AEGIS-X: Autonomous Incident Triage Engine

Sub-Second Anomaly Response, Bayesian Fusion, and Physics-Inspired Lateral Movement Forecasting

Empirical Threat & Architecture Analysis: Traditional Tier-1 Human SOC vs. AEGIS-X Autonomous Architecture

1. The Architecture of Multi-Agent Incident Triage

Modern enterprise networks generate upwards of 100,000 security alerts daily. Tier-1 human analysts quickly succumb to cognitive fatigue, dropping critical low-signal anomalies that conceal advanced persistent threats (APTs). AEGIS-X solves this via a hierarchical multi-agent state machine built on LangGraph. Incoming alerts enter an ingestion buffer where deterministic signature rules immediately filter known noise. Borderline anomalies are routed through a cascading inference pipeline:

// Rust router pipeline excerpt
pub async fn route_threat_telemetry(event: &SecurityEvent) -> TriageDecision {
    if let Some(rule_hit) = fast_deterministic_filter(event) {
        return rule_hit;
    }
    // Conformal edge evaluation
    let (confidence, edge_pred) = local_quantized_qwen_evaluate(event).await;
    if confidence >= CONFORMAL_EPSILON_THRESHOLD {
        return edge_pred;
    }
    // High-ambiguity escalation to deep reasoning swarm
    orchestrate_deep_swarm_analysis(event).await
}

2. Log-Odds Bayesian Fusion & Dissent-Gated Consensus

When multiple detection agents analyze an unfolding incident (network telemetry, host process tree, cloud IAM audit), their findings are fused using log-odds Bayesian belief updating: $$L(T | E_{1\dots n}) = L_0(T) + \sum_{i=1}^n \ln\left( \frac{P(E_i | T)}{P(E_i | \neg T)} \right)$$ To prevent hallucinations from precipitating premature host shutdowns, AEGIS-X enforces a strict Dissent-Gated Consensus protocol: destructive containment actions (such as isolating domain controllers) require non-zero consensus across at least three decoupled evaluators.

3. Modeling Lateral Movement via Wave Equations

Lateral movement across enterprise Active Directory graphs mirrors energy propagation across physical lattices. AEGIS-X models attacker traversal using a damped wave equation applied to graph Laplacian operators $\mathcal{L}$: $$\frac{\partial^2 \Phi}{\partial t^2} + \gamma \frac{\partial \Phi}{\partial t} + c^2 \mathcal{L} \Phi = S(t)$$ where $\Phi_i(t)$ represents the predicted compromise probability of asset node $i$. By calculating wave fronts across the credential adjacency graph, the system dynamically isolates high-value targets (Crown Jewels) before the attacker completes privilege escalation.

4. Live Production Validation & Open Source Ecosystem

During live deployment at NEUROBOTS 2026, AEGIS-X demonstrated real-time telemetry processing under platform DDoS pressure, autonomously tracing credential stuffing patterns while simultaneously keeping operational latency under 500ms. The production system is accessible at soc-rust.vercel.app and GitHub.