Advancing Sustainability in IT by Transitioning to Zero-Carbon Data Centers

Ali Abdulkareem, Sarah; M. Kallow, Sabah; Timur Rashidinovich, Paizildaev; Abdullah, Salima Baji; T.Y. Alfalahi, Saad

doi:10.22034/jipm.2025.728197

Advancing Sustainability in IT by Transitioning to Zero-Carbon Data Centers

Document Type : Original Article

Authors

Sarah Ali Abdulkareem ¹

Sabah M. Kallow ²

Paizildaev Timur Rashidinovich ³

Salima Baji Abdullah ⁴

Saad T.Y. Alfalahi ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728197

Abstract

ABSTRACT
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.
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.
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.
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.

Keywords

KEYWORDS: Artificial Intelligence

Network Security

Autonomous Threat Response

Machine Learning

Cybersecurity

Deep Learning

Anomaly Detection

Threat Mitigation

Real-Time Security

AI-Driven Systems (AI)

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