Independent researcher, USA

Title of the Talk:
Beyond the Perimeter: How AI Transforms Log Data into Zero Trust Action

Abstract of the Talk:
Integrating AI and machine learning into log data analysis is a strategic necessity for modern cybersecurity, especially within a Zero Trust framework. As digital infrastructures expand, the sheer volume of logs from diverse sources overwhelms traditional, manual security operations. This makes it difficult for analysts to identify sophisticated threats, leaving organizations vulnerable to attack.
This document offers guidance on using AI/ML models to automate pattern recognition and event correlation, transforming raw log data into actionable intelligence. By training models to detect deviations from normal behavior, security teams can significantly reduce their workload and minimize false positives. We advocate for a customizable approach, empowering Security Operations Center (SOC) teams to develop and fine-tune models tailored to their specific environments.
Advanced techniques like federated learning are explored, which allow for collaborative threat detection while preserving data privacy. The core benefits we discuss include enhanced visibility, more efficient incident detection, and the ability to automate responses. Ultimately, this work provides a practical roadmap for modernizing security operations, ensuring that logging—a cornerstone of Zero Trust—becomes a proactive, intelligent, and scalable defense mechanism.

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