Gating<br/>(Threshold >= 0.90)"}
    
    subgraph Actions["Action Execution Paths"]
        CalibCheck --> |"Threat Detected (Prob >= 0.90)"| Block["BLOCK IMMEDIATE<br/>(HTTP 403 Forbidden)"]
        CalibCheck --> |"Clean Query (Prob >= 0.90)"| FastPass["FORWARD TO APP<br/>(HTTP 200 Fast Pass)"]
        CalibCheck --> |"Ambiguous / Low Confidence"| Escalate["SYSTEM TWO LLM<br/>(Ollama / GPT-4 Fallback)"]
    end

    Block --> DBLog["SQLite Audit Logger (core/database.py)"]
    FastPass --> DBLog
    Escalate --> DBLog

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🧩 Key Technical Primitives

Instead of unparsed JSON strings or free-form text, Jev evaluates state against strict typed primitives in a single parallel pass:

1. Choice[T]: Categorical classification across up to 255 predefined choices (e.g., SAFE, PROMPT_INJECTION, PII_LEAK, TOXIC_CONTENT).
2. Score: Bounded numerical rating or ordered level score (LOW, MEDIUM, HIGH, CRITICAL).
3. Noul: Exact probability score for binary/boolean decisions (is_security_threat + calibrated probability %).

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🚀 Quick Start Guide

Prerequisites

• Python 3.10 or higher
• Local Ollama (optional, for System 2 fallback evaluation)

1. Clone & Install

git clone https://github.com/vikasums/rlcd-jev.git
cd rlcd-jev

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install editable package
pip install -e .

2. Run Head-to-Head Live Benchmark (Jev vs Presidio vs Ollama)

PYTHONPATH=src python3 src/rlcd_jev/benchmark/head_to_head_benchmark.py

3. Run Automated Unit & Integration Test Suite

python3 main.py --test

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🤝 Contributing & License

Distributed under the MIT License. See LICENSE for more information.