Traditional IT Research Articles

Literature Review on Cloud Computing: A Paradigm Combining Service-Oriented Architecture with Internet-Based Solutions

Cloud computing, a paradigm combining service-oriented architecture with Internet-based network solutions, has rapidly transformed the IT industry by offering on-demand access to shared resources. Key models of cloud services include Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). These models provide cost-effective, scalable, and flexible alternatives to traditional IT infrastructure, enabling enterprises to outsource hardware, software, and network services. Cloud systems such as Amazon EC2, Google App Engine, and IBM’s Blue Cloud illustrate these benefits, significantly lowering costs through pay-as-you-go pricing structures. However, data security and privacy have become critical concerns, as cloud users must entrust sensitive information to third-party providers. Ensuring data confidentiality and implementing fine-grained access control are essential to maintaining user trust and enabling compliance with legal and organizational regulations. The success of cloud computing in the future depends on overcoming these security challenges, allowing enterprises to fully embrace its potential

Continuous Service Improvement in IT Operations through Predictive Analytics

In the evolving landscape of IT operations, continuous service improvement is essential for maintaining high performance, reliability, and customer satisfaction. Predictive analytics, leveraging advanced data analysis techniques and machine learning algorithms, offers a transformative approach to enhancing IT service management. This research paper explores the integration of predictive analytics into IT operations to drive continuous service improvement. It investigates how predictive models can forecast potential issues, optimize resource allocation, and enhance decision-making processes, ultimately leading to improved operational efficiency and service quality. The study begins with an overview of traditional IT service management practices and the limitations they face in adapting to dynamic and complex IT environments. Conventional approaches often rely on reactive problem-solving and periodic reviews, which can lead to inefficiencies and missed opportunities for proactive intervention. Predictive analytics offers a paradigm shift by utilizing historical data and real-time information to predict future outcomes, enabling organizations to address potential problems before they impact operations.

Continuous Service Improvement in IT Operations through Predictive Analytics

In the evolving landscape of IT operations, continuous service improvement is essential for maintaining high performance, reliability, and customer satisfaction. Predictive analytics, leveraging advanced data analysis techniques and machine learning algorithms, offers a transformative approach to enhancing IT service management. This research paper explores the integration of predictive analytics into IT operations to drive continuous service improvement. It investigates how predictive models can forecast potential issues, optimize resource allocation, and enhance decision-making processes, ultimately leading to improved operational efficiency and service quality. The study begins with an overview of traditional IT service management practices and the limitations they face in adapting to dynamic and complex IT environments. Conventional approaches often rely on reactive problem-solving and periodic reviews, which can lead to inefficiencies and missed opportunities for proactive intervention. Predictive analytics offers a paradigm shift by utilizing historical data and real-time information to predict future outcomes, enabling organizations to address potential problems before they impact operations.

Vulnerability-oriented risk identification framework for IoT risk assessment

The proliferation of Internet of Things (IoT) systems across diverse applications has led to a notable increase in connected smart devices. Nevertheless, this surge in connectivity has induced a broad spectrum of vulnerabilities and threats, jeopardizing the security and safety of IoT applications. Security risk assessment methods are commonly employed to analyze risks. However, traditional IT and existing IoT-tailored security assessment methods often fail to fully address key IoT aspects: complex assets intercommunication, dynamic system changes, assets’ potential as attack platforms, safety impacts of security breaches, and assets resource constraints. Such oversights lead to significant risks being overlooked in the IoT ecosystem. In this paper, we propose a novel vulnerability-oriented risk identification framework comprising a four-step process as a core element of IoT security risk assessment, applicable to any IoT system. Our process enhances both traditional and IoT-specific security risk assessment methods by providing tailored approaches that address their crucial oversights for comprehensive IoT risk assessment. We validate our process with a case study of an IoT smart healthcare system using a proposed expert-driven approach. The results confirm that our process effectively identifies critical attack scenarios originating from the lack of proper security measures, mobility, and intercommunication processes of IoT devices in the healthcare system. Furthermore, our analysis reveals potential attacks that exploit the IoT devices as platforms to target the backend and user domains. We demonstrate the feasibility of our process for identifying realistic risks by conducting simulations of two derived attack scenarios using the Contiki Cooja network simulator.

