Shamnad Mohamed Shaffi

This paper explores the implications of using Artificial Intelligence (AI) in organizations, particularly Decision Support Systems (DSS). These systems help to increase the effectiveness and productivity accompanied by better use of data that drives the decision-making process but simultaneously results in new problems such as biases, responsibility, openness, and even invasion of personal privacy. Some ethical risk areas identified include: The current problem with AI in organizations is that they lack fairness and equality in coming up with decisions since algorithms can be biased; There is no supervision of the decisions made by AI by human beings; and finally, they misuse sensitive data. Thus, the transparency of AI decision-making and people’s responsibility for processes are the key priorities to achieve the ethical usage of beneficial AI. However, it is also imperative for organizations to manage the regulatory aspects and ethical concerns for using artificial intelligence in support of fairness in the decision-making processes. This paper will discuss the relationship between ethics and AI and related risks and will focus on essential principles such as responsible AI governance, ethical design, and sustained monitoring. By ensuring fairness, explainability, and accountability, it will be possible to achieve a good balance where organizations include decision support from AI while upholding ethical standards.

This paper presents a comprehensive analysis of Data Lakehouse Architecture implementation and applications in healthcare. It addresses the challenges of managing exponential health care data growth while ensuring regulatory compliance, data security, and operational efficiency. The study demonstrates significant improvements, including 42% reduction in data retrieval time, 35% improvement in patient outcomes, and 28% reduction in diagnosis times [34]. The paper outlines an implementation framework with advanced security measures to address cybersecurity threats [2], and explores the integration of AI, machine learning, and real-time processing within the data lakehouse [3], [4]. Examining the impact on key stakeholders, this research provides health care organizations with actionable insights for modernizing their data infrastructure while ensuring HIP AA compliance through automated PHI protection and privacy-preserving techniques [5]. As the healthcare predictive analytics market is projected to reach USD 184.58 billion by 2032 [7], this work positions the data lakehouse as a transformative solution for the future of data-driven healthcare.

This paper presents a comprehensive analysis of Data Lakehouse Architecture implementation and applications in healthcare. It addresses the challenges of managing exponential health care data growth while ensuring regulatory compliance, data security, and operational efficiency. The study demonstrates significant improvements, including 42% reduction in data retrieval time, 35% improvement in patient outcomes, and 28% reduction in diagnosis times [34]. The paper outlines an implementation framework with advanced security measures to address cybersecurity threats [2], and explores the integration of AI, machine learning, and real-time processing within the data lakehouse [3], [4]. Examining the impact on key stakeholders, this research provides health care organizations with actionable insights for modernizing their data infrastructure while ensuring HIP AA compliance through automated PHI protection and privacy-preserving techniques [5]. As the healthcare predictive analytics market is projected to reach USD 184.58 billion by 2032 [7], this work positions the data lakehouse as a transformative solution for the future of data-driven healthcare.

The growing complexity and frequency of incidents across many fields, particularly cybersecurity, healthcare, critical infrastructure, and emergency response, highlight the pressing need for automated, intelligent, and effective frameworks for incident reporting. Traditional manual methods often face constraints regarding latency, vulnerability to errors, and lack of analytical insights that are vital to supporting timely decision-making. This research explores the conceptual model and implementation of an Automated Incident Reporting and Intelligence Framework that enhances the speed, accuracy, and strategic value of incident management processes. The system proposed in this research leverages cutting-edge technologies like machine learning, natural language processing, decision support systems, real-time analytics, and Artificial Intelligence to support the detection, classification, and reporting of incidents. It also includes predictive intelligence and contextual analysis to develop actionable insights to aid stakeholders in prioritization of interventions and prevention of future incidents. The system architecture presented in this paper emphasizes scalability, interoperability, and modularity to cater to a diversity of organizational types while ensuring protection, confidentiality, and compliance with local and international regulations and standards. By integrating literature, technological innovations, and empirical case studies, this paper outlines fundamental design principles, deployment strategies, and assessment metrics essential to the effectiveness of an automated incident reporting system.

