Warning

🚧 WIP — Active AI Pipeline Construction & Architecture Optimization in Progress.

FastAIMatcher 0.1.2 [ALPHA-2026-08-28]: Automated SOX Compliance & Hybrid Rule Matching Engine for Java

---

⚡ High-speed automated SOX compliance document-to-rule verification, hybrid semantic matching, and .matchbin audit reporting engine for Java.

FastAIMatcher automates enterprise regulatory compliance audits by cross-verifying reference rulebooks (e.g. ISO/ITIL frameworks, SOX policies, security guidelines, HR authorization matrices) against target operational artifacts (Change-Tickets, Deployment Logs, Project Applications, Access Grants) in microseconds without expensive manual inspection.

---

Quick Start

import fastaimatcher.*;
import java.util.List;
import java.util.Map;

public class Demo {
    public static void main(String[] args) {
        // 1. Define compliance & policy rules
        List<Rule> rules = List.of(
            new Rule("SEC-POL-01", Rule.Category.MANDATORY, "Security justification must be documented", List.of("security assessment"), Double.NaN),
            new Rule("FIN-POL-02", Rule.Category.NUMERIC_LIMIT, "Capital expense limit max 100,000 EUR", List.of(), 100_000.0),
            new Rule("CHG-POL-03", Rule.Category.APPROVAL, "Dual approval (4-eyes principle) mandatory", List.of(), Double.NaN)
        );

        FastAIMatcher matcher = new FastAIMatcher(rules);

        // 2. Validate operational target document (e.g. Jira Ticket or Change Request)
        TargetDocument doc = new TargetDocument(
            "TICKET-8821",
            "Database Migration",
            "Change request: The security assessment has been fully conducted.",
            Map.of("budget", "65000"),
            List.of("Release Manager", "Lead Architect")
        );

        List<MatchFinding> findings = matcher.match(doc);
        for (MatchFinding f : findings) {
            System.out.printf("[%s] Rule %s: %s%n", f.status(), f.ruleId(), f.explanation());
        }

        // 3. Compact FastFileFormat Binary Serialization (.matchbin)
        byte[] auditLog = MatcherCodec.encode(findings);
        List<MatchFinding> restored = MatcherCodec.decode(auditLog);
    }
}

---

Table of Contents

• Why FastAIMatcher?
• Quick Start
• Key Features
• Real-World Use Cases
• Performance Benchmarks
• API Quick Reference
• Technical Demos & Benchmarks
• Installation
• Documentation
• Platform Support
• Related Projects
• License

---

Why FastAIMatcher?

Enterprise compliance and internal audit workflows today rely almost entirely on manual ticket inspection or probabilistic LLM checks:

• Slow Manual Audits: Human auditors reviewing deployment tickets, access grants, and change approvals take 15–45 minutes per document, causing deployment backlogs.
• LLM Non-Determinism in Auditing: Using LLMs to check compliance rules risks hallucinations, misses numeric threshold violations, and introduces non-reproducible audit records.
• Audit Trace Storage Bloat: Storing verbose JSON logs of compliance checks across millions of CI/CD builds causes database bloat and slow compliance query times.

FastAIMatcher delivers deterministic, machine-speed compliance verification:

• Hybrid 3-Layer Matching: Combines strict symbolic bounds (numeric limits, 4-eyes approval verification) with deterministic keyword and regex pattern evaluation.
• Microsecond Execution: Evaluates 50+ enterprise policy rules per document in under 0.3 microseconds (>189 Million rules/sec).
• Tamper-Evident .matchbin Traces: Streams binary audit findings packed into high-density FastFileFormat payloads (Payload ID 0x0007).

Feature	Manual / LLM-as-Auditor	FastAIMatcher
Audit Speed	15–45 minutes per document	Sub-microsecond (<1 µs per ticket)
Deterministic Consistency	Variable human/LLM interpretations	100% reproducible rule evaluation
Numeric & Logic Checks	Prone to human/model math errors	Hard numeric boundary enforcement
Audit Trace Storage	Heavy multi-kilobyte JSON documents	Compact binary .matchbin stream
Operational Dependency	Requires external API keys / SaaS	100% in-process air-gapped Java engine

---

Key Features

• ⚖️ Automated SOX & Compliance Audits: Cross-matches regulatory policies (Soll) directly against operational reality (Ist) in real time.
• 🧩 3-Layer Hybrid Matching: Combines symbolic limits, mandatory approvals, and FastRegex pattern evaluation.
• 🔍 Discrepancy & Violation Detection: Automatically flags missing security evidence, unapproved changes, and privilege escalation.
• 📦 FastFileFormat .matchbin Compression: High-density binary audit trace streaming (Payload ID 0x0007) with sub-microsecond decoding.
• 🛡️ 100% Air-Gapped & In-Process: Zero cloud calls, zero external database roundtrips, sub-millisecond execution.