Exploitation Process for Machine Learning Systems

In recent years, many companies have been involved in the development of machine learning systems. However, developing machine learning systems presents unique challenges compared with the traditional IT system development. In software engineering, research has been conducted on software development methodologies for machine learning systems, but most of these studies focused on the development process of individual machine learning systems, and limited research has investigated the exploitation process of continuously enhancing machine learning systems after their development and deployment. Thus, this paper focuses on the exploitation process, in which the collaboration between the machine learning systems and humans evolves gradually. Specifically, four stages of the exploitation process are proposed: visualization for machine learning, human-centered machine learning assistance, machine learning-centered human assistance, and autonomy without human involvement. Then, the characteristics and challenges of each stage are clarified. In addition, this paper examines the important aspect of trust in this context. The findings of this study are expected to provide guidelines for advancing machine learning systems incrementally in collaboration with humans from the perspective of the exploitation process rather than focusing on the development process.

Integrated predictive analytics in IT audit planning

This review explores the integration of predictive analytics in IT audit planning, emphasizing its transformative potential in enhancing the efficiency and effectiveness of audit processes. Predictive analytics, which involves the use of machine learning, data mining, and statistical modeling to forecast future events based on historical data, has seen widespread adoption across various industries. In the context of IT audit planning, its application can significantly improve risk assessment, resource allocation, and overall audit execution. The study begins by defining predictive analytics and outlining its key techniques. It then provides an overview of the traditional IT audit planning process, highlighting its critical steps and inherent challenges, such as limited risk visibility and inefficient resource use. By integrating predictive analytics, organizations can address these challenges by leveraging data-driven insights to identify emerging risks, prioritize audit areas, and optimize audit schedules. The integration process involves several stages, including data collection and preparation, model development, and implementation. The review discusses the importance of selecting appropriate data sources, cleaning and preprocessing data, and choosing the right predictive models. It also covers the deployment of these models within existing IT audit frameworks, emphasizing the role of advanced tools and technologies. The benefits of integrated predictive analytics are manifold. Enhanced risk assessment allows auditors to proactively identify and mitigate potential issues, while improved resource allocation ensures that audit efforts are focused on the most critical areas. Additionally, predictive analytics can detect anomalies and patterns that might go unnoticed in traditional audits, thereby increasing audit effectiveness. Case studies of successful implementations in various organizations are presented to illustrate the practical benefits and outcomes of integrating predictive analytics into IT audit planning. The review also addresses potential challenges, such as data quality issues, model accuracy, and organizational resistance, offering strategies to overcome these hurdles. The integration of predictive analytics in IT audit planning represents a significant advancement in audit practices. By adopting these techniques, organizations can enhance their audit capabilities, leading to more proactive and effective risk management. The review provides recommendations for implementation and highlights future trends in predictive analytics and IT auditing. Keywords: Integrated, Predictive Analytics, IT, Aduit Planning.

Validation of Sensor Data Integrity in OT Environments Through Multisource Data Sensors

This research paper focuses on detecting cyber threats from the OT environment by combining data from multiple sources. Monitoring cyber security or hybrid threats in an industrial OT environment is difficult due to different equipment, protocols, environments, personnel management and training, etc. However, the OT environment can also be observed with a multisource sensor system, which can be used to collect data. By combining IT and OT data, additional cyber threats can be found. Especially concerning the integrity of OT command-and-control data. We deal with the key concepts and differences of the industrial operating environment, which create challenges compared to the traditional IT environment. This is important because the policies defined at the European level for the NIS2 regulation are coming to touch all member countries, regardless of what the national implementation schedule is. The increased standards for OT environment cyber security implementation and development will also have an impact on the personnel management and training to support the onboarding of the standards in practice. Critical infrastructure protection is important because, without the protection of critical infrastructure, vital functions cease to function. Hostile actors cause security challenges among Western actors. In this study, we delve into whether it is possible to find threats concerning OT command-and-control process. The increased data surface collected from the IT/OT environment improves the capabilities for the system to detect malicious attacks towards the OT system. With the help of test equipment, the goal is to demonstrate that it is possible to find threats by combining data from multiple sources. With the help of test equipment, we find out IT and OT capabilities, which we load with various attacks and anomalies. We produce added value compared to traditional monitoring method test cases by comparing data obtained from different sources. The research paper shows the importance of detecting OT threats. By monitoring IT and OT environments and combining their data, we can find hidden threats. Only one test equipment configuration has been used in the study, but the results can be generalized and classified. The study also provides guidelines for how the detection of cyber threat capabilities should be developed.

