Authors:
Vamshidhar Reddy Vemula
Addresses:
Department of Artificial Intelligence and Cloud Security, Intalent LLC, Plano, Texas, United States of America. vvamshidharreddy1@gmail.com
Cognitive AI systems for proactive threat hunting in AI-driven cloud applications are examined in this research. Cognitive AI was compared to traditional, signature-based, behavioural-based, hybrid, AI-based, and manual review solutions for various cybersecurity risks. We measured detection accuracy and response time. We extracted sample data points from simulated threat scenarios with seven unique cloud application risks. Cognitive AI consistently outperformed other systems in danger detection, obtaining 89-98% accuracy. Cognitive AI had the fastest response time, 30–50 ms. Traditional and signature-based systems take 70 ms to 100 ms to respond, whereas manual review systems take 100 ms to 130 ms. Data processing and bar and line graphs are implemented in this Python script. Mathematica will be used to ensure comparable outcomes for more complicated data processing and mathematical modelling. The results show that Cognitive AI performs superior in adaptive real-time threat detection of cloud application security. This study shows that cloud infrastructures need AI-powered technologies to improve security and reaction times. This will keep organizations up to date on emerging cyber dangers.
Keywords: Proactive Threat Hunting; Cloud Security; AI-Driven Applications; Machine Learning; AI-based Signature-Based Systems; Python Script of Data; Mathematical Modelling; AI-powered Systems; Cloud infrastructures; Fast-Emergent Cyber Threats.
Received on: 19/03/2024, Revised on: 21/05/2024, Accepted on: 03/07/2024, Published on: 01/09/2024
AVE Trends in Intelligent Computing Systems, 2024 Vol. 1 No. 3, Pages: 173-183