Federated Learning (FL) has emerged as a transformative approach for training machine learning models across distributed devices without transferring raw user data, making it particularly suitable for on-device personal assistants. This paper investigates the performance, privacy, and security trade-offs inherent in applying FL to personal assistant systems. It explores how FL enables personalized experiences while preserving user confidentiality, and evaluates challenges related to model accuracy, communication overhead, adversarial attacks, and data heterogeneity. Through a critical review of recent advancements and experimental frameworks, the study outlines design considerations for optimizing FL deployments in real-world personal assistant applications. The paper concludes by proposing future research directions to enhance the robustness, efficiency, and trustworthiness of FL-powered personal assistants.

Identity protection is an essential component of enterprise security in the current era. With phishing, credential theft, and adversary-in-the-middle (AiTM) attacks persisting and morphing, traditional authentication methods like passwords and omnipresent multi-factor authentication (SMS, OTP, push notification, etc.) are proving increasingly inadequate. This article provides an in-depth examination of two modern and popular authentication protocols, namely FIDO2/WebAuthn and Certificate-Based Authentication (CBA). FIDO2 facilitates passwordless authentication with the assistance of cryptographic credentials securely bound to a person's device, offering improved usability and phishing resistance. CBA, rooted in public key infrastructure (PKI), remains a necessary requirement in compliance-focused environments and is crucial for safeguarding human and machine identities. This study explores how these technologies operate across diverse contexts, from enterprise-owned notebooks to personal mobile devices and non-human account systems. Using internationally accepted standards and frameworks—such as NIST SP 800-63-3, the CISA Zero Trust Maturity Model, and eIDAS—the document provides implementation considerations that incorporate policy and identity credential lifecycle approach techniques. It also evaluates operational recovery and fallback processes in cases of credential loss or compromise. A structured framework is provided to enable organizations to achieve identity assurance at scale and support evolving technology and regulatory demands. Future trends such as passkeys, derived credentials, quantum computing, and modular authentication systems are also considered, which will introduce flexibility and strength in the identity assurance landscape.

E-commerce sites and marketers need to personalize customer experiences without breaking the law because people are becoming more worried about data privacy and third-party cookies are being phased out. This paper shows how to use machine learning to create a framework for customer segmentation and media optimization that protects privacy. The system is made to work in decentralized, privacy-sensitive settings. It uses unsupervised clustering, predictive modeling, and real-time decisioning engines to give users useful information without giving away their identity. Our method uses federated learning and cleanroom technologies to make sure that it follows laws like GDPR and CCPA. This is different from traditional commercial segmentation tools that rely heavily on centralized data collection and unclear personalization methods. The framework shows big improvements in performance when tested on real-world e-commerce datasets. It gets a 23% increase in Return on Ad Spend (ROAS), a 17% increase in conversion rates, and a 14% drop in cost-per-acquisition. The proposed solution is a scalable and compliant replacement for old marketing tools. It lets you target people more accurately and buy media more efficiently in today's changing digital world.

In today’s fast-evolving threat landscape, ransomware attacks have become more sophisticated, faster, and more destructive leaving traditional Security Operations Center (SOC) response strategies struggling to keep pace. Traditional SOC workflows struggle to match the speed and complexity of modern ransomware attacks. Manual processes like alert triage, incident scoping, and containment often consume critical hours giving adversaries ample opportunity to encrypt data, exfiltrate assets, and demand ransoms. AI-optimized SOC playbooks are redefining this paradigm by automating the entire investigation lifecycle. Leveraging machine learning, LLMs, and real-time telemetry analysis, these systems rapidly identify high-fidelity threats, enrich alerts with contextual intelligence, and scope incidents with minimal analyst input reducing response time from hours to mere minutes.

Generative AI further accelerates this shift by auto-generating attack summaries, mapping indicators to known threat tactics, and recommending or initiating containment actions such as isolation or credential revocation. These playbooks evolve continuously by learning from analyst feedback and past events, improving both accuracy and efficiency over time. The result is a measurable reduction in mean-time-to-detect (MTTD) and mean-time-to-respond (MTTR), while empowering SOC analysts to focus on strategic analysis over repetitive triage. As ransomware campaigns grow faster and more autonomous, adopting AI-driven SOC playbooks has become a mission-critical step for organizations seeking proactive, resilient security operations.

The recent evolution of cloud computing demands that systems are able to self-diagnose and self-heal as well as constantly optimize without human intervention. This paper provides an in-depth review of the self-healing software architectures in cloud computing, focusing on AI-induced detection and recovery methods. The authors talk about how self-healing systems have changed from traditional ideas to modern AI-powered systems and categorize the main types of methods used for synchronization, tracking, and fixing problems in today's cloud services. Based on a systematic review of available literature, we investigate essential issues such as fault detection accuracy, recovery time optimization, and system reliability improvement. The study finds that although much has been achieved in self-healing, the existing approaches are not yet able to efficiently deal with complex fault situations and to reduce the level of service interruption. Our results suggest that the application of large language models updated using machine learning has the potential to deliver up to an 85% increase in the accuracy of fault prediction and a 60% reduction in system downtime as compared to state-of-the-art approaches. Finally, we talk about what future research should focus on, including the necessary understanding and development of new AI models, different system structures, and standard ways to measure how well self-healing cloud systems work.

