AI-Driven Customer Data Platforms: Unlocking Personalization While Ensuring Privacy

A Review of AI Assisted Continuous Auditing Systems in Technology Risk and Cybersecurity Oversight explores how artificial intelligence is transforming assurance practices in increasingly digital and distributed enterprises. Traditional audit approaches, typically periodic and retrospective, struggle to keep pace with the speed, scale, and complexity of modern technology environments. Continuous auditing supported by artificial intelligence offers the ability to monitor controls, detect anomalies, and generate assurance insights in near real time. This review synthesizes current research and industry practices on AI enabled continuous auditing systems, focusing on their application in technology risk management and cybersecurity governance. The study examines machine learning, natural language processing, process mining, and anomaly detection techniques used to evaluate access controls, configuration management, incident response, and regulatory compliance. It categorizes existing models into data driven monitoring, predictive risk analytics, and automated assurance reporting approaches. Strengths and limitations are analyzed across scalability, explainability, data quality, and integration with governance risk and compliance platforms. The review highlights the growing convergence between internal audit, information security, and enterprise risk management functions as organizations adopt integrated assurance models. Emphasis is placed on explainable artificial intelligence, model transparency, and ethical considerations to ensure trust and regulatory acceptance. The paper proposes a conceptual lifecycle for AI assisted continuous auditing that includes data ingestion, control mapping, risk scoring, alerting, investigation support, and audit ready reporting. It also examines how dashboards and visualization tools enhance decision making for executives and boards. Key implementation challenges are discussed, including skills gaps, privacy concerns, legacy system integration, and resistance to organizational change. By consolidating fragmented knowledge, the review provides a comprehensive perspective on how AI can strengthen proactive oversight, reduce audit fatigue, and improve organizational resilience. Future research directions include standardized benchmarks, cross industry collaboration, and the integration of privacy preserving analytics. The findings position AI assisted continuous auditing as a strategic capability that enhances transparency, accountability, and cyber resilience in technology driven enterprises worldwide today.

Individuals with multiple chronic conditions (MCCs) and mental health challenges such as depression or anxiety have complex health needs and experience significant challenges with care coordination. Approaches to enhance care for patients with MCCs typically focus on eliciting patients' values to identify and align treatment priorities across patients and providers. However, these efforts are often hindered by both systems- and patient-level barriers, which are exacerbated for patients with co-occurring mental health symptoms. Technology-enabled services (TES) offer a promising avenue to facilitate values elicitation and promote patient-centered care for these patients, though TES have not yet been tailored to their unique needs. This study aimed to identify design and implementation considerations for TES that facilitate values elicitation among patients with MCCs and depression or anxiety. We sought to understand the preferences of both clinicians and patients for TES that could bridge the gap between mental and physical health care. Using human-centered design methods, we conducted 7 co-design workshops with 18 participants, including primary care clinicians, mental health clinicians, and patients with MCCs and depression or anxiety. Participants were introduced to TES prototypes that used various formats (eg, worksheets and artificial intelligence chatbots) to elicit and communicate patients' values. Prototypes were iteratively refined based on participant feedback. Data from these sessions were analyzed using reflexive thematic analysis to uncover themes related to service, technology, and implementation considerations. Three primary themes were identified. (1) Service considerations: TES should help patients translate elicited values into actionable treatment plans and include low-burden, flexible activities to accommodate fluctuations in their mental health symptoms. Both patients and clinicians indicated that TES could be valuable for improving appointment preparation and patient-provider communication through interpersonal skill-building. (2) Technology considerations: Patients expressed openness to TES prototypes that used artificial intelligence, particularly those that provided concise summaries of appointment priorities. Visual aids and simplified language were highlighted as essential features to support accessibility for neurodiverse patients. (3) Implementation considerations: Clinicians and patients favored situating values elicitation in mental health care settings over primary care and preferred self-guided TES that patients could complete independently before appointments. Findings indicate that TES can address the unique needs of patients with MCCs and mental health challenges by facilitating values-based care. Key design considerations include ensuring TES flexibility to account for fluctuating mental health symptoms, facilitating skill-building for effective communication, and creating user-friendly technology interfaces. Future research should explore how TES can be integrated into health care settings to enhance care coordination and support patient-centered treatment planning. By aligning TES design with patient and clinician preferences, there is potential to bridge gaps in care for this complex patient population.

