In the realm of securing critical office environments, particularly data centers and server rooms, this research endeavors to establish a comprehensive framework for real-time monitoring, anomaly detection, and misplaced device localization. The proposed system integrates multiple modules that collaboratively ensure the integrity of device arrangement and address potential security breaches. Central to this architecture is an image processing module that employs advanced computer vision techniques, as spearheaded by the first team member. This module autonomously extracts and identifies devices within video footage, subsequently assessing their spatial distribution against a predefined arrangement. The second module, led by the second team member, focuses on network traffic analysis to uncover suspicious activities within the workstation. By meticulously scrutinizing network interactions and patterns, this module aims to detect any unauthorized access attempts or malevolent actions, such as unauthorized password attempts. Complementing the digital aspects, the third team member pioneers the hardware-based solution for misplaced devices. Leveraging technologies like WIFI and GPS, this module provides indoor and outdoor tracking capabilities to swiftly pinpoint devices that have been unintentionally displaced from their designated locations. Acting as the cohesive nexus of this multifaceted system, the fourth team member orchestrates data flow between the image processing, network analysis, and device tracking modules. This member not only ensures seamless communication but also establishes a robust database infrastructure to chronicle and manage every finding. Additionally, a user-friendly interface is developed, granting administrators full control and insight into each module's outputs and system status. By amalgamating these diverse modules, the research aims to furnish office environments with a holistic safeguarding mechanism that addresses both physical arrangement integrity and cybersecurity concerns in a real-time SOC environment and predicts future attacks using a machine learning approach. This comprehensive approach transcends conventional security paradigms, forging a new frontier in the protection of critical spaces where data integrity and operational continuity are paramount.