Improving Detection Capabilities in OT Environments Through Multisource Data Sensors

This research focuses on implementing cyber threat detection in OT environments by combining data from IT and OT sensors and logs to enhance SOC's situational awareness. OT environment is challenging to monitor and includes various sensors. We deal with the key concepts and differences of the industrial operating environment, which create challenges compared to the traditional IT environment. This is important because the policies defined at the European level for the NIS2 regulation will affect all member countries. Hostile actors cause security challenges highlighting the importance of critical infrastructure protection. Cyber security solutions have often solely focused on IT threats, but similar investments have yet to be made in response to the challenges of the OT environment. The security solutions of OT operators rely heavily on solutions from the IT side. Here, we delve into whether it is possible to find threats in the IT/OT ecosystem by combining data from the IT and OT sides. All threats are not found by monitoring data separately from IT or OT sources but we identified hidden threats by monitoring and comparing IT and OT data. This paper shows the importance of detecting OT threats. The study proposes how the detection of cyber threat capabilities should be developed.

MCAD: A Machine Learning Based Cyber Attack Detector using SDN for Healthcare Systems

The healthcare industry, increasingly reliant on digital technology, has become a prime target for cyberattacks. While traditional security measures address external threats, they often fail to effectively counter a particularly dangerous foe: insider threats. This project proposes MCAD (Machine Learning-based CyberAttack Detector), a groundbreaking approach that leverages the power of machine learning within Software-Defined Networks (SDNs) to bolster healthcare network security. Healthcare networks are inherently complex ecosystems, housing a diverse range of medical devices alongside traditional IT infrastructure. This intricate web creates a larger attack surface for malicious insiders with access to critical systems. These insiders can be disgruntled employees or attackers exploiting vulnerabilities in poorly designed systems. The COVID-19 pandemic further exacerbated this vulnerability as the surge in telehealth services and remote access points opened new avenues for exploitation. The statistics are alarming, with a staggering 92% of healthcare organizations reporting insider-caused security breaches. These breaches not only compromise sensitive patient data but can also disrupt critical healthcare services, potentially jeopardizing patient safety. MCAD, a Machine Learning-based Cyber Attack Detector, tackles the growing threat of insider attacks in healthcare networks. It employs a multi-pronged approach: collecting both normal and abnormal network traffic to train a real-time machine learning model. This model continuously analyzes network activity, identifying suspicious behavior indicative of insider threats. MCAD seamlessly integrates with SDN controllers for efficient deployment within existing infrastructure, and undergoes rigorous testing with various machine learning algorithms and simulated attacks to ensure optimal protection against evolving cyber threats.

Predictive Maintenance of Server using Machine Learning and Deep Learning

Traditional IT maintenance often leads to wasted resources and downtime. This paper explores how predictive maintenance (PdM) with machine learning can revolutionize server management in Industry 4.0. By comparing various machine learning models for PdM, we analyze their effectiveness in predicting server failures. The paper emphasizes the critical role PdM plays in boosting server reliability and driving industry transformation, supported by statistics highlighting its growing importance. Furthermore, we bridge the theory-practice gap by proposing a web application specifically designed for server PdM, allowing for proactive maintenance and reduced downtime. Finally, we explore future research directions and emerging trends in server PdM, providing valuable insights for organizations seeking to optimize maintenance practices and achieve operational excellence in the digital age.

