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CyberSentinel: An Emergent Threat Detection System for AI Security

Krti Tallam

TL;DR

CyberSentinel tackles emergent threats in frontier AI by unifying brute-force, phishing, and emergent threat detection within a single-agent orchestrator. The Emergent Threat Detector employs streaming data and unsupervised anomaly modeling, with periodic automated retraining to counter drift, while the brute-force and phishing modules provide real-time, rule-lean defenses. The architecture features multi-threaded execution, automated responses, and seamless integration with SIEM, cloud security, and CI/CD pipelines, enabling scalable deployment with zero-downtime model updates. Overall, the system demonstrates real-time threat detection with adaptive capabilities, offering a practical, scalable solution for securing AI-driven environments against unknown and evolving cyber threats.

Abstract

The rapid advancement of artificial intelligence (AI) has significantly expanded the attack surface for AI-driven cybersecurity threats, necessitating adaptive defense strategies. This paper introduces CyberSentinel, a unified, single-agent system for emergent threat detection, designed to identify and mitigate novel security risks in real time. CyberSentinel integrates: (1) Brute-force attack detection through SSH log analysis, (2) Phishing threat assessment using domain blacklists and heuristic URL scoring, and (3) Emergent threat detection via machine learning-based anomaly detection. By continuously adapting to evolving adversarial tactics, CyberSentinel strengthens proactive cybersecurity defense, addressing critical vulnerabilities in AI security.

CyberSentinel: An Emergent Threat Detection System for AI Security

TL;DR

CyberSentinel tackles emergent threats in frontier AI by unifying brute-force, phishing, and emergent threat detection within a single-agent orchestrator. The Emergent Threat Detector employs streaming data and unsupervised anomaly modeling, with periodic automated retraining to counter drift, while the brute-force and phishing modules provide real-time, rule-lean defenses. The architecture features multi-threaded execution, automated responses, and seamless integration with SIEM, cloud security, and CI/CD pipelines, enabling scalable deployment with zero-downtime model updates. Overall, the system demonstrates real-time threat detection with adaptive capabilities, offering a practical, scalable solution for securing AI-driven environments against unknown and evolving cyber threats.

Abstract

The rapid advancement of artificial intelligence (AI) has significantly expanded the attack surface for AI-driven cybersecurity threats, necessitating adaptive defense strategies. This paper introduces CyberSentinel, a unified, single-agent system for emergent threat detection, designed to identify and mitigate novel security risks in real time. CyberSentinel integrates: (1) Brute-force attack detection through SSH log analysis, (2) Phishing threat assessment using domain blacklists and heuristic URL scoring, and (3) Emergent threat detection via machine learning-based anomaly detection. By continuously adapting to evolving adversarial tactics, CyberSentinel strengthens proactive cybersecurity defense, addressing critical vulnerabilities in AI security.

Paper Structure

This paper contains 57 sections, 1 equation, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Emergent Threat Detector (ETD): A Novel Contribution
  3. Architecture and Data Flow
  4. Feature Extraction and Modeling
  5. Adaptive Learning and Model Updates
  6. Real - Time Detection and Response
  7. Advantages Over Signature - Based Detection
  8. Integration with GitHub Agent Workflows
  9. Background and Related Work
  10. Cybersecurity for AI Systems
  11. Emergent Threats in AI Systems
  12. Agent - Based Cybersecurity
  13. CyberSentinel: A Single - Agent Emergent Threat Detector
  14. System Overview
  15. Brute - Force Attack Detection
  16. ...and 42 more sections

Figures (2)

  • Figure 1: High - level architecture of CyberSentinel. A single - agent orchestrator (security_agent.py) coordinates three detection modules: (1) brute - force detection, (2) phishing detection, and (3) emergent threat detection. Incoming system logs (e.g., SSH, network, GitHub events) feed each module, while alerts and mitigation decisions (e.g., IP blocking) flow back through the orchestrator for unified control.
  • Figure 2: System architecture of the Emergent Threat Detector (ETD). The ETD processes multiple data streams, including system logs, network telemetry, GitHub activity and user behavior. It applies feature engineering techniques and anomaly detection models (Isolation Forest and Mahalanobis Distance) to identify emerging threats. The model continuously updates using historical data for adaptive threat detection.