Introduction: The rapid evolution of artificial intelligence from static large language models to autonomous, agentic AI systems has introduced capabilities such as persistent memory, tool-augmented reasoning, and multi-agent collaboration. While these advancements significantly enhance real-world applicability, they also create a new and underexplored class of privacy risks, including unintended retention, propagation, and amplification of sensitive information across tasks, users, and execution cycles. Existing research predominantly focuses on stateless or single-inference models, leaving the privacy implications of agentic systems insufficiently understood. Methods: This study presents a comprehensive architectural analysis of data leakage in agentic AI systems. The proposed framework models the end-to-end agent workflow and systematically examines how sensitive information can traverse key components, including persistent memory modules, planning and reasoning processes, tool invocation layers, inter-agent communication channels, and feedback-driven autonomy loops. Based on this architecture, a structured taxonomy of leakage pathways is developed and mapped to realistic threat models and attack vectors observed in practical deployments. Results: The analysis identifies multiple leakage pathways unique to agentic AI systems, demonstrating how data can persist, propagate, and be unintentionally exposed across system components and operational cycles. The findings reveal that these leakage mechanisms are more complex and pervasive than those observed in traditional large language model settings, particularly due to the integration of memory, tools, and multi-agent interactions. Discussion: The study highlights the limitations of existing LLM-centric privacy and security defenses when applied to autonomous agentic systems. It emphasizes the need for lifecycle-aware, component-level mitigation strategies that address privacy risks across the entire agent workflow. The proposed architectural perspective provides a foundation for designing privacy-by-design agentic AI systems and supports safer deployment in sensitive and regulated domains.

The incident, intensity, detection and prediction strategies of forest fires are increasing day by day which is having a significant impact on infrastructure and the global economy around the world, and therefore affecting the Sustainable Development Goals. The aim of this research study is to detect and predict forest fires based on multi-temporal images captured by satellites. It was observed that the majority of fires occur during the pre-monsoon period, especially during the month of March. Out of all areas surveyed, the current and anticipated high-risk areas were marked in the regions with the largest concentration of protected zones. It is vital to control the underground bio-mass burning in the forests at lower elevations to minimize the chances of fire in the peak season. The study underscores the necessity for a well-defined framework, to predict, identify, and prioritize fire-prone zones. Additionally, a deep learning-based hybrid approach using change detection, Long Short-Term Memory (LSTM) and attention mechanism on pre-processed satellite images is proposed for the early detection of forest fires. Change detection is used for the comparison of multiple raster datasets, typically collected for one area at different times, to determine the type, magnitude, and location of change. It is used to track forest fires, access forest wildfire impacts, detect burned areas, and for reducing damage and cost. Long Short-Term Memory Networks or LSTM in deep learning, is a sequential neural network that allows information to persist. It is used to analyse temporal dependencies in the change-detected regions. The attention mechanism is a technique used in machine learning and natural language processing to increase model accuracy by focusing on relevant data by assigning higher weights to important parameters, which makes the model better fit the current data. The trained model demonstrates high accuracy, surpassing traditional methods, and aids in early warning and decision-making for fire management authorities. This combination of remote sensing and deep learning offers a robust system for accurate forest fire detection and prediction, essential for mitigating the impact of forest fires on ecosystems and communities.

Environmental monitoring in the city demands the solution which will enable to work with the huge volumes of data of different kinds and utilize the resources in the most effective way. The presented work proposes Big Data Analytics Platform to address the urban problems with the help of the improved algorithms. Preprocessing Data Aggregation and Preprocessing Algorithms Preprocessing Data Aggregation and Preprocessing Algorithms obtain and standardize different data to ensure that the data result of the analysis is standardized. The temporal and spatial relationships are determined using the Spatial Temporal Data Analysis Algorithms before explaining the urban dynamics. The learning based on the Machine Learning-Based Predictive Modelling Algorithms and offer the correct environmental conditions and trends within the urban regions to make timely decisions. Multi-Objective Optimization Algorithms are employed when there are resources to be controlled through the fulfillment of groups of competing goals including energy use and emission issues. These two algorithms provide the Real-Time Data Visualization and Reporting and expose the stakeholders to the relevant information in the format that will be understood by them. In general, one can refer to these algorithms as a strong foundation of the overall monitoring and management of sustainable environments in cities, and thus creation of sustainable cities. The fact that the control of emissions, traffic simulation, and waste disposal sustainability concepts are among the areas of discussion.

The merging of Artificial Intelligence (AI) and Internet of Things (IoT) forms a revolutionary partnership that is reshaping industries and influencing our global landscape. To begin with, it delves into the intricate dynamics between these two innovations, unveiling their mutually beneficial connection. With capabilities ranging from data collection and real-time decision making to predictive analysis and energy conservation, AI processes the extensive data generated by IOT devices, transforming it into valuable insights. This collaboration extends to healthcare, security, and other domains, as AI-empowered IoT devices facilitate remote patient monitoring and proactive threat identification. Nevertheless, ethical and privacy considerations arise as these technologies remodel our existence. Moving onto the essential domains where AI and IoT intersect, fostering ingenuity throughout industries. Customized AI-generated insights enhance smart home automation, industrial IoT, and healthcare, whereas transportation, agriculture, and energy administration profit from refined resolutions. This collaboration further reaches into realms like entrainment and education, crafting immersive encounters and tailored learning trajectories. Nonetheless, assuming data privacy and upholding ethical concerns proves to be a hurdle. The fusion of Ai and IoT presents the potential to mold a more intelligent and effective world, necessitating a meticulous equilibrium between pioneering advancement and ethical duty.