The integration of Artificial Intelligence (AI) in higher education has transformed teaching, learning, and administrative processes, offering unprecedented opportunities for personalized learning, predictive analytics, and operational efficiency. While AI enhances educational effectiveness, it introduces complex ethical, privacy, and security challenges that require systematic attention. Algorithmic bias, lack of transparency, and limitations in accountability pose significant ethical dilemmas, potentially affecting fairness and student autonomy. Privacy concerns emerge from large-scale collection, storage, and analysis of sensitive student data, necessitating robust data protection measures, including anonymization and differential privacy. Security vulnerabilities, encompassing cyberattacks, model manipulation, and insider threats, further amplify the risks associated with AI deployment. This chapter examines these multidimensional challenges, highlighting the importance of ethical AI design, privacy-preserving techniques, and comprehensive risk management strategies. Integration of governance frameworks throughout the AI lifecycle ensures responsible, secure, and equitable adoption across diverse educational contexts. Cross-cultural and global perspectives are explored to address variations in regulatory standards, cultural expectations, and institutional capacities, emphasizing the need for adaptable and inclusive AI governance. The chapter concludes by identifying future research directions that advance trustworthy AI adoption in higher education, fostering transparency, accountability, and resilience while safeguarding student rights.

Agricultural data privacy: Emerging platforms & strategies

Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy. We explore the impact of big data on AI-driven drug discovery, discussing the challenges and opportunities presented by multi-omics data integration, electronic health records mining, and the need for data standardization. The study also addresses ethical considerations and regulatory challenges associated with AI implementation in drug development. Finally, we present emerging trends and prospects for AI in biopharmaceuticals, emphasizing the importance of collaborative ecosystems and the potential for AI to revolutionize personalized medicine. This review synthesizes current research and industry practices, providing insights into the transformative potential of AI in drug discovery and the challenges that lie ahead in realizing its full potential.

Significant progress has been made in recent years in the development of tensile and compressive strain capacity models. These models, along with various methods of strain demand determination, form the basic building blocks for the strain-based design and assessment (SBDA) of pipelines. At the same time, gaps exist between the current industry practice and the data needed for the proper application of those models. Furthermore the current practice of independently determining the tensile strain capacity, compressive strain capacity, and strain demand may not accurately represent field conditions as these elements interact and influence each other as opposed to act independently. Key elements related to SBDA are provided for the planning and execution of life-time integrity management of pipelines subjected to high longitudinal strains. The paper places emphasis on two aspects of SBDA: (1) overall framework and (2) considerations that are not adequately covered in the current general industry practices. The entire processes of SBDA, including but not limited to design, material specifications, construction, post-construction field monitoring, and mitigation are covered at high-levels to assist decision-making in practical projects. Detailed methodologies for executing components of SBDA are not covered in this paper, but can be found in the cited references.

The interation of data engineering processes with artificial intelligence has essentially transformed the handling of personal information by organizations, posing an enormous privacy risk in real-time data processing and automated workflow configurations. Ordinary security cannot be used to address the special issues that systems based on AI that continuously process streaming data and are distributed across architectures present. In the case of machine learning models operating on high-velocity data streams, they introduce points of exposure to the data lifecycle, specifically during ingestion and model inference. This implies that privacy protection strategies should be algorithmic and operate with strict time and computational constraints. Privacy-preserving technologies, such as differential privacy, federated learning, homomorphic encryption, and secure multi-party computation, among others, can be used to safeguard sensitive data and enable AI applications to work with the required data simultaneously. Privacy-by-design principles and privacy engineering methodology are organizational frameworks that provide systematic means of integrating privacy protections into every phase of system development. Legal requirements, including technical controls and governance processes, are outlined by the GDPR, CCPA, and new AI-specific rules to ensure data engineers comply with them. Privacy technologies do not only fulfill the legal requirements, but their impact on the economy and society is far more drastic. They radically alter the competitive landscape of the business, the level of trust that customers have in one another, and the operation of digital rights in societies that are becoming more data-driven. The decisions that organizations make about privacy protection must be balanced against their sustainability commitments, since privacy systems with high consumption of computing resources do have environmental impacts. Active privacy engineering strategies assist companies in achieving their innovation as well as privacy objectives simultaneously. This will provide them with a competitive advantage through greater trust of stakeholders and regulatory strength, as well as promote the ethical application of AI in accordance with democratic principles and individual autonomy.