Mass production and distribution companies face multifaceted challenges in managing customer deductions, promotional rebates, and trade settlement processes. Fragmented and manual deduction workflows have contributed to revenue leakage, aging receivables, and delays in cash application. This paper explores the implementation of Oracle Channel Revenue Management (ChRM) integrated with Oracle Receivables to create a streamlined, automated solution for end-to-end deduction processing, from claim initiation through settlement and accounting. Drawing upon the case of a food packing company’s U.S. operations, the study highlights how a rules-based, system-driven approach enhanced accuracy and control. Notably, the company achieved a 90% reduction in manual write-offs and shortened average claim resolution time from 21 days to 8 days. The integrated model not only minimized manual intervention but also improved transparency, strengthened financial compliance, and enhanced working capital efficiency. These results affirm that Oracle ChRM, when deployed strategically, can transform deduction management into a driver of operational and financial excellence.

The paper is expansive on the psychology of visual perception in dashboards in a push to design for impact. Visualization is a game of storytelling that ensures everything meets human perception in their designs. It is based on building and developing better data visualizations to show how everything would be designed. It is explicit in terms of interpreting and communicating information in making sure that everything is applied to how it works in understanding different psychological requirements to show its application and use, based on improving how information can be applied and improved to make sure that it fits what is expected, based on improved visual impacts. Data interpretation works on processing data requirements to balance information delivery and aesthetics in the data visualizations. The paper is key in providing information that starts with improving compelling visuals, which come with improving the focus on data improvement and improving the essence that works on cognitive load and balance for understanding and reducing clutter or distractions of the data visuals. Organizing and improving information is an important aspect in ensuring that data visualization is effective and reliable, with related data points being explicit and improved in how viewers or the audience view and relate to the information being viewed. Case findings are that there lacks more knowledge in terms of how designing for impact is the future for data visualizations to make visual impact key.

The telecom sector now prioritizes real-time data accuracy because of increased consumer demand for top-notch customer experiences. The study investigates the connection between real-time data accuracy and customer satisfaction in telecom services. Accurate real-time information becomes essential for shaping customer experiences throughout service delivery and support functions as businesses increasingly depend on data-driven decision-making. This research demonstrates the connection between data inconsistencies and problems that include billing errors along with service interruptions which result in customer dissatisfaction. The study employed a mixed-method approach with qualitative interviews and quantitative surveys among telecom customers and service providers to examine the relationship between data accuracy and customer satisfaction. Real-time data accuracy builds customer trust while reducing resolution time for service problems and increasing customer loyalty. The research emphasizes that inaccurate data erodes customer trust leading to service churn which negatively impacts telecom companies' reputation. The research explores how technology such as AI and machine learning helps maintain real-time data accuracy while automation presents opportunities to reduce human errors. The study proposes several strategies for telecom companies to utilize precise real-time data to boost service quality while enhancing operational efficiency and achieving greater customer satisfaction. The research enhances comprehension of how data precision interacts with customer-focused business tactics in the telecommunications sector.

Cloud computing offers organizations flexibility, scalability, and efficiency, but it can also become quite costly if resources are not managed well. Many companies face unplanned and unexpected expenses and waste resources because cloud services have complicated pricing models. Usage patterns change daily. Artificial Intelligence (AI) provides a smarter method to reduce cloud costs by analyzing data, making predictions, and automating actions. AI driven framworks can review past usage patterns of utilized resources, forecast future requirements, and recommend the best settings. This includes adjusting resource sizes, automatically turning services on or off, and choosing the right service options. AI can also detect unusual spending, alert organizations about potential cost increases, and make automatic adjustments to prevent waste. By using AI in this way, organizations save money, boost system performance and reliability, and comply with regulations more easily. This improves the efficiency of cloud operations and makes sure that costs match business goals. This paper looks at how AI-driven cost optimization functions, its benefits, and its challenges. It highlights its importance for sustainable and cost-effective cloud usage in the future.

We are sitting at the cusp of an AI-driven revolution in the Wealth Management advisory space. AI-enabled intelligent automation and customized intelligent wealth management are fast emerging as a gold standard for wealth advisors. This research examines various studies focused on how technology can elevate wealth advisory services and enable scalable personalization while preserving client trust and strategic insight. As technology enables hyper-personalized services, advisors face increasing pressure to differentiate themselves while continuing to support different segments of the wealth bands in asset growth, protection, and legacy planning. Standing out in the age of Hyper-personalization is the key to success for wealth advisors. Wealth managers need to personalize client interactions owing to the increasingly sophisticated needs of the clients and to set them apart in wealth management experiences. With the rise of intelligent wealth management systems, advisors can leverage artificial intelligence, behavioral analytics, and algorithmic processing to offer bespoke services to different segments of the Clients. Today’s clients expect more than financial expertise—they demand transparency, technology integration, and highly personalized strategies. Meeting these high expectations requires constant innovation and adaptability. The author intends to provide actionable insights to wealth managers to differentiate their offerings and justify their premium services.

The growing number of cybersecurity threats against U.S. healthcare systems resulted in 133 million patient record breaches during 2023 which caused massive financial losses and destroyed patient trust. The National Health Information Systems (NHIS) need enhanced security measures to fulfill HIPAA requirements and achieve better health care interoperability. This paper establishes a framework which combines Artificial Intelligence (AI) with blockchain technology to create healthcare data protection that increases data confidentiality and connectivity among healthcare systems along with trust in the process. AI technology offers real-time threat recognition and compliance tracking through its system while Blockchain creates a permanent audit tracking system to control decentralized data management. Agile methodologies allow businesses to implement projects in cycles for stakeholder suitability through alignment. The paper uses real-world examples together with assessment of difficulties and exemplary practices to show how the framework deals with breaches and achieves CMS interoperability targets and provides enhanced treatment outcomes. The approach for securing NHIS moves healthcare systems forward while strengthening national priorities through fostering care equity and delivery resilience.