Exploring Cloud Computing

Cloud computing has reached maturity since Amazon introduced the pioneering cloud services in 2006. Hong Kong, with its vast daily data processing needs across various sectors, is poised to embrace cloud services extensively, despite a gradual uptake initially. Cloud computing now dominates research agendas in computer science due to its profound implications across multiple computing sectors, particularly in managing big data, where it's indispensable. The establishment of a major cloud R&D centre in Hong Kong by Lenovo in January 2015 underscores this trend. Cloud computing, fulfilling the long-standing vision of computing as a utility, possesses the capability to revolutionize a significant portion of the IT industry. It enhances the attractiveness of software as a service and influences the design and procurement of IT hardware. For developers with innovative ideas for new Internet services, the need for substantial capital investment in hardware deployment or ongoing operational expenses is eliminated. Concerns about overprovisioning, which wastes resources, or under provisioning, which can lead to missed opportunities, are alleviated. Additionally, companies with extensive batch-oriented tasks can achieve results at unprecedented speeds, as the scalability of programs allows for efficient utilization of resources without incurring additional costs. This elasticity of resources, where utilizing 1,000 servers for one hour costs the same as using one server for 1,000 hours, represents a significant departure from traditional IT models.

COMPARATIVE ANALYSIS OF EMERGING AND TRADITIONAL IT BUSINESS MODELS IN A GLOBAL LANDSCAPE

This research aims to provide a comprehensive comparative analysis of emerging and traditional IT business models within the global landscape, addressing their scalability, sustainability, and the distinct challenges and opportunities each presents. Emerging IT business models, characterized by innovative technologies and disruptive market strategies, contrast significantly with traditional models, which rely on established technologies and stable market approaches. This study explores how these diverse business models adapt to rapid technological advancements and fluctuating market demands, thereby impacting global economic dynamics. By examining various factors such as activity type, organizational structure, ownership, market scope, technological focus, revenue models, and customer base, this paper categorizes IT companies into emerging and traditional sectors. Emerging sectors, driven by digital transformation and new market creation, offer high growth potential but face challenges in market acceptance and regulatory adaptation. In contrast, traditional IT sectors provide stability and incremental innovation but must continually adapt to prevent obsolescence and maintain competitiveness. The paper utilizes a mix of qualitative research methods to analyze the operational, financial, and strategic dimensions of IT companies. Case studies of leading firms in both sectors highlight the practical implications of different business models and their effectiveness in leveraging technological and market opportunities. This research contributes to the understanding of IT business dynamics, offering insights that could influence policy formulation, investment strategies, and corporate decision-making globally. It underscores the importance of strategic adaptability and proactive innovation management in sustaining business growth and competitiveness in the rapidly evolving IT industry.

Strengthening Aviation Cybersecurity with Security Operations Centres

Even though cybersecurity is a top priority for the aviation industry, research indicates that there are still many challenges to address. Modern aviation systems encompass cloud computing, OT, IoT, mobile devices, and traditional IT infrastructure. The network complexity has expanded the attack surface, leading to an increase in security incidents. Due to this complexity, detecting security incidents on time is challenging. Research indicates that it may take up to 196 days to detect an incident and another 56 days to address it, highlighting the urgency of improving security response. In this regard, establishing Security Operations Centres (SOCs) in the aviation sector must be addressed. SOCs can be instrumental in reducing the time it takes to detect and respond to security incidents. They provide visibility into threats, aid investigations, and enhance forensic efforts, enabling proactive threat mitigation. Research has been carried out on SOC implementations for specific domains like IoT, mobile devices, and higher education, neglecting aviation systems. Aviation systems such as Air Traffic Management (ATM) face unique security vulnerabilities, including signal modification, jamming, flooding, data and command injection, GPS spoofing, and blocking attacks, primarily due to their reliance on wireless technology. Most of these wireless technologies do not use encryption or authentication because they were designed to maximise performance. Insufficient funding also negatively affects ATM systems, resulting in the wide use of legacy ATM systems and a shortage of skilled personnel. ATM systems are considered critical infrastructure frequently targeted by well-resourced threat actors, including terrorists and nation-state actors, necessitating higher protection levels. This paper motivates the development of a customised SOC implementation framework for ATM systems to enhance aviation security by increasing visibility into threats and facilitating timely remediation.