Cloud security concerns have been greatly realized in recent years due to the increase of complicated threats in the computing world. Many traditional solutions do not work well in real-time to detect or prevent more complex threats. Artificial intelligence is today regarded as a revolution in determining a protection plan for cloud data architecture through machine learning, statistical visualization of computing infrastructure, and detection of security breaches followed by counteraction. These AI-enabled systems make work easier as more network activities are scrutinized, and any anomalous behavior that might be a precursor to a more serious breach is prevented. This paper examines ways AI can enhance cloud security by applying predictive analytics, behavior-based security threat detection, and AI-stirring encryption. It also outlines the problems of the previous security models and how AI overcomes them. For a similar reason, issues like data privacy, biases in the AI model, and regulatory compliance are also covered. So, AI improves the protection of cloud computing contexts; however, more efforts are needed in the subsequent phases to extend the technology's reliability, modularity, and ethical aspects. This means that AI can be blended with other new computing technologies, including blockchain, to improve security frameworks further. The paper discusses the current trends in securing cloud data architecture using AI and presents further research and application directions.

The field of data governance is changing drastically due to the emergence of AI technologies. The way data is being managed, secured, and gained value is being re-imagined as AI has new capabilities and challenges. AI is rewriting data management rules by allowing for real-time monitoring, automatic decision-making, and predictive analytics on a scale and speeds never before possible. This change that we refer to as Data Governance 2.0 is distinguished by automation enhancement, predictive capabilities improvement, and dynamic approaches to data processing and security (These are not just incremental changes but a paradigm change in how organizations handle data governance. Automated data quality management, AI-driven compliance monitoring, and more are the effects of these technologies in every aspect of data management [3]. Organizations must adjust to the new Data Governance 2.0 since it has become crucial for success in the contemporary business world. Firms that integrate AI tools within their existing data governance systems are in a position of superiority in the market. These advantages include enhanced functioning, analytical skills, and risk management methods. Nevertheless, it is difficult for businesses since they experience numerous obstructions in this transition. They have to address ethical issues related to the use of AI, confront some technical issues and even train their employees so that they can learn new skills. The management groups are usually forced to rearrange their company structure to cope with these changes. Studying the current situation in the business landscape, it is evident that the necessity to understand and align with Data Governance 2.0 is important for firms keen to remain competitive in the current data-oriented economy

In today’s data-driven economy, organizations increasingly seek efficient, scalable, and secure ways to share and monetize their data assets. Data marketplaces have emerged as transformative platforms that connect data providers with consumers in a centralized, governed environment [1]. These marketplaces streamline data discovery, enhance data quality assurance, and support monetization through flexible pricing models like subscriptions and pay-per-use. By implementing robust governance, metadata management, and compliance with regulations such as GDPR and CCPA, data marketplaces ensure privacy and trust. The integration of Data-as-a-Service (DaaS), artificial intelligence, and blockchain further enhances capabilities by enabling real-time insights, automation, and secure transactions. Industry use cases across healthcare, finance, retail, and manufacturing demonstrate the marketplace's potential to democratize data access and foster innovation. This paper explores the key components, challenges, and future trends of data marketplaces, positioning them as vital infrastructure for the modern digital economy.

This article explores the emerging concept of Zero-ETL, a modern approach to data integration that seeks to address the limitations of traditional Extract, Transform, Load (ETL) processes. As organizations demand faster insights and real-time data access, the complexities and inefficiencies of traditional ETL become increasingly apparent. Zero-ETL minimizes data movement, integrates data at query time, and leverages technologies such as real-time streaming and data virtualization. The article compares Zero ETL to traditional ETL, highlighting differences in process, data latency, complexity, flexibility, and infrastructure costs. It discusses the benefits of Zero-ETL, including real-time data availability, simplified operations, cost savings, improved data governance, and scalability. The article also addresses the trade offs and challenges associated with Zero-ETL, such as infrastructure demands, legacy system integration, and security risks. Best practices for optimal data performance and real-world applications of Zero-ETL in machine learning, customer experience analytics, fraud detection, and supply chain optimization are presented. Finally, the article outlines key considerations for building a Zero-ETL architecture and reviews the technology landscape, including AWS, Snowflake, and Databricks. This comprehensive overview aims to provide organizations with the insights needed to leverage Zero-ETL in their data integration strategies. This document is a template to provide guidance about formatting the research papers which are going to be submitted to the journal IJFMR. Authors can get a general idea of formatting and various possible sections in the research paper.

