Jul 2026· East African Journal of Information Technology· Vol 9, pp. 461-473· 0 citations
TL;DR
A fully automated situational awareness framework that leverages Generative Artificial Intelligence, specifically Large Language Models (LLMs), to extract, analyse, and synthesise actionable information from social media streams for real-time monitoring and situational awareness of critical infrastructure in resource-constrained environments is proposed.
Abstract
Monitoring critical infrastructure (CI), particularly national power distribution networks in developing countries such as Uganda, remains a significant challenge due to the limited deployment, high operational costs, and maintenance requirements of conventional monitoring technologies, including Supervisory Control and Data Acquisition (SCADA) systems, field sensors, and roadside surveillance infrastructure. These limitations often result in inadequate real-time situational awareness during service disruptions, delaying incident detection and response. Meanwhile, social media platforms such as X (formerly Twitter) have emerged as valuable sources of real-time, user-generated information that can provide rapid insights into infrastructure-related events. However, the unstructured, noisy, multilingual, and context-dependent nature of social media data presents significant challenges for traditional artificial intelligence and data analytics approaches. This study proposes a fully automated situational awareness framework that leverages Generative Artificial Intelligence, specifically Large Language Models (LLMs), to extract, analyse, and synthesise actionable information from social media streams. The proposed pipeline integrates Twitter API-based data collection, LLM-driven relevance filtering and event classification, geolocation inference for identifying affected areas, and visualisation tools for presenting incident information in an accessible format. Given the limitations of the free-tier Twitter API, a synthetic dataset of 150 tweets was constructed to support system development, prompt tuning, and evaluation. Synthetic data generation was carried out using Grok, a generative AI model with web search integration, which was instructed to search for current infrastructure conditions and generate realistic tweets that reflected those conditions. By combining natural language understanding with automated information extraction, the system transforms dispersed social media posts into meaningful intelligence that can support infrastructure operators and emergency response teams. A case study focusing on power disruption monitoring in Uganda was used to evaluate the effectiveness of the proposed approach. The study also addresses key challenges, including ambiguous location references, local language expressions, informal communication styles, and variations in outage-related terminology. The findings suggest that the proposed framework offers a scalable, cost-effective, and adaptable solution for enhancing real-time monitoring and situational awareness of critical infrastructure in resource-constrained environments.
The rapid development of Large Language Models (LLMs) and Artificial Intelligent (AI) powered autonomous agents has fundamentally changed the existing forms of software governance. In spite of the rigorous standards of transparency and account ability required according to the international frameworks such as the European Union's AI Act, there is a considerable gap between theory and reality. The present study discusses the inherent drawbacks of currently utilized platforms for LLM evaluation, machine learning workflow, and application performance monitoring in general. It has been shown that current disjointed solutions fail to protect unbound state space agentic architecture from serious threats such as alignment drift, SaaS security concerns, and unauthorized deployment of shadow AI systems. Moreover, a solution is proposed for overcoming the discussed challenges in form of a coherent multi-level AI governance stack Traccia built on the top of OpenTelemetry infrastructure platform. Traccia resolves the last mile for AI Alignment by adding the telemetry data, passive semantic guardrail assessment, and execution lineage into a hashed trace ledger. Traccia automatically creates compliance evidence packages by appending tamper-resistant fingerprints and SHA-256 content hash, that map to regulatory requirements (Articles 12, 14, 19, 26(6), and 50 of the EU AI Act) without invading any data privacy. By performing this evaluation in a methodical manner, a solid machine-readable base has been created for enterprise-wide management of autonomous AI systems.
Smart grids are becoming more complicated with distributed energy resources, dynamic load changes, real-time operating requirements. The conventional black-box artificial intelligence (AI) models lack in trust, transparency and compliance with regulations, thereby limiting their use in practical deployment. To resolve these issues, this paper suggests a conceptual unified GenAI-XAI framework for smart grids. Explainable Artificial Intelligence (XAI) offers an interpretable and reliable decision-making ability for existing scalability and real-time integration related issues. Recent advances in Generative AI (GenAI) complement XAI by supporting in scenario modeling, intelligent decision assistance, and synthetic data generation. The study presents a comparative analysis of XAI methods and a systematic taxonomy of GenAi applications in anomaly detection, load forecasting, energy optimization, and predictive maintenance The proposed conceptual framework help to improves adaptability, transparency,, and operational efficiency, thereby facilitating the development of a sustainable, resilient, and smart grid systems.
