An Ai-Driven Framework for Enhancing Security and Threat Detection in Academic Settings Dr. Ayesha Karim

Abstract

Academic environments, once perceived as safe havens, have increasingly become vulnerable to various security threats, including acts of violence that necessitate advanced protective measures. Traditional security protocols often prove insufficient in proactively identifying and mitigating such risks. This article proposes a comprehensive architectural framework leveraging Artificial Intelligence (AI) to enhance threat detection and security within educational institutions. The framework integrates intelligent surveillance, real-time data analytics, and advanced machine learning models, including object detection algorithms and behavioral analytics, to identify potential threats such as weapons or anomalous behaviors. The methodology details the system's components, data sources, and the AI techniques employed, while the results present a layered architecture designed for continuous monitoring and rapid response. The discussion evaluates the framework's implications, highlighting its potential for proactive threat mitigation, improved decision-making, and streamlined security operations, alongside acknowledging crucial challenges such as data privacy and the ethical deployment of AI. Ultimately, this AI-driven approach aims to foster safer learning environments through intelligent, data-driven security solutions.