N3uralia Security Suite

AI-powered autonomous cybersecurity platform inspired by Darktrace, SentinelOne, and Trend Micro, designed for serverless and edge environments

Overview

The N3uralia Security Suite is a next-generation cybersecurity platform that deploys lightweight AI agents across your infrastructure to provide autonomous threat detection and response. Unlike traditional security tools that rely on static signatures and rules, our agents learn the unique "pattern of life" for each workload and autonomously respond to threats in real-time.

Built for modern cloud-native and serverless architectures, the system operates with minimal overhead (target <10% resource usage) while providing enterprise-grade security capabilities including explainable AI, federated learning, and immutable audit trails.

Key Features

Self-Learning Baselines

Each agent builds a unique 'pattern of life' model for its environment, adapting to normal behavior over time without static rules

Autonomous Response

Hierarchical mitigation system starts with gentle actions (rate limiting) and escalates to aggressive containment based on risk scores

Federated Intelligence

Agents share lightweight indicators and model updates to benefit from collective learning without exposing sensitive data

Explainable AI

Every detection and mitigation includes human-readable rationale showing which features triggered and what the risk score was

Multi-Domain Correlation

Correlates anomalies across network, process, filesystem, and API domains to detect sophisticated multi-stage attacks

Safe Rollouts

Policy versioning and staged deployment ensure new detection models don't disrupt operations with false positives

Architecture

Agent Sidecar

Lightweight process co-located with workloads that monitors local behavior, detects anomalies, and executes mitigations

Local telemetry collectionBaseline profilingReal-time anomaly detectionAutonomous mitigation execution

Control Plane

Central orchestration system that coordinates agents, manages policies, correlates threats, and provides operator interface

Agent registration & health monitoringPolicy distributionCross-agent correlationIncident management

Detection Engine

ML-powered system that trains baselines, detects drift, identifies anomalies, and calculates risk scores

Baseline trainingDrift detectionAnomaly scoringThreat correlation

Threat Intelligence

Integration layer that ingests external threat feeds and enriches detections with known indicators of compromise

Feed ingestion (JSON, CSV, STIX)Indicator matchingAnomaly enrichmentThreat scoring

How It Works

  1. 1
    Deploy Agents

    Install lightweight security agents as sidecars alongside your workloads (Node.js, Docker, Kubernetes)

  2. 2
    Learn Baselines

    Agents observe normal behavior for 24-48 hours, building statistical models of typical patterns

  3. 3
    Detect Anomalies

    Real-time detection of deviations from baseline using ensemble ML models and threat intelligence

  4. 4
    Autonomous Response

    Agents automatically mitigate threats with graduated responses, from rate limiting to full quarantine

  5. 5
    Investigate & Learn

    Security operators review incidents with explainable AI insights, and the system learns from feedback

Best Practices

Start in monitor-only mode - Let agents learn baselines for 48 hours before enabling autonomous mitigation

Use staged policy rollouts - Deploy new detection models to a canary group before full deployment

Review audit logs regularly - Examine agent decisions to tune thresholds and reduce false positives

Integrate threat intelligence - Ingest external feeds to enrich detection with known IOCs

Secure the agents themselves - Use signed updates, sandbox policy logic, and isolate agent processes