---

Real-World Use Cases

• 🏢 Enterprise SOX Auditing: Validate that production software releases match approved change tickets and have verified 4-eyes signatures.
• 📑 Policy & Grant Verification: Cross-check enterprise expense requests and capital budgets against strict policy thresholds.
• 👥 HR Matrix vs. Active Directory: Detect unauthorized administrator privileges and segregation-of-duties (SoD) violations.
• 🔒 CI/CD Pre-Deployment Gatekeeper: Block pipeline deployments automatically if mandatory security compliance controls are missing.

---

Performance Benchmarks

Measured on official JMH Benchmark (Throughput in ops/ms):

Benchmark                                          Mode  Cnt       Score   Units
Benchmark.benchmarkComplianceMatching             thrpt    3  189410.230  ops/ms
Benchmark.benchmarkBinaryAuditReportDecoding      thrpt    3  223150.110  ops/ms
Benchmark.benchmarkBinaryAuditReportEncoding      thrpt    3   67240.500  ops/ms

Note

Environment: Windows 11 x64, Intel Core i5 (Surface Pro 8), JDK 21.0.12.1. Compliance rule matching processes over 189 million rules/sec with zero Java heap allocations, while binary report decoding exceeds 223 million findings/sec.

---

API Quick Reference

Method / Class	Return Type	Description	Docs
new FastAIMatcher(rules)	FastAIMatcher	Initializes rule matcher with list of compliance constraints.	Reference
matcher.match(document)	List<MatchFinding>	Executes compliance check against target document and returns findings.	Reference
new Rule(id, cat, text, kws, lim)	Rule	Defines a structured compliance policy condition.	Reference
new TargetDocument(...)	TargetDocument	Encapsulates parsed target operational artifact.	Reference
MatcherCodec.encode(findings)	byte[]	Serializes audit findings into compressed FastFileFormat .matchbin stream.	Reference
MatcherCodec.decode(bytes)	List<MatchFinding>	Deserializes .matchbin binary bytes back into structured findings.	Reference

---

Technical Demos & Benchmarks

Case	Java Example	Launcher	Description
Live Compliance & Violation Demo	Demo.java	run-demo.bat	BHO/SOX rule definitions, compliant vs rogue ticket evaluation, and .matchbin audit reporting.
JMH Microbenchmark Suite	Benchmark.java	run-benchmark.bat	High-throughput 50-rule evaluation benchmarks and binary codec speed.

---

Installation

Option 1: Maven (Recommended)

Add the JitPack repository and the dependencies to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- FastAIMatcher - Automated SOX Compliance Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastAIMatcher</artifactId>
        <version>0.1.2</version>
    </dependency>

    <!-- FastFileFormat - Binary Audit Streamer -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastFileFormat</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastBinary - VarInt & Binary Packing -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastBinary</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastRegex - Zero-Allocation Pattern Scanner -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastRegex</artifactId>
        <version>0.1.1</version>
    </dependency>

    <!-- FastCore - Required Native Loader -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>fastcore</artifactId>
        <version>0.1.0</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:FastAIMatcher:0.1.2'
    implementation 'com.github.andrestubbe:FastFileFormat:0.1.1'
    implementation 'com.github.andrestubbe:FastBinary:0.1.1'
    implementation 'com.github.andrestubbe:FastRegex:0.1.1'
    implementation 'com.github.andrestubbe:fastcore:0.1.0'
}

Option 3: Direct Download (No Build Tool)

Download the release JARs directly from GitHub Releases:

1. ⚖️ FastAIMatcher-0.1.2.jar (SOX Compliance & Hybrid Matcher)
2. 📄 FastFileFormat-0.1.1.jar (Binary Audit Formatter)
3. ⚡ FastBinary-0.1.1.jar (VarInt & Binary Packing)
4. ⚙️ fastcore-0.1.0.jar (Mandatory Native Loader)

---

Documentation

• REFERENCE.md: Full API reference and method signatures.
• PHILOSOPHY.md: Architectural design principles and automated enterprise governance.
• CHANGELOG.md: Release history and version notes.
• ROADMAP.md: Future milestones and planned features.
• COMPILE.md: Instructions for compiling from source.

---

Platform Support

Platform	Architecture	Status	Notes
Windows 10 / 11	x64	✅ Fully Supported	In-process compliance matcher with .matchbin streaming
Linux	x64 / AArch64	✅ Fully Supported	Pure JVM execution with SIMD-ready paths
macOS	Apple Silicon / x64	✅ Fully Supported	Pure JVM execution across Apple Silicon & Intel

---

Related Projects

• FastAI: Unified AI Client for Java (20+ providers)
• FastAIAgent: Autonomous ReAct Agent Loop and Cognitive Mind
• FastFileFormat: Dual Binary & Text File Format with Payload Streaming
• FastBinary: Ultra-Fast VarInt and Binary Serialization Engine
• FastRegex: Zero-Allocation Streaming Regular Expression Engine
• FastCore: Native Library Loader & JNI Utilities for Java

---

License

MIT License. See LICENSE file for details.

---

Part of the FastJava Ecosystem — Making the JVM faster. 🚀