A Review Of Ai-based Cybersecurity Monitoring Systems – Securai Sentinel: The Intelligent Threat Detection Platform

Abstract

Cybersecurity Threats Have Grown Exponentially In Complexity And Volume, Rendering Traditional Rule-based Monitoring Tools Increasingly Inadequate For Modern Enterprise And Institutional Environments. Existing Security Platforms Are Often Siloed, Lack Intelligent Reasoning Capabilities, And Fail To Integrate Cross-domain Threat Intelligence With Automated Response Workflows. This Review Paper Surveys The Evolution Of AI-based Cybersecurity Monitoring Systems, Examining Machine Learning-based Intrusion Detection, Threat Intelligence Platforms, Dark Web Surveillance Systems, MITRE ATT&CK-aligned Detection Frameworks, And Large Language Model Applications In Security Operations. Through Systematic Examination Of Eight Significant Research Contributions, Five Persistent Research Gaps Are Identified: Absence Of Unified Multi-domain Threat Correlation, Exclusion Of AI-driven Red Team Simulation, Reliance On Static Signature-based Rule Databases, Lack Of Natural-language Explainability In Threat Analysis, And The Absence Of Integrated Zero-trust Policy Management. These Gaps Collectively Justify The Conceptual Design Of SecurAI Sentinel, A Proposed Full-stack AI-powered Cybersecurity Web Application Integrating Eight Intelligence Modules—CVE Intelligence Hub, Dark Web Monitor, MITRE ATT&CK Mapper, Incident Response Playbook Generator, Forensics Timeline Builder, Packet Capture Analyzer, AI Red Team Agent, And Zero Trust Policy Builder—within A Unified Glassmorphic Interface Powered By Google Gemini AI, React, Node.js, And Express.js. The Paper Concludes With A Discussion Of The System's Feasibility, Societal Impact, And Directions For Future Research.

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