The ENCRYPT project, funded under the Horizon Europe Framework, aims to advance privacy-preserving technologies across various sectors, ensuring robust data protection while maintaining utility. By providing users with core methodologies including the Fully Homomorphic Encryption, Trusted Execution Environments, Differential Privacy and advanced Hybrid Protection Services, ENCRYPT seeks to address the challenge of ensuring data privacy and utility, across federated data spaces within the EU. Differential privacy is an important approach within ENCRYPT, in protecting individual privacy in the growing landscape of digital data. In this paper, we provide an overview of fundamental concepts and present an overview of differential privacy foundations, examining its theoretical underpinnings and practical implementations. We also provide an insight into how it will be applied within the ENCRYPT project. Experiments carried out demonstrate that differential privacy can maintain high data accuracy despite the addition of noise, and we will describe how the ENCRYPT platform simplifies the use of this privacy-preserving technology for non-expert users by automating privacy parameter selection and model optimization. This approach enhances data security, efficiency and accessibility, helping to develop a more privacy-conscious environment for data analysis to carry out research and innovation in a secure and private manner. We will also explore potential future developments and applications of differential privacy within various industries and sectors.

Implementing artificial intelligence (AI) and blockchain technology in management systems transforms traditional libraries into advanced information centers that are data-driven and effectively managed. While these technologies enhance efficiency and operational capabilities, they also present two critical challenges: data privacy and ethical concerns. This study examines the role of AI and blockchain in library management, focusing on issues related to data privacy and ethical challenges that arise from their use. It also offers best practices to ensure safe implementation. The research adopts a comprehensive mixed-methods approach, involving qualitative interviews and quantitative surveys to identify these challenges within the system architecture, assess the effectiveness of current designs, and propose a complete framework using privacy-preserving technologies. This framework incorporates innovative cryptographic techniques, including homomorphic encryption, differential privacy, and zero-knowledge proofs, providing a novel model for the ethical use of AI in libraries. The findings indicate that robust data protection, transparency, and accountability are essential to building trust in AI-powered library services.

The rapid evolution of artificial intelligence (AI) has transformed how personal data is harnessed, driving innovation in diverse fields, from healthcare to finance. However, this transformation brings complex challenges in safeguarding individual privacy while leveraging vast amounts of personal information. This paper examines the dual role of AI as both a catalyst for technological advancement and a potential risk to privacy. By analyzing current trends in AI data usage, exploring the inherent challenges in balancing privacy and functionality, and highlighting emerging solutions—such as federated learning and differential privacy—the study offers a comprehensive overview of strategies for responsible AI development. The findings underscore the need for ethical frameworks, robust regulatory measures, and innovative privacy-preserving techniques to ensure that the benefits of AI do not come at the expense of personal data protection. Index Terms— Artificial Intelligence (AI), Personal Data, Privacy, Data Protection, Privacy-Preserving Machine Learning, Federated Learning, Differential Privacy, Ethical AI, Data Governance, Regulatory Compliance.

Introduction: This research examines the potential of cloud-based healthcare systems, enhanced by artificial intelligence (AI) and predictive analytics, to improve patient care. With increasing demand for more efficient healthcare solutions, AI-driven systems offer promising advancements in real-time health monitoring, risk assessments, and personalized treatment recommendations. By integrating machine learning algorithms like random forests, support vector machines (SVMs), and gradient boosting, these systems predict patient health trends and identify high-risk individuals. This research aims to demonstrate how AI-powered frameworks can optimize resource allocation, improve health outcomes, reduce costs, and enhance overall patient satisfaction. Top of Form Bottom of Form Objectives: This research are to develop AI-driven cloud-based healthcare systems for real-time patient monitoring, risk assessment, personalized treatment recommendations, and improved resource allocation to enhance patient care. Methods: This research employs a multi-stage methodology to evaluate the potential of AI-driven cloud-based healthcare systems. First, a comprehensive literature review is conducted to explore current advancements in healthcare technologies, AI algorithms, and cloud infrastructures. Next, various machine learning algorithms, including random forests, support vector machines (SVMs), and gradient boosting, are developed and integrated into predictive analytics frameworks. These algorithms process large-scale healthcare datasets to predict patient health, risk factors, and disease progression. The developed systems are tested in cloud environments to assess their effectiveness in real-time health monitoring, risk assessment, and targeted treatment suggestions. Top of Form Bottom of Form Results: AI-driven cloud-based healthcare systems significantly improve patient care. Predictive analytics frameworks, utilizing random forests, SVMs, and gradient-boosting algorithms, accurately forecast patient outcomes, identify high-risk individuals, and predict disease progression. Real-time health monitoring and risk assessment enabled by these systems allow healthcare providers to deliver more targeted treatment recommendations and optimize resource allocation. The research shows potential for enhanced patient outcomes, reduced healthcare costs, and improved patient satisfaction. These findings confirm that combining AI and cloud-based systems can transform the healthcare industry by offering data-driven, personalized care solutions. Conclusions: The cloud-based healthcare systems powered by AI and predictive analytics can significantly enhance patient care. By enabling real-time monitoring, accurate risk assessment, and personalized treatments, these systems improve health outcomes, reduce costs, and increase patient satisfaction, offering a transformative approach to modern healthcare management.  

