Conceptual Framework for AI-Augmented Threat Detection in Institutional Networks Using Layered Data Aggregation and Pattern Recognition

As cyber threats grow in complexity and frequency, institutions face increasing challenges in safeguarding sensitive data and ensuring the integrity of their network infrastructures. Traditional security mechanisms often struggle to keep pace with rapidly evolving attack vectors. This paper presents a conceptual framework for AI-augmented threat detection in institutional networks, leveraging layered data aggregation and pattern recognition techniques to enhance the accuracy and speed of threat identification. The proposed model integrates artificial intelligence (AI) with multi-layered network monitoring systems to collect, analyze, and interpret real-time data from various sources including firewalls, endpoints, and intrusion detection systems (IDS). The framework is designed around three core components: data aggregation, feature extraction, and intelligent pattern recognition. Data aggregation collects and consolidates log files and event streams from disparate network components into a centralized repository. Feature extraction applies preprocessing algorithms to identify critical attributes such as access anomalies, packet irregularities, and login behavior deviations. The pattern recognition layer utilizes machine learning and deep learning models to detect deviations from established behavioral baselines and predict potential threats, including zeroday attacks and insider threats. A key innovation of this framework is the use of contextual layering, where data is categorized and analyzed at different abstraction levels—user, application, and system—allowing for a more nuanced understanding of threat dynamics. This layered approach supports adaptive learning, enabling the AI models to refine detection capabilities over time through continuous feedback. Furthermore, the system can generate alerts and automated responses in real time, improving the institution’s resilience against cyber intrusions. The paper discusses implementation considerations, challenges in training AI models with diverse datasets, and the importance of explainable AI (XAI) for transparency in threat detection decisions. By offering a scalable and intelligent approach, the framework aims to strengthen cybersecurity in academic and enterprise environments. Future work will explore integration with existing Security Information and Event Management (SIEM) systems for enhanced operational value.