This paper explores brain-inspired computing as a transformative approach that integrates principles from neurobiology with artificial intelligence (AI) to enhance computational efficiency and adaptability. By mimicking neural structures and cognitive processes, brain-inspired models aim to overcome limitations of traditional AI systems, enabling more robust learning, pattern recognition, and decision-making. The study reviews key neurobiological mechanisms, such as neural plasticity and parallel processing, and discusses their applications in neuromorphic hardware and advanced AI algorithms. This interdisciplinary convergence offers promising pathways for developing intelligent systems that closely emulate human brain function.

Contemporary digital transformation initiatives have intensified the demand for professionals capable of effectively connecting technological capabilities with strategic business objectives, individuals commonly recognized as tech-business translators. While their organizational significance continues to expand, the specific competencies necessary for success in these interdisciplinary positions remain inadequately conceptualized and lack systematic examination. This research addresses that gap by introducing the first integrated skills taxonomy that bridges existing disconnects across academic research, employment market intelligence, and industry practice. Drawing on an analysis of over 50 job advertisements, recent scholarly literature, and leading industry reports, we identify six primary competency areas, ranging from strategic reasoning and interpersonal communication to artificial intelligence proficiency and quantitative analysis. The proposed framework offers clear parameters to guide recruitment, learning and development (L&D) programs, and curriculum design. By articulating the essential competencies for these multidisciplinary professionals, this study lays foundational groundwork for future empirical research and supports talent development in digitally transformed organizational environments.

In contemporary IT organizations, Change Advisory Boards (CABs) are entrusted with ensuring that technical changes—ranging from infrastructure updates to software deployments—do not compromise operational stability. These boards traditionally rely on manual processes, expert judgment, and historical precedents to assess risk and approve changes. While effective in low-volume environments, this model begins to break down as the rate and complexity of changes increase in DevOps-driven ecosystems. The reactive nature of traditional CAB operations often leads to delays, inconsistent risk assessment, and suboptimal change approvals.

This research introduces a machine-learning-based system designed to augment CAB decision-making with automated, data-informed risk evaluations. By pulling data from multiple IT sources—including change records, deployment logs, historical incidents, and system health indicators—the proposed framework applies predictive analytics to compute a quantifiable risk score for each change. This score, combined with interpretable AI justifications, provides CAB members with clear, actionable insights. The goal is not to replace human oversight but to enhance it by directing attention to high-risk cases and accelerating the approval of safe, repetitive changes.

The paper outlines the architecture, data pipeline, feature engineering, model training methodology, risk scoring strategy, and interface integrations. Results from experimental trials demonstrate tangible benefits in incident reduction, review efficiency, and decision consistency. By embedding this system into ITSM workflows, organizations can reduce change failure rates and enhance service reliability while maintaining governance and compliance.

As cyber threats evolve, attackers increasingly target financial retirement accounts like 401(k)s, exploiting their high-value nature and weak user-level security controls. Traditional defenses—passwords, OTPs, and device fingerprinting—have proven insufficient in detecting sophisticated account takeovers. This paper presents a behavioral biometrics framework that uses artificial intelligence to continuously authenticate users based on typing patterns, mouse movements, login behavior, and navigation habits. Instead of static credentials, the system builds a behavioral profile for each user and detects anomalies in real-time. Our framework aims to catch suspicious access attempts without interrupting legitimate users. By integrating seamlessly into existing financial platforms, this solution offers a balance of strong security and low user friction. We evaluate the framework in a simulated environment using behavioral data from anonymized user sessions, achieving high accuracy in detecting imposters while minimizing false alarms.