Customer Relationship Management (CRM) platforms are undergoing a fundamental transformation driven by rapid advancements in Artificial Intelligence (AI). Modern CRM systems are no longer limited to transactional data storage or rule-based automation; instead, they are evolving into intelligent, adaptive ecosystems capable of predictive decision-making, autonomous workflows, and real-time personalization. This paper examines future trends shaping AI-driven CRM ecosystems, with a particular focus on machine learning, generative AI, autonomous agents, real-time analytics, and ethical AI governance. The study analyzes emerging architectural patterns, evolving use cases, and enterprise challenges associated with scalability, trust, and regulatory compliance. By synthesizing academic research and industry practices, the paper proposes a future-ready AI-CRM ecosystem model that supports intelligent customer engagement, operational efficiency, and strategic decision-making. The findings highlight how AI will redefine CRM from a supporting system into a core digital intelligence layer for enterprises. Artificial Intelligence (AI) is rapidly transforming Customer Relationship Management (CRM) platforms from transactional systems into intelligent, predictive, and autonomous enterprise ecosystems. Modern CRM solutions are no longer limited to data storage and workflow automation; instead, they are evolving into AI-driven decision platforms capable of real-time personalization, predictive analytics, and autonomous service orchestration. This paper explores future trends in AI-driven CRM ecosystems with a focused analysis on leading enterprise platforms such as Salesforce, Microsoft Dynamics 365, and SAP CRM. The study examines architectural evolution, embedded AI capabilities, data integration patterns, ethical considerations, and emerging technologies such as generative AI, autonomous agents, and industry-specific CRM intelligence. By synthesizing platform capabilities and future directions, this paper provides a structured framework for understanding how AI will shape next-generation CRM systems.

Abstract: This article examines the transformative impact of machine learning (ML) on data analytics and reporting practices, highlighting its potential to revolutionize decision-making processes across various industries. We comprehensively analyze current literature and industry practices and explore how ML techniques enhance data processing, predictive analytics, and visualization capabilities. The article delves into advanced ML applications, including automated data cleaning, feature engineering, and real-time analytics, demonstrating their efficacy in extracting actionable insights from complex datasets. Furthermore, we investigate the role of ML in generating intelligent reports, creating interactive dashboards, and providing contextual recommendations. The article also addresses critical challenges such as data security, regulatory compliance, and ethical considerations in ML-driven analytics. Our findings suggest that the integration of ML in data analytics and reporting not only improves accuracy and efficiency but also enables more sophisticated, data-driven decision-making strategies. This article contributes to the growing body of knowledge on ML applications in business intelligence and provides a foundation for future research in this rapidly evolving field.

Employee attrition is a critical challenge for organizations, impacting productivity, operational costs, and workforce stability. Traditional approaches to managing attrition rely on reactive strategies, often failing to provide predictive insights. The advent of Artificial Intelligence (AI) has transformed attrition management by enabling data-driven decision-making, predictive analytics, and proactive employee engagement.This research explores the role of AI in predicting and managing employee attrition through machine learning algorithms, natural language processing (NLP), and AI-driven sentiment analysis. AI models analyze vast datasets, including employee performance metrics, engagement surveys, and organizational culture indicators, to identify early warning signs of attrition. Predictive analytics empowers HR professionals to implement targeted retention strategies, enhance employee experience, and reduce voluntary turnover.Furthermore, AI-driven chatbots and virtual HR assistants contribute to employee satisfaction by offering personalized career development suggestions, real-time feedback, and mental well-being support. Explainable AI (XAI) frameworks ensure transparency in AI-driven decisions, fostering trust between employees and organizations. Despite AI’s potential, ethical concerns, data privacy, and algorithmic biases remain key challenges that require robust governance frameworks.This study provides a comprehensive analysis of AI applications in attrition management, highlighting case studies from multinational corporations that have successfully integrated AI for workforce retention. The findings underscore AI's transformative potential in HRM, enabling organizations to shift from reactive to proactive attrition management strategies. The paper concludes with future research directions on AI’s evolving role in predictive HR analytics and its integration with emerging technologies like blockchain and the metaverse for enhanced workforce planning.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure