Healthcare organizations are beginning to embed agentic AI into routine workflows, including clinical documentation support and early-warning monitoring. As these capabilities diffuse across departments and vendors, health systems face agent sprawl, causing duplicated agents, unclear accountability, inconsistent controls, and tool permissions that persist beyond the original use case. Existing AI governance frameworks emphasize lifecycle risk management but provide limited guidance for the day-to-day operations of agent fleets. We propose a Unified Agent Lifecycle Management (UALM) blueprint derived from a rapid, practice-oriented synthesis of governance standards, agent security literature, and healthcare compliance requirements. UALM maps recurring gaps onto five control-plane layers: (1) an identity and persona registry, (2) orchestration and cross-domain mediation, (3) PHI-bounded context and memory, (4) runtime policy enforcement with kill-switch triggers, and (5) lifecycle management and decommissioning linked to credential revocation and audit logging. A companion maturity model supports staged adoption. UALM offers healthcare CIOs, CISOs, and clinical leaders an implementable pattern for audit-ready oversight that preserves local innovation and enables safer scaling across clinical and administrative domains.

This systematic review investigates the impact of Generative Artificial Intelligence (GenAI) on supply chain management (SCM). Though there is an acknowledgment of GenAI’s potential in SCM, there remains a lack of research that thoroughly examines the opportunities, tangible advantages, and challenges associated with GenAI adoption in SCM. This literature review seeks to analyze the role of GenAI in SCM and fulfill three primary objectives. First, it identifies the key opportunities for GenAI implementation in SCM, encompassing its applications in demand forecasting, inventory control, and decision-making support systems. Second, it assesses the tangible benefits observed from current implementations, such as cost savings and enhanced operational efficiency. Third, the study investigates the challenges and obstacles to effective GenAI adoption, focusing on technical limitations, data quality concerns, workforce skill deficiencies, and ethical issues. This research followed the systematic review methodology, reviewing and synthesizing information from 24 peer-reviewed articles published between 2023 to 2024. The findings reveal substantial potential for GenAI in SCM areas like demand forecasting, inventory optimization, and risk management while emphasizing significant data quality, integration, and organizational preparedness challenges. The study offers two practical implications: a) the findings provide actionable insights for supply chain professionals, highlighting how GenAI can be integrated into SCM; b) the research underscores the necessity for strong data governance, ethical AI usage policies, and adherence to evolving regulations. Objective: This study aims to understand GenAI’s role in SCM, identify key opportunities for implementing GenAI in supply chain processes, analyze the tangible benefits reported in existing implementations, and examine the challenges and barriers to successful GenAI adoption. Method: This review adhered to PRISMA guidelines, applying rigorous inclusion and exclusion criteria across multiple databases to yield 24 peer-reviewed articles from 2023–2024. Data extraction, quality assessment, and synthesis were conducted systematically to ensure unbiased insights into GenAI’s impact on supply chain management. Results and Discussion: The results reveal that GenAI significantly enhances SCM processes such as demand forecasting, inventory optimization, and risk management, while also exposing challenges in data quality, system integration, and organizational readiness. The discussion contextualizes these findings within the theoretical framework, emphasizing practical implications and addressing study discrepancies and limitations. Research Implications: This research expands the exploration of GenAI in SCM and encourages further investigation into emerging technologies to optimize supply chains. For the SCM industry, it highlights a strategic approach to GenAI adoption, emphasizing technology investments, workforce training, data integrity, and ethical practices to boost operational efficiency and resilience. Originality/Value: This study contributes to the literature by examining the practical integration of GenAI into supply chain management, addressing underexplored technical, organizational, and ethical challenges. The relevance and value of this research are evidenced by its actionable insights, which can guide practitioners and policymakers in enhancing operational efficiency, innovation, and resilience in supply chains.

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs)...

The Industrial Internet of Things (IIoT) 's convergence with hybrid cloud infrastructures transforms industrial operations through scalable, real-time data processing and automation. However, this evolution introduces complex cybersecurity risks, including broadened attack surfaces and dynamic trust boundaries. This chapter proposes a cybersecurity framework that integrates AI-driven anomaly detection with Zero Trust Architecture (ZTA) to address these challenges. The framework spans edge, fog, and cloud layers and leverages models like Autoencoders, LSTMs, and Isolation Forests for real-time, contextual threat detection. ZTA principles enforce microsegmentation, identity validation, and adaptive access control. Empirical use case based on the SWaT and WADI datasets validates the framework's efficacy in detecting cyber-physical anomalies. Additionally, the solution aligns with standards such as NIST 800-207, IEC 62443, and ISO/IEC 27001, offering practical guidance for secure, adaptive IIoT deployments.

The Technology-Organization-Environment (TOE) framework is widely applied in organizational technology adoption research, yet its measurement practices remain fragmented. Across studies of EDI, cloud computing, blockchain, AI, and other contexts, researchers routinely rename, adapt, or recombine constructs without documenting how their operationalizations relate to prior work, producing a literature that is empirically rich but difficult to accumulate. This study addresses that problem by developing a measurement catalog of 14 reusable TOE constructs drawn from 45 empirical anchor studies. Using a targeted construct-selection approach, the study retained constructs that were peer-reviewed, tested at the firm level, statistically validated, and generalizable across technology domains. Related aliases were consolidated under canonical names through three documented rules based on shared theoretical mechanisms, item-level overlap, and functional equivalence. The catalog organizes constructs across the technological, organizational, and environmental contexts, provides core definitions with recommended measurement facets, and includes representative survey items with reported reliability coefficients. Beyond consolidation, this study identifies persistent gaps, including limited post-adoption measurement, weak readiness-capability differentiation, and underdeveloped governance constructs for emerging technologies. The catalog serves as a practical starting point for researchers designing TOE-based survey instruments and conceptual models, strengthening construct consistency while preserving the framework’s flexibility.

The healthcare sector has become one of the most frequent targets for cyberattacks due to the sensitivity and high value of patient data. This study systematically analyzes healthcare data breaches over the past decade using breach data reported to the U.S. Department of Health and Human Services Office for Civil Rights (HHS-OCR). Employing descriptive statistics, correlation analysis, and chi-square testing, the study identifies hacking and IT incidents as the most prevalent and damaging breach types, with healthcare providers being the most frequently targeted entities. Geographic and organizational disparities in breach frequency and scale underscore the need for entity-specific security strategies. Notably, there is no significant correlation between breach frequency and the number of individuals affected, indicating that a small number of large-scale incidents contribute disproportionately to the overall impact. These findings provide healthcare leaders, policymakers, and IT professionals with data-driven insights to enhance cybersecurity resilience in an increasingly digitized healthcare environment.