Skip to content
Open access

Design of the Intelligent Security Operations Automation Algorithm for AI-Enabled Security Orchestration, Automation, and Response with Comparative Analysis Against Rule-Based SOAR Platform

2026 · International Journal of Engineering and Computational Applications · 0 citations

TL;DR

An Intelligent Security Operations Automation Algorithm (ISOAA) for AI-enabled SOAR is proposed that achieves lower mean time to detect, lower mean time to respond, higher containment success, improved precision and recall, reduced false-positive automation, and lower compliance-breach rate than rule-based SOAR.

Abstract

Security Operations Centers (SOCs) increasingly rely on Security Orchestration, Automation, and Response (SOAR) platforms to manage high-volume alerts, enrich telemetry, execute playbooks, and shorten incident-response cycles. However, many deployed SOAR systems remain rule dominated: actions are triggered by static if-then playbooks, threshold scores, and analyst-defined routing logic. Such deterministic automation is auditable and operationally useful for known, repetitive events, but it becomes brittle when adversary behavior shifts, telemetry quality varies, alert streams are noisy, assets have unequal business criticality, and compliance constraints differ across response contexts. This paper proposes an Intelligent Security Operations Automation Algorithm (ISOAA) for AI-enabled SOAR. The algorithm integrates probabilistic alert risk scoring, event-graph representation, constrained response optimization, governance-risk-compliance (GRC) gating, human-in-the-loop validation, and feedback-based policy improvement. The mathematical core models each alert as a feature-bearing security object, transforms heterogeneous telemetry into a state representation, estimates actionable incident probability, and selects response actions by maximizing expected security utility subject to operational cost, false-positive loss, and compliance penalty. A rule-based SOAR baseline, an ML-assisted triage baseline, and a reinforcement-learning cyber-response baseline are used for comparative analysis. Controlled benchmark results indicate that ISOAA achieves lower mean time to detect, lower mean time to respond, higher containment success, improved precision and recall, reduced false-positive automation, and lower compliance-breach rate than rule-based SOAR. The paper contributes a defensible mathematical architecture for intelligent SOC automation and offers practical deployment recommendations for risk-aware, auditable, and GRC-constrained response orchestration.

Read PDF

Similar papers

Open access Sep 2026

A rule-driven SOC architecture for real-time threat detection and autonomous incident response

A rule-driven SOC architecture that integrates cloud telemetry, threat intelligence, MITRE ATT&CK Cloud Matrix mappings, and SOAR-based response automation is proposed that demonstrates a 41.2% reduction in time-to-detection, a 37.5% reduction in time-to-response, and 87.9% precision in automated remediation.

Jilika Jithendarnadh, J. Balaraju · 0 citations
Conference Open access 2026

Smart Contract-Based Automated Response System for IoT Attacks in Web3 Ecosystems

Experimental evaluation demonstrates up to 95% detection accuracy, a 50% reduction in response latency, and scalability to over 100,000 IoT devices without performance degradation, highlighting the suitability of SC-ARS for deployment in smart cities, industrial IoT, and decentralized critical infrastructures where tru...

S. Bassey, B. Stephen, Emediong Bassey Obot et al. · 0 citations
Open access Oct 2026

AI-Based Security Posture Management as an Enabling Layer for Real-Time NIST Cybersecurity Framework Adherence: A Theory- Grounded Conceptual Framework

A theory-grounded conceptual framework is developed that positions AI-Based Security Posture Management (ASPM) as the enabling layer through which CSF functions are transformed from periodically evidenced artifacts into continuously operating control mechanisms.

Chukwunenye Amadi · 0 citations
Open access Sep 2026

SOC-in-a-Box: A Multi-Agent LLM-Based Security Operations Center for Threat Detection and Automated Incident Response

SOC-in-a-Box, a multi-agent prototype that automates the three core SOC functions—detection, investigation, and response—using specialised AI agents powered by a large language model (LLM), demonstrates that a self-contained, locally deployable multi-agent architecture can meaningfully reduce manual effort in routine S...

D. S, Pallavi U, K. Devika · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.