پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک) پژوهشنامه پردازش و مدیریت اطلاعات 2251-8223 40 ویژه نامه انگلیسی 4 2025 07 23 Advancing Sustainability in IT by Transitioning to Zero-Carbon Data Centers Advancing Sustainability in IT by Transitioning to Zero-Carbon Data Centers 583 608 728197 10.22034/jipm.2025.728197 FA Sarah Ali Abdulkareem Al-Turath University, Baghdad 10013, Iraq 0000-0003-1417-598X Sabah M. Kallow Al-Mansour University College, Baghdad 10067, Iraq 0009-0004-3283-0399 Paizildaev Timur Rashidinovich Osh State University, Osh City 723500, Kyrgyzstan 0000-0001-8909-1655 Salima Baji Abdullah Al-Rafidain University College Baghdad 10064, Iraq 0000-0002-5933-334X Saad T.Y. Alfalahi Madenat Alelem University College, Baghdad 10006, Iraq 0000-0002-4352-650X Journal Article 2025 08 06 <strong>ABSTRACT</strong><br />Cyber threats are changing constantly and these days more than 560,000 new malware varieties are launched daily, which means that rudimentary measures of protecting networks from attacks cannot be of much help in handling real time threats. Single-static security control and manual intervention are insufficient to address APTs, Zero Day, and high-volume DDoS attacks. This is where the application of AI in network security lays its foundation, where real time threat response programs become possible where they are trained to automatically identify, categorize, and mitigate highly complex attacks without requiring massive amount of time and effort.<br />The changing role of AI in network security is examined in this work since it can contribute to the improvement of threat detection, decrease response time, and minimize reliance on human factors. This research reviews more than 150 AI-based security frameworks, and 25 case studies of different industries including finance, healthcare, telecommunications, to assess the efficiency of machine learning and deep learning algorithms for autonomous threat response.<br />The insights show that in challenging contexts, AI-based solutions provide anomaly detection scores of up to 97%, which are far higher than those obtained by conventional systems with average scores of 80%. The response time increased up to 75% as the AI systems responded under 3 seconds during the large scale cyberattack simulation operations. Significant achievement of scalability was across networks with number of nodes more than ten thousand nodes at 90% reliability in different threat scenarios.<br />These findings underscore the importance of AI as the cornerstone of today’s cybersecurity: delivering accurate and timely threat coverage and demonstrating high resilience to threat evolution. However, issues like, algorithm bias, ethical concerns, and resistance to adversarial perturbation calls the need for research to develop effective measures towards the longevity of banking security systems integrated with AI. This study emphasizes the importance of search for new strategies to strengthen current digital environments against the increasing number of threats. <strong>ABSTRACT</strong><br />Cyber threats are changing constantly and these days more than 560,000 new malware varieties are launched daily, which means that rudimentary measures of protecting networks from attacks cannot be of much help in handling real time threats. Single-static security control and manual intervention are insufficient to address APTs, Zero Day, and high-volume DDoS attacks. This is where the application of AI in network security lays its foundation, where real time threat response programs become possible where they are trained to automatically identify, categorize, and mitigate highly complex attacks without requiring massive amount of time and effort.<br />The changing role of AI in network security is examined in this work since it can contribute to the improvement of threat detection, decrease response time, and minimize reliance on human factors. This research reviews more than 150 AI-based security frameworks, and 25 case studies of different industries including finance, healthcare, telecommunications, to assess the efficiency of machine learning and deep learning algorithms for autonomous threat response.<br />The insights show that in challenging contexts, AI-based solutions provide anomaly detection scores of up to 97%, which are far higher than those obtained by conventional systems with average scores of 80%. The response time increased up to 75% as the AI systems responded under 3 seconds during the large scale cyberattack simulation operations. Significant achievement of scalability was across networks with number of nodes more than ten thousand nodes at 90% reliability in different threat scenarios.<br />These findings underscore the importance of AI as the cornerstone of today’s cybersecurity: delivering accurate and timely threat coverage and demonstrating high resilience to threat evolution. However, issues like, algorithm bias, ethical concerns, and resistance to adversarial perturbation calls the need for research to develop effective measures towards the longevity of banking security systems integrated with AI. This study emphasizes the importance of search for new strategies to strengthen current digital environments against the increasing number of threats. https://jipm.irandoc.ac.ir/article_728197_efdbf91271f185ad26a1422b31544dab.pdf