Sarah Ali Abdulkareem

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.

Background: The availability of advanced digital technology and evolving need for high speed and low latency connections have put pressures on the existing optical fiber networks. New technologies like the Wavelength Division Multiplexing (WDM), Photonic Integrated Circuits (PICs), Mode Division Multiplexing (MDM) and Quantum Communication will be valuable towards the achievement of these demands. Objective: The study examines the capability, expansiveness, and cost-effectiveness of current and emerging optical fiber systems for the development of future Internet technology. The research also seeks to assess these formations to improve data transmission rates, network response time, secure and efficient networks’ solutions. Methods: This is a mixed methods study where both experimental and computational data were collected and analyzed accompanied by theoretical insight. The results that were compared included transmission rate, spectral efficiency, signal integrity and lifecycle costs. Specific work was done on multi-band WDM, PIC-based systems, optical QKD along with simulation studies on large scalable multi-core and mode-division architectures. Results: The article samples acknowledge improved network capabilities with increased transits per watt by 300% in multi-band WDM and reduction of latency levels by employing edge computing. The tested PIC-based systems were shown to be more efficient than the comparable existing systems and quantum communication proved to be reliable method for transmitting data over short to medium distances. Conclusion: Today, it can be stated that the advanced optical fiber technologies are of great value for the construction of high speed, large bandwidth and secure Internet connection. Their integration can reportedly conquer future connectivity issues but new development is required to come over the barriers of deployment and sustainability.

Background : As digital transformation accelerates globally, effective IT governance has become critical for organizational success. With global spending on IT governance and risk management projected to reach $16 billion by 2025, emerging technologies such as artificial intelligence (AI), blockchain, and cloud computing are introducing new governance complexities that demand adaptive strategies. Objective : The article explores the key factors and anticipated trends in IT governance that are expected to shape organizational management by 2025. The aim is to understand how evolving technological landscapes influence governance models and risk management practices. Method : A qualitative methodology was adopted, involving a systematic review of 100 scholarly and industry articles focused on recent trends and future directions in IT governance. The analysis highlights issues related to risk management, regulatory compliance, cybersecurity, and technology integration. Results : The review revealed that 83% of organizations reported significant governance challenges due to technological disruption, while 68% indicated a transition toward decentralized governance models, particularly within blockchain-based systems. Additionally, AI-powered decision-making tools are projected to be adopted by over 70% of large enterprises for IT governance functions by 2025. Conclusion : The findings underscore the growing need for flexible and adaptive IT governance frameworks that align with both agile and traditional business objectives. By anticipating and addressing future risks and compliance demands, organizations can enhance their current governance strategies to remain resilient and competitive in the digital era.

Background: Telecommunications networks are exposed to numerous issues concerning equipment and that causes network outage, which proves very expensive. Basic maintenance methodologies like reactive or even scheduled preventive maintenance cannot cope up with the increasing trends in the facilities of telecom companies. Objective: The article examines how AI is applied to support predictive maintenance so that telecommunication networks can perform as intended with reduced downtime. Methods: The review of existing AI algorithms is presented, focusing on the ML models and deep learning methods. Network operations and maintenance logs are analyzed for data to assess the capabilities of the AI models in terms of prediction. It identifies and analyses such quantifiable parameters as the failure rate prediction accuracy and the response time cut. Results: Computerisation of the forecast maintenance revealed a corresponding decrease in equipment failure incidences and generally reduced time lost due to unscheduled stops. Through the improved network performance, the response to potential threats was quicker than before and services became more reliable and inexpensive to offer. Conclusion: To reduce network outages, reduce network vulnerability, and maximize the efficiency of telecommunications operations, the use of AI-based predictive maintenance can be viewed as a prospect. As technology advances, newer versions of AI algorithms will provide improved predictive strength and incorporation into the telecommunications system.

Background: Telecommunication system plays a crucial role in fast development of energy demand growth and carbon dioxide emissions. As sustainability becomes part of corporate goals green telecommunications strive to bring innovation in energy efficiency. Objective: As part of examining the state of art developments in energy-efficient networking technologies and approaches to minimize power consumption in telecommunication facilities, the important global task of using green telecommunication for sustainable development goals is highlighted. Methods: A literature review and analysis were successfully performed to examine the use of advanced hardware technologies, SDN technology, NFV, and intelligent renewable energy integration. Some of the green telecommunication’s solutions that were implemented are explained with case studies in this article. Results: The studies reveal that new practices including energy-sensitive algorithms, state-of-art cooling solutions and integration of renewable power into Telecommunications networks have improved the energy efficiency standards. In addition, SDN and NFV also improve resource allocation of data centers, which also boosts energy efficiency. Conclusion: Green telecoms offer available strategies for cutting back energy use in telecoms sector. Mitigation of the environmental impacts can therefore be achieved through incorporation of Energy Efficiency measures and Renewable Energy Source technology to utility services without necessarily compromising quality of service delivery hence catalyzing the Advancement of the progress of sustainability.