Autonomous Threat Detection and Response in Cloud Security: A Comprehensive Survey of AI-Driven Strategies

TL;DR

This paper surveys AI-driven strategies for autonomous threat detection and response in cloud security, highlighting recent advances, challenges, and future directions for scalable, adaptive, and intelligent security systems.

Contribution

It provides a comprehensive overview of AI techniques in cloud security, analyzing their strengths, weaknesses, and potential for autonomous, real-time threat management.

Findings

AI enhances detection accuracy and reduces false positives.

Fusion of language models improves threat response speed.

Key issues include data privacy and adversarial attacks.

Abstract

Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead. But there are serious security concerns which are brought about by its distributed and multi-tenant characteristics. The old methods of detecting and reacting to threats which are mostly reliant on fixed signatures, predefined rules and human operators are becoming less and less effective even in the advanced stages of cyberattacks of cloud infrastructures. The recent trend in the field of addressing these limitations is the creation of technologies of artificial intelligence (AI). The strategies allow independent protection, anomaly detection, and real-time analysis with references to using deep learning, machine learning, and reinforcement learning. Through imbuing AI with a constantly-learning feature, it enables the intrusion detection system to be more accurate and generate a lesser number of false positives and it also enables the possibility of adaptive and predictive security. The fusion of large-scale language models with efficient orchestration platforms contributes to reacting to the arising threats with a quicker and more precise response. This allows automatic control over incidences, self-healing network, and defense mechanisms on a policy basis. Considering the current detection and response methods, this discussion assesses their strengths and weaknesses and outlines key issues such as data privacy, adversarial machine learning and integration complexity in the context of AI-based cloud security. These results suggest the future application of AI to support autonomous, scalable and active cloud security operations.