AI POWERED SMART SURVEILLANCE AND THREAT DETECTION

Abstract

AI-powered cloud security has emerged as a transformative approach to address the
growing complexity and sophistication of cyber threats in the cloud computing era. This study
explores the integration of artificial intelligence (AI) and machine learning (ML) techniques
to enhance threat detection, prevention, and response capabilities within cloud-based security
solutions.
The paper delves into the key drivers behind the adoption of AI-powered cloud security,
including the exponential growth of cloud-based data and applications, the increasing
prevalence of advanced persistent threats, and the need for real-time, adaptive security
measures. It examines how AI and ML algorithms can be leveraged to analyze vast amounts
of security-related data, identify anomalies, and detect emerging threats with greater accuracy
and speed than traditional security approaches.
The study also investigates the various AI-powered security capabilities, such as predictive
analytics, automated incident response, and self-healing systems, that can be integrated into
cloud security platforms. It explores the challenges and considerations associated with the
implementation of AI-powered cloud security, including data privacy, model interpretability,
and the integration of AI with existing security infrastructure.