The rapid expansion of the Internet of Things (IoT) has introduced an unprecedented number of interconnected devices, creating new opportunities for automation and data exchange but simultaneously increasing the attack surface for cyber threats. Among these, botnet attacks have emerged as one of the most severe threats, capable of compromising massive networks of IoT devices for malicious purposes such as Distributed Denial of Service (DDoS) and data exfiltration. This research proposes a Lightweight Deep Learning-based Intrusion Detection System (DL-IDS) designed to efficiently detect IoT botnet activities with high accuracy and minimal computational overhead. The proposed model integrates a hybrid Autoencoder–Long Short-Term Memory (LSTM) architecture that captures both spatial and temporal traffic features. The system preprocesses raw network data through feature selection, Min–Max normalization, and class balancing using SMOTE, before training on benchmark datasets such as BoT-IoT and UNSW-NB15. Evaluation results demonstrate that the model achieves an overall accuracy of 99.4%, precision of 99.2%, and AUC score of 0.997, outperforming traditional machine learning and hybrid deep learning baselines (CNN–LSTM, GRU-hybrid, and SVM-Ensemble). Furthermore, the lightweight design of the model ensures its deployability in real-time edge computing environments. Experimental validation on a Raspberry Pi 4 (8 GB) confirms that the proposed DL-IDS maintains low latency and minimal memory consumption, making it well-suited for IoT gateway and edge-level applications. This study concludes that lightweight deep learning frameworks can effectively balance detection accuracy and computational efficiency, contributing significantly to securing modern IoT infrastructures against evolving botnet threats.

The rapid expansion of artificial intelligence (AI) and digital health technologies has made data sovereignty a critical policy concern. In Saudi Arabia, Vision 2030 emphasizes technological innovation, economic diversification, and resilient digital infrastructures, positioning data governance as a foundation for national transformation. This review examines how data sovereignty strategies can support the Kingdom’s digital health transformation while ensuring compliance with international standards. Using a qualitative approach, the study synthesizes policy documents, institutional reports, and peer-reviewed literature. The analysis identifies both enablers and barriers: strong institutions such as the Saudi Data and Artificial Intelligence Authority (SDAIA) and the National Data Management Office (NDMO), alignment of the Personal Data Protection Law (PDPL) with frameworks like the GDPR, but also challenges including rigid intellectual property rules, fragmented cloud data governance, and weak sector-specific policies. The study’s objectives are to clarify these challenges, assess their impact on generative AI adoption, and recommend reforms that align with Vision 2030’s innovation goals. Policy recommendations include adaptive IP frameworks, sector-specific governance models, inter-agency coordination, and public–private partnerships to expand access to lawful datasets. The findings demonstrate that data sovereignty, when strategically designed, functions not as a barrier but as an enabler of secure, scalable, and ethically responsible innovation in healthcare and beyond. This work contributes actionable insights for policymakers, regulators, and researchers in Saudi Arabia and offers lessons for other nations navigating the balance between sovereignty and competitiveness in the digital era.

Artificial Intelligence (AI) is transforming recruitment by allowing organisations to employ data-driven hiring decisions. However, AI-powered tools are prone to reinforcing biases instead of eliminating them, posing ethical and fairness concerns. Since AI-powered tools are critical in hiring, this study introduces HITHIRE, an AI-driven recruitment model designed to enhance transparency, fairness, and inclusivity within the diverse workforce of Saudi Arabia, which aligns with Vision 2030. A baseline model was first evaluated using Llama 3.1, BERT, and regular NPL techniques. However, the baseline model revealed significant biases in gender and nationality-based hiring, making it inappropriate for the Saudi Arabian diverse hiring environment. The Llama 3.1 model was enhanced through data augmentation, sentence transformers, standard scoring, and transparency mechanisms, resulting in the HITHIRE model. Fairness analysis demonstrated improvements across gender and nationality dimensions, with reduced Statistical Parity Difference (SPD) and Disparate Impact (DI) scores. The findings highlight the potential of ethical AI integration in recruitment, ensuring unbiased, accountable, and transparent hiring practices. HITHIRE sets a precedent for AI-driven fairness in recruitment, contributing to HR policies and ethical AI discourse globally.

The Internet of Things (IoT) has emerged as a transformative technology connecting physical devices through intelligent networks that enable real-time data exchange, automation, and decision-making. This research presents the design and implementation of an IoT-based smart monitoring system aimed at enhancing security and regulatory compliance within the Ministry of Commerce in Saudi Arabia. The proposed solution utilizes motion and pressure sensors integrated with CCTV cameras, GPS modules, and a MongoDB database to automatically detect and record unauthorized reopening of closed commercial establishments. Once a violation is detected, the system generates instant alerts and transmits notifications to the nearest inspector through a mobile application, providing detailed location and timestamp information. This automation minimizes the dependence on manual inspections, reduces operational costs, and improves enforcement efficiency in line with the Kingdom’s Vision 2030 digital transformation goals. The study further discusses the potential business impacts, integration challenges, and key security and ethical considerations related to IoT deployment in public sector environments. Overall, the proposed solution demonstrates how IoT technologies can significantly strengthen compliance monitoring, enhance public safety, and support sustainable digital governance initiatives. Prototype testing of the developed IoT system was conducted in a controlled environment to validate detection accuracy and response time. Results demonstrated reliable operation with average alert latency under five seconds and over 95 percent detection accuracy, confirming the system’s practical feasibility.

Large Language Models (LLMs) are increasingly deployed in multi-agent systems; however, existing frameworks continue to suffer from communication ambiguity, coordination failures, and poor scalability as task complexity increases. This paper introduces COLLAB-LLM, a communication-centric, role-based framework designed to enable reliable and scalable collaboration among LLM agents. The framework combines a structured communication protocol, a hierarchical role architecture, and a dynamic distributed task-graph engine to support coordinated planning, efficient negotiation, and adaptive task execution. COLLAB-LLM is evaluated on over 120 complex, multi-step tasks spanning software engineering, business process automation, and scientific research synthesis. Task success is defined using task-specific completion criteria, with a task considered successful when the aggregate completion score exceeds 0.8. Under identical underlying LLM configurations, COLLAB-LLM achieves an 89% overall success rate, representing a 13–19% improvement over strong single-agent and multi-agent state-of-the-art baselines, with statistically significant gains in performance, communication efficiency, and robustness. Experimental results demonstrate that structured communication and role specialization substantially reduce ambiguity, improve collaboration quality, and enable scalable coordination for teams of up to eight agents. This work establishes foundational design principles for high-performing collaborative AI systems and provides a practical, reproducible pathway toward scalable, human-aligned multi-agent LLM architectures. All experimental artifacts, task definitions, prompts, and evaluation scripts will be released to support reproducibility.