AI LEGION

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  AI LEGION

PROJECT DESCRIPTION Pegasus OS: AI Legion Command Center for Autonomous Incident Response Overview

Pegasus OS is an interactive, agentic command center that visualizes and orchestrates a legion of AI-driven security analysts. Built as a proof-of-concept for the SIFT Workstation environment, it demonstrates how multiple autonomous agents can collaborate on incident response tasks while maintaining rigorous self-correction, accuracy validation, and structured analytical reasoning.

The interface presents a real-time dashboard where specialized agents (Memory Forensics, Filesystem Triage, Network PCAP, Log Analysis, YARA Scanning) execute parallel investigations. Each agent's findings are anchored to specific artifact references—file offsets, log line numbers, PCAP frame IDs, or registry paths—ensuring complete traceability. When inconsistencies or hallucinations are detected, the system autonomously triggers correction cycles, revalidates evidence, and updates its confidence metrics. Key Features Feature Implementation Self-Correction Automatic detection of high CPU load, missing artifact traces, or ambiguous indicators triggers remediation actions without human input Accuracy Validation Every finding includes a traceable artifact reference (e.g., volatility3 - offset 0x7ffe1000, syslog: line 2041) Analytical Narrative Structured console logs present investigations as a coherent story, not raw execution dumps Guardrails Architectural boundaries prevent hallucination propagation; prompt-based constraints enforce tool-call verification Audit Trail Full execution logs exportable as JSON with timestamps, agent communications, and tool execution sequences Phi-Harmonic Visualization Sacred geometry canvas symbolizing coherence across the agent mesh—optional, aesthetic representation of reasoning depth How It Works

Agent Deployment – Each specialized agent runs autonomously, scanning evidence types (memory dumps, logs, PCAPs).

Artifact Anchoring – Findings are bound to specific evidence coordinates (offsets, line numbers, hashes).

Continuous Validation – A supervisory loop checks for hallucinations, missing traces, or performance anomalies.

Self-Correction – When issues arise, the agent re-analyzes, adjusts its approach, and logs the correction.

Exportable Audit – Investigators can export the complete execution log for external verification.

Use Cases

Autonomous triage of compromised endpoints

Cross-referencing indicators across multiple log sources

Validation of YARA rule matches against file offsets

Training and demonstration of agentic IR principles

Technology Stack

Frontend: HTML5, CSS3, JavaScript (ES6)

Visualization: Canvas API for real-time harmonic wave rendering

Agent Framework Simulation: Custom JavaScript agent orchestration (extensible to Claude Code / OpenClaw)

Deployment: Static web application, runs in any modern browser or embedded in SIFT Workstation via chromium --app

Built With

• apis
• cloud-services
• databases
• frameworks
• html
• or
• other
• platforms
• technologies