This research presents the architecture and development of an AI-powered compliance engine tailored for the Workforce Innovation and Opportunity Act (WIOA). The system is designed to automate adherence to complex federal and state mandates with high precision and minimal manual oversight. By integrating machine learning (ML), natural language processing (NLP), and regulatory knowledge graphs, the engine enables real-time compliance monitoring, automated documentation validation, and dynamic error correction. The proposed framework addresses long-standing inefficiencies in the public workforce system, where manual processes often lead to audit errors, delayed service delivery, and data inconsistencies. In simulated deployment environments, the engine achieved a documentation validation accuracy of 97%, resolved compliance flags within 48 hours, and reduced audit preparation time by over 60%. When tested with anonymized case data from a regional workforce board, the system showed the potential to cut audit findings by 80% and reduce per-case audit processing time from 90 minutes to just 22 minutes. Manual interventions dropped by over 40%, freeing staff to focus more on participant engagement, career planning, and service coordination. These projected outcomes highlight the engine’s potential to transform WIOA compliance from a reactive, labor-intensive process into a proactive, intelligent workflow. Beyond automation, the system functions as a decision-support tool for frontline staff, administrators, and policy analysts—bridging the gap between regulatory rigor and service delivery. This paper details the system’s technical architecture, key components, validation simulations, and proposes a roadmap for scalable implementation across regional and state workforce agencies.

Plant diseases pose a significant threat to global food security, leading to substantial crop losses and economic instability. The emergence of Internet of Things (IoT) technologies offers promising avenues for early and precise plant disease detection through real-time data collection. However, traditional centralized IoT architectures are susceptible to critical vulnerabilities, including data integrity breaches, security risks, privacy concerns, and single points of failure. This article proposes an enhanced framework for IoT-based plant disease detection systems by integrating blockchain technology and Web3.0 principles. Drawing insights from secure data management in other critical sectors like healthcare [1, 4, 13, 14, 15, 16, 19, 20, 28, 30], this framework leverages blockchain's decentralized, immutable ledger for secure and verifiable data storage, and Web3.0's emphasis on data ownership and decentralized applications (dApps) for enhanced user control and transparency. The proposed integration aims to establish a robust, trustworthy, and efficient ecosystem for agricultural data, facilitating more accurate disease diagnosis, enabling secure data sharing among stakeholders, and fostering a new paradigm of decentralized, intelligent agriculture.

Large Language Models (LLMs), such as GPT-4, Claude, and Gemini, are reshaping the cyber threat landscape, particularly in the domain of social engineering. These models empower adversaries to automate, personalize, and scale phishing, impersonation, and business email compromise (BEC) attacks with unprecedented realism. Unlike traditional social engineering techniques, LLM-driven threats can adapt to contextual cues, simulate executive communication patterns, and generate deepfake audio or video to enhance credibility. As such, conventional security awareness programs and static detection mechanisms are proving insufficient against the sophistication and speed of these AI-enabled attacks.

This paper investigates the role of generative AI in enabling next-generation social engineering threats and introduces a multi-layered defense strategy. The proposed framework spans technical solutions such as behavioral anomaly detection, AI-driven phishing simulation, and real-time synthetic media analysis as well as human-centric and policy-based countermeasures. Additionally, the study explores adversarial AI, data poisoning, and red teaming as both offensive and defensive mechanisms. Grounded in emerging trends, case studies, and explainable AI (XAI) techniques, this research emphasizes the urgency of adopting adaptive, intelligence-driven cybersecurity practices. The findings aim to inform practitioners and policymakers on building resilient systems capable of detecting, mitigating, and responding to AI-powered social engineering attacks in real time..