AI-Driven Cybersecurity Risk Management: Leveraging Machine Learning for Automated Threat Detection, Real-Time Risk Assessment, and Regulatory Compliance Auditing

Abstract

Advanced solutions became necessary for cybersecurity threat mitigation because the challenges have grown in complexity. The paper investigates Artificial Intelligence (AI) or Machine Learning (ML) applications for cybersecurity risk management transformation through automated threat recognition and real-time risk evaluation and regulatory compliance assessment capabilities.The researchers apply machine learning algorithms within their investigation to determine how AI technology strengthens threat identification during which improved speed and precision are generated for risk detection. Through AI organizations can execute perpetual risk evaluations which let them respond instantaneously to security risks together with shifting attack procedures and vulnerabilities. The paper demonstrates how AI technology evaluates regulatory compliance auditing while also explaining how it executes automated compliance processes which guarantee standard adherence and minimize human-driven mistakes. The study creates an AI framework which proves how machine learning delivers full-time cybersecurity surveillance with regulatory compliance reporting capabilities for better cybersecurity operational success and efficiency. The study shows artificial intelligence systems have the capability to handle the rising cybersecurity challenges and to boost operations' flexibility and meet their regulatory needs. This study demonstrates an advanced method of cybersecurity risk management through AI tools which secure digital assets along with regulatory adherence in the rising digital environment.