The marketing analytics landscape is being transformed by the convergence of Generative AI and Advanced Attribution Models. Generative AI enables the creation of unique, personalized content, revolutionizing customer engagement and campaign optimization [3 - 6]. Advanced Attribution Models provide unprecedented insights into the complex customer journey, tracking the impact of touch points and channels on conversion rates [7 - 10]. This article explores the integration of these cutting - edge technologies, demonstrating how organizations can harness their synergies to drive measurable improvements in marketing performance [1 - 2]. Through case studies and analysis, the study examines the practical applications, challenges, and strategic implications of this transformative approach [11 - 12]. The findings offer valuable insights for practitioners, data scientists, and leaders, providing a roadmap to leverage Generative AI and Advanced Attribution Models to revolutionize marketing strategies, enhance customer experiences, and achieve sustainable growth in the digital age [1 - 2]

Real-time big data analysis is already disrupting healthcare by changing the way decisions are made, treatment given, and organizations and processes managed. Using technologies like AI, IoT, and cloud computing, healthcare organizations can analyze large data sets alerting patients to diseases, picking appropriate treatments, and determining where resources will be essential. In this article, the author explores the progress, opportunities, and issues of real-time analytics in the healthcare industry and the sort such as predictive analytics, patient supervision, and telemedicine implementation. Despite the ethical issues, data privacy and system integration issues remain potent barriers, trends such as precision medicine and digital health mapping of the world present the implementation of a brave new world in the delivery of healthcare. For this, there is a need for innovation through embracing technology, sector collaboration and, most importantly, proper regulation in order to foster and promote secure usage for people

The growing dependence of data for decision-making has made BI a necessity for companies. However, BI systems mostly like the traditional model where they cannot process or analyze the volumes of data generated every day. The revolutionary AI analytics has spun the approach, leveraging AI such as machine learning and natural language processing to offer real-time insights, predictive forecasts, and action applications. Through the discussion, this research explores BI data milieu changes in AI analytics, distinctions, and its various implementations throughout industries. This research also provides a light to the challenges revolving around AI driven BI setups and suggestion on different ways to assuage them. By swelling the AI analytics, which is desirable because of competition and leads to a significant improvement in efficiencies and decision-making progress for the organization

Enterprise Content Management (ECM) and Data Governance are essential for organizations to manage, secure, and optimize data assets efficiently. This paper presents a structured framework for implementing ECM and data governance policies using an imaginary company, Teleware, as a case study. The study explores common challenges faced by enterprises, including data integration, quality, accessibility, and regulatory compliance. The proposed framework includes an assessment of the existing information infrastructure, identification of regulatory requirements, and enhancement of content management processes. A phased implementation approach is introduced, incorporating best practices in metadata management, data security, and risk mitigation. The adoption of a next-generation data governance platform ensures improved data quality, workflow efficiency, and enterprise-wide accessibility. Agile methodologies are leveraged to streamline policy execution, ensuring adaptability to evolving business needs. This framework serves as a strategic guide for organizations aiming to establish a data-driven culture, enhance compliance, and optimize decision-making through effective content and data governance

This paper presents a comprehensive strategy for ensuring compliance with the California Consumer Privacy Act (CCPA) and securing sensitive customer data within the organization. It focuses on evaluating and strengthening the current information security infrastructure, addressing potential risks, and enhancing privacy policies and training programs. The project aims to meet current and future state-level privacy compliance requirements while safeguarding Personal Identifiable Information (PII), Customer Proprietary Network Information (CPNI), and Payment Card Information (PCI). Key recommendations include risk assessments, the implementation of secure access controls, centralized data management, and network security measures. By adhering to established standards like NIST 800-30, the organization aims to mitigate risks, ensure regulatory compliance, and create a resilient data privacy framework that supports business growth and customer trust.

Enterprises today face increasing cybersecurity threats that necessitate robust digital forensics and risk mitigation strategies. This paper explores these challenges through an imaginary case study of an organization, a global identity management and data analytics company handling vast customer data. Given the critical nature of its data assets, EP has established a dedicated digital forensics team to detect threats, manage vulnerabilities, and respond to security incidents. This study outlines an approach to cybersecurity, including proactive threat anticipation, forensic investigations, and compliance with regulations like GDPR and CCPA. Key threats such as social engineering, insider risks, phishing, and ransomware are examined, along with mitigation strategies leveraging AI and machine learning. By detailing security framework, this paper highlights best practices in digital forensics, incident response, and enterprise risk management. The findings emphasize the importance of continuous monitoring, policy enforcement, and adaptive security measures to protect sensitive data and ensure business continuity in an evolving threat landscape