OPTIMIZING INCIDENT RESPONSE IN CLOUD SECURITY WITH AI AND BIG DATA INTEGRATION

Abstract

This study investigates how to combine big data analytics and artificial intelligence (AI) to optimize incident response in cloud security. Strong security measures are becoming essential as more and more businesses use cloud environments. The study looks into how AI and Big Data can work together to improve incident response systems by using real-time data processing, predictive analytics, and machine learning. By integrating AI, security events in the cloud can be proactively identified and mitigated, reducing the possibility of negative effects on business operations. Big Data technologies, on the other hand, manage a variety of datasets with efficiency and offer insights into patterns, abnormalities, and possible security breaches. A collaborative and automated incident response system is formed by essential elements such as threat intelligence integration, behavioral analytics, and anomaly detection. Investigating modern tools is essential. These include XDR, SIEM, SOAR, NDR, and real-time monitoring and network forensics. These tools provide insights into the dynamic field of detection and response systems. Security and threat detection have significantly improved as a result of the cloud mov]ement of business data and apps. To protect against sophisticated threats inside the delicate network infrastructures of cloud settings, traditional security techniques need to be updated. Artificial intelligence (AI) steps in to help improve the precision and speed of threat assessment and response by comprehending this difficulty. The impact of AI on cloud security and threat detection is illustrated in this research. The increasing focus of cyber-attacks on cloud infrastructures and service providers has made it necessary to have strong, simply deployed security solutions.