Rapid digitalization of essential national infrastructure has created new vulnerabilities to cyber threats, leading to major security threats against the nation. The current security measures prove inadequate for keeping pace with developing cyberattacks, so artificial intelligence needs integration for threat detection enhancements and response improvements. The combination of AI-enabled cybersecurity systems gives them the ability to examine huge data collections instantly, monitor irregularities and perform automatic threat response functions to improve national security. Research investigates the system of artificial intelligence to enhance cyber threat response capabilities alongside its specific use for defending crucial infrastructure elements like energy networks as well as financial organizations and government IT infrastructure. Methodology The study combines qualitative approaches with quantitative methods as its research methodology. The analysis includes a structured review of existing frameworks that use AI for cybersecurity purposes with their performance evaluation. The paper evaluates real-world AI deployments across critical infrastructure systems through case studies to reveal successful strategies with encountered problems. The empirical proof of machine learning-based intrusion detection systems is carried out by testing IDS along with real-world dataset assessment to verify AI's threat mitigation effectiveness through the accuracy and precision & recall method. Security experts who perform interviews deliver valuable information about the current use of AI technology in national security applications. The national cybersecurity capabilities gain strength from AI-driven systems because these systems accomplish improved threat detection and swift responses without requiring human involvement. AI deliver its maximum effectiveness only when data privacy issues with adversarial AI attacks and regulatory hurdles, receive proper solutions,

With increasingly sophisticated and common cyberattacks these days, conventional cybersecurity solutions in IT project management tend to fall short. As the sophistication of cyberattacks increases, the demand for fresh ideas that can best protect IT systems and information continues to rise. This research examines the adoption of Artificial Intelligence (AI) in cybersecurity solutions in IT project management. In particular, it examines how AI technologies, such as machine learning, deep learning, and anomaly detection, can be utilized to augment threat detection, respond automatically, and enhance risk management in IT initiatives. The study follows a mixed-methods framework with an amalgamation of systematic literature review along with case studies. Statistical instruments like SPSS v25 were used to test data from 40 IT initiatives that adopted AI-based cybersecurity systems. Key performance measures like time to detect threats, effectiveness in mitigating risk, and security results were measured before and after integrating AI solutions. Findings reveal that there was a 35% decrease in time to detect threats, a 25% increase in the effectiveness of mitigating risk, and a 45% increase in the accuracy in detecting threats, which overall contributed to a tremendous reduction in cybersecurity breaches. These results emphasize the revolutionary influence of AI in cybersecurity practices of IT project management. The research concludes that AI-based cybersecurity models provide a viable avenue to address risks proactively and improve the security stance of IT projects. It is highly recommended that IT project managers embrace AI-driven solutions to enhance their cyber defenses against current and impending threats and guarantee the successful and secure delivery of projects in today’s connected world.

This research demonstrates how MIS transforms energy independence through combined database integration of national energy boards with environmental regulatory agencies and smart grid technology systems. Numerous countries are now adopting Energy Dominance Strategies to boost home energy production decreasing import dependency preserving environmental sustainability during rising worldwide energy requirements. Management Information Systems function as a crucial intervention to link energy policies improving resource management and real-time decision processes. The study employs a mixed-method approach. Reports and dashboards provided by the U.S. Department of Energy International Energy Agency and Energy Information Administration between 2010 and 2024 supplied the quantitative data. The implementation of MIS resulted in a detailed assessment of key performance indicators which included energy output and operational costs combined with system downtime along with carbon emissions during before and after periods. Thirty executives along with IT professionals working in national oil and gas sectors and renewable energy industries and government departments provided qualitative insights through semi-structured interviews. The analysis demonstrates that MIS serves as more than a support tool by enabling the strategic needs of energy dominance through digital systems for sustainable resilient energy networks.

This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management.

A global pandemic of COVID-19 is underway. The world now thinks coronaviruses cause all illnesses, including this pandemic. The world is also seeing fast viral spread. COVID-19 spreads swiftly by respiratory droplets and contact, according to the WHO. Big data analytics technologies are essential for developing the information needed to make choices and take preventative action. The large volume of COVID-19 data available by multiple sources necessitates, however, a review of big data analysis's roles in containing the virus, highlighting the primary obstacles and future directions in COVID-19 data analysis, and offering a framework for relevant current applications and studies to aid in COVID-19 analysis research in the future. The aim of ML, DL methods in COVID-19 research and applications is the focus of this study. This endeavor use machine learning to evaluate and predict COVID-19 pandemic behavior to help control a pandemic. A study uses the Israeli Department of Health dataset to predict COVID-19 results using ML and big data analytics. The test results were analyzed using CNN-LSTM and Decision Tree classification algorithms. Model efficacy was measured by F1-score, recall, accuracy, and precision. CNN-LSTM was the most accurate, with 96.34% accuracy and strong predictive ability. Also, for the model explainability used LIME model. The findings may be used by various government agencies to implement remedial actions. It may be simpler to fight COVID-19 if methods for infectious illness forecasting were readily available.

This study aims at exploring the use of Business Intelligence (BI) tools in supporting decision making processes at strategic, tactical and operational levels. In this study, the research data is collected from secondary sources including the academic developments, business reports, and case analysis to investigate the role of BI in enhancing the decision making, organizational ambidexterity, and strategic alignment in different business sectors. The research also shows that the use of BI tools enhances decision making accuracy and quickness, giving organizations valuable information for informed decision making. The present work also discusses how BI can help organizations to attain organizational ambidexterity, i.e., the ability to explore new markets while, at the same time, exploiting the existing ones. However, the analysis also finds that there are critical problems, including data integration, limited resources, and the search for an optimal balance between analytics and intuition. A comparison with other industries shows that while BI tools have a rather high potential, their efficiency strongly depends on the context and may require a unique approach to address the needs of a particular industry. This research work will also be useful to managers and other stakeholders in organizations in order to enhance their understanding on how to effectively integrate BI to gain competitive advantage in today’s business environment.