Unveiling Outperformance: A Portfolio Analysis of Top AI-Related Stocks against IT Indices and Robotics ETFs

In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.

Security and Safety in Cyber-Physical System (CPS): An Inclusive Threat Model

A Cyber-Physical System (CPS) is a combination of computational algorithms and physical processes that are integrated together. Cyber-Physical Systems (CPS) integrate computer and communication capabilities with the monitoring and management of physical parts, establishing a mutually beneficial interaction between the cyber and physical components. Industrial Control System (ICS) is one example of CPS which integrates the physical (OT) and cyber domains (IT), which makes them more vulnerable to attacks. Two essential characteristics of Cyber-Physical Systems (CPS) are safety and security. Threat models are methods for identifying, analysing, and proposing security control countermeasures for threats and their capabilities. However, the threat model methods which are used for the traditional IT systems are not sufficient as they do not include the physical interactions, consequences and impacts to the safety aspects in the Operational Technology (OT). On the other hand, a risk assessment analyses attack scenario, examines cybersecurity from the attacker's point of view, and gives cost-benefit data to support the expenditure on security measures. This study proposes an inclusive attacker’s centric threat model and pro-active risk assessment model for CPS using Mamdani Fuzzy Inference System (FIS). The outcomes of the threat model prove that the lateral propagation of the threat is possible and threat may also propagate from the CPS assets to the IT segment. The risk assessment by using FIS shown that the safety and security risk for the CPS is significant and calculated as medium level. Hence, the risk factors that are considered in calculating the overall risk for a CPS need to be immediately addressed and mitigated.

Ensuring cybersecurity for industrial networks: A solution for ARP-based MITM attacks

The increased adoption of the Internet Protocol (IP) in ICSs has made these systems vulnerable to the same security risks that are present in traditional IT environments. The legacy nature of ICSs and their unique operational requirements make them vulnerable to security threats that are different from those in IT environments. In this paper, we describe a protocol, named ArpON, which is able to wipe out in quasi real time any ARP cache poisoning attempt, thus making it ineffective. Contrarily to solutions presented in the literature for contrasting ARP cache poisoning, ArpON incurs in low operational costs, is backward compatible, transparent to the ARP protocol and does not use any HW feature nor cryptography functionality. We also model and validate ArpON in the OMNET + + network simulator. The simulation results show that ArpON is effective in avoiding ARP poisoning, and its communication overhead is negligible with respect to classical ARP protocol.

From on-premise to cloud: Evolving IT infrastructure for the AI age

The transition from traditional on-premise IT infrastructure to cloud-based systems represents a fundamental shift necessary for the advancement and implementation of artificial intelligence (AI) technologies. This paper explores the critical role of cloud computing in supporting AI, emphasizing its benefits in terms of scalability, flexibility, and cost-efficiency. It examines the limitations of on-premise systems, the advantages of various cloud models (IaaS, PaaS, SaaS), and key considerations for successful cloud migration, such as data security, compliance, and cost analysis. Through a detailed case study of a financial institution's cloud migration for AI implementation, this study highlights the challenges, solutions, and lessons learned in the process. The case study provides practical insights into the phased approach to cloud migration, the use of hybrid cloud solutions as transitional strategies, and the importance of training and skill development for IT teams to ensure business continuity during the transition. Furthermore, the paper discusses future trends in cloud and AI integration, including the impact of emerging cloud technologies like server less and edge computing, and the pivotal role of 5G in enhancing cloud-based AI applications. It predicts the future landscape of cloud and AI integration, emphasizing the increasing adoption of hybrid and multi-cloud strategies, investment in data privacy and security, and the integration of AI with other emerging technologies such as IoT and block-chain. The paper concludes by underscoring the importance of cloud infrastructure in the AI age and calls for businesses to evaluate their cloud readiness to stay competitive in a rapidly evolving digital landscape. By embracing cloud-based solutions, businesses can unlock new opportunities for innovation, efficiency, and growth, positioning themselves at the forefront of the AI revolution.

