Year: 2024
Type: article
Abstract: This paper sheds some light on how AI-powered cybersecurity can be applied to protecting storage infrastructures, namely, high-throughput NFS and S3 object stores. As data becomes more sensitive and volumes larger, conventional security is failing and perhaps the most vulnerable to this are AI/ML data. The research suggests taking into consideration the behavior-based threat identification, which reflects application to detection of ransomware, data exfiltration, insider threats, and others, prior to their evolvement. An AI can proactively identify anomalies by studying the activities and actions of the users and systems and help raise an alert on the occurrence of a possible breach. The article also discusses the integration of AI systems with SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) tools, leveraging Open Telemetry for seamless coordination and real-time threat response. As it suggests the sure need to adopt appropriate security measures to highly sensitive AI/ML datasets, the article lends prominence to the flexibility and scalability of AI-enhanced cybersecurity as a solution to security issues concerning storage in a dynamic environment.
Author: Oluwatosin Oladayo Aramide
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412185564