Modern digital ecosystems generate massive volumes of heterogeneous, high-velocity data, making it increasingly difficult for conventional security systems to detect, interpret, and respond to threats in real-time. This paper presents a unified framework that integrates Geographic Information Systems (GIS), artificial intelligence (AI), and cybersecurity principles to enhance situational awareness and automated threat response across complex environments. The proposed approach leverages spatial analytics, machine learning, computer vision, and distributed sensing to identify anomalies, predict risk patterns, and support rapid, data-driven decision-making. By combining geospatial intelligence with AI-driven inference, the system enables continuous monitoring of dynamic urban and infrastructural settings, providing a richer understanding of threat evolution. To improve scalability and resilience, the framework incorporates edge computing for processing, cloud analytics for large-scale computation, and secure data exchange mechanisms to protect sensitive information. Federated architecture further supports privacy-preserving model training across multiple agencies. Key challenges related to interoperability, governance, and ethical data handling are examined to ensure responsible deployment. Experimental evaluation demonstrates that the fusion of GIS and AI significantly strengthens cybersecurity operations, enhances predictive capabilities, and supports the development of robust smart city security infrastructures. The framework offers a scalable foundation for next-generation real-time monitoring systems.
Tirumala Bala Koteswara Rao Kamisetty· International Journal of Geo...· 0 citations
This paper proposes GRAL, which is an AI-driven framework that combines graph neural networks (GNN) for cross-asset temporal alert correlation, retrieval-augmented generation (RAG) for dynamic threat intelligence enrichment, and large language models (LLM) for semantic reasoning and verdict generation.
Deng Zhang, Juan Wang, Hanjun Gao et al.· Symmetry· 0 citations
The development of disruptive technologies such as artificial intelligence (AI), autonomous and electric vehicles, and smart grids challenges legal frameworks to remain relevant, adaptive, and coherent over time. This is evident in the transport sector, where technological innovation has the potential to fundamentally reshape transportation, logistics, and urban design, or is already doing so. Against this backdrop, the present study examines the legal ramifications of such transformations by exploring whether AI-based tools can support forward-looking legal research. Specifically, it aims to identify and map potentially complex or unclear legal situations that may arise from the deployment of disruptive technologies. This study employs two large language models (LLMs), Microsoft 365 Copilot and Claude Sonnet 4.6, to generate possible future legal scenarios for the period 2026 – 2037, using a scenario-based research design primarily situated within a Swedish and EU legal context. Several methods for engaging with the LLMs are applied, including mixed prompting, Chain-of-Thought prompting, the RACE framework, and meta-prompting. The results show that both LLMs kept well to the prompt instructions regarding output format, including the integration of future scenarios, external drivers, and potentially challenging legal themes. Additionally, while differences between the responses of the two LLMs were observed, these were primarily differences of degree rather than of kind. The overall conclusions suggest that both LLMs demonstrate potential usefulness for further research and may serve as exploratory tools in legal research as a point of departure. However, although the scenarios produced by the LLMs were grounded in contemporary legal research “hot topics”, a more thorough examination of the validity of their claims is required to support well-founded decisions regarding future research. Finally, it is recommended that researchers should inter alia continue refining their “prompt literacy” while still relying on human judgment, especially for critically evaluating the validity and robustness of LLM-assisted research.
The rapid global transition toward sustainable energy resources (SERs) is reshaping how modern power systems are observed, optimized, and controlled. While SERs have significantly advanced decarbonization, their weather dependence, variability, and inverter-dominated characteristics challenge traditional, centralized, and deterministic grid operation. At the same time, the proliferation of high-resolution data from inverters, smart meters, and sensors offers unprecedented visibility into system dynamics. Yet, it also exceeds the analytical capability of conventional model-based approaches. Artificial intelligence (AI) provides a new foundation for addressing these challenges by bridging physical laws with data-driven learning, enabling accurate state awareness, adaptive operation, and coordinated control across distributed assets. This article examines how AI transforms the management of SER-rich power systems along three critical dimensions: 1) enhancing visibility by inferring behind-the-meter (BTM) activities, assessing SER flexibility, and reconstructing system states from sparse or noisy measurements; 2) improving operation through AI-enhanced SER service provision, volt/var control (VVC), and dynamic operating envelopes (DOE) for efficiency and security; and 3) advancing control by embedding learning-based intelligence into inverter coordination, voltage and frequency regulation, and long-term dispatch. Together, these developments reveal how AI can convert the variability of SERs from an operational challenge into a source of flexibility, resilience, and intelligence, paving the way toward sustainable, adaptive, and self-optimizing power systems.
Yunyi Li, Hongyi Li, Wenlong Shi et al.· IEEE Energy Sustainability M...· 0 citations