The evolution of the it profession navigating by exponential growth in the era of ai and digitalization

The article is dedicated to exploring the strategic methods for the survival of IT professionals amidst the exponential proliferation of artificial intelligence and digitalization. The purpose of this article is to analyze the evolving strategies that IT experts can adopt to navigate and thrive in an environment characterized by the rapid advancement of AI and digitalization. The object of this study is the strategic approaches employed by IT professionals to adapt to the changing landscape of technological evolution. The subject is the formulation of effective survival strategies tailored to the challenges posed by the expanding domain of artificial intelligence and digital transformation. Tasks to be solved include evaluating the impact of exponential growth in AI and digitalization on traditional IT roles, formulating adaptive strategies for IT professionals, and proposing methods for skill enhancement and career sustainability in this dynamic environment. Methods employed encompass a comprehensive analysis of the impact of AI and digitalization on IT roles, strategic planning, empirical studies on the evolving skill sets demanded by the industry, and the examination of case studies illustrating successful adaptation strategies. The following results were obtained: identification of the transforming landscape for IT professionals due to the rise of AI and digitalization, formulation of adaptive strategies catering to the changing demands, insights into skill development, and continuous learning necessary for survival in the rapidly changing IT landscape. Conclusions drawn from this research emphasize the crucial need for IT professionals to adopt a proactive stance towards skill enhancement, continuous learning, development of soft skills, and adaptation to the evolving technological paradigms to ensure career sustainability.

Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study.

Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells' criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform's flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells' criteria tool will be reported in a future publication. The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics "nudges" on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint.

Project Management Evolution: From Traditional IT Implementations to AI-Driven Projects

The rapid advancement of artificial intelligence (AI) has brought transformative changes to project management, necessitating a departure from traditional methodologies previously employed in IT project implementations. This paper explores the evolution of project management from conventional IT approaches to the dynamic demands of AI-driven projects. While foundational principles of project management—such as planning, risk management, and stakeholder communication—remain relevant, AI projects introduce unique challenges and require significant adaptations to existing frameworks. The study begins by delineating the characteristics and core principles of traditional IT project management. Traditional methods are characterized by their structured phases, fixed requirements, and a focus on sequential task execution. These principles have been foundational in achieving success in conventional IT projects through detailed planning, rigorous documentation, and predefined quality assurance measures. In contrast, AI projects are distinguished by their reliance on data, iterative development, and high levels of uncertainty. Unique characteristics of AI projects include the need for continuous experimentation, data-driven decision-making, and adaptability to evolving project requirements. The paper identifies key challenges in managing AI projects, such as dealing with data quality issues, ensuring model interpretability, and addressing ethical considerations. To effectively manage AI projects, project managers must adopt new strategies, including Agile and iterative methodologies that support flexibility and continuous feedback. The study emphasizes the importance of cross-functional teams, as AI projects require diverse expertise from data scientists, engineers, and domain specialists. Additionally, handling the inherent uncertainty in AI projects involves fostering a culture of innovation and adaptability. Key differences between traditional IT and AI project management are analyzed, highlighting variations in planning and scoping, risk management, stakeholder communication, and quality assurance. Traditional IT management relies on detailed upfront planning and predictable risk management, whereas AI projects necessitate adaptive planning, dynamic risk assessment, and ongoing model validation. The paper also addresses the transition to AI project management, discussing necessary skill adaptations for project managers, organizational changes to support AI initiatives, and the role of specialized tools and technologies. A hypothetical case study illustrates how traditional IT project management experience can be applied to an AI project, providing insights into practical adaptations and lessons learned. LllLooking forward, the paper explores emerging trends in project management influenced by AI advancements and emphasizes the need for continuous learning and adaptation. The evolving role of project managers in the AI era is examined, underscoring the importance of embracing new methodologies and technologies to stay relevant. While core project management principles remain integral, the shift to AI-driven projects requires substantial modifications to traditional practices. Project managers must evolve their approaches to navigate the complexities of AI projects effectively, ensuring continued success in an increasingly technology-driven landscape.