Balancing Security and Information Management in the Digital Workplace

Abstract

As the digital workplace becomes more prevalent, organizations are faced with the challenge of balancing security and information management. On one hand, there is a need to protect sensitive data and prevent cyberattacks, while on the other hand, organizations must enable employees to collaborate and share information effectively. Machine learning (ML) is a promising technology that can help organizations address this challenge. By analyzing data patterns and identifying potential security threats, ML algorithms can enhance security measures and mitigate risks. At the same time, ML can also facilitate information management by automating routine tasks and improving the accuracy of data analysis. In this paper, we explore the role of ML in balancing security and information management in the digital workplace. We propose a hybrid ML model that integrates autoencoder and convolutional subnetworks in unified architecture to help capturing and security threats in the digital workplace, without compromising the information management tasks. We also present a case study of a real-world implementation of ML in a digital workplace setting, highlighting the benefits and limitations of this approach. Our findings suggest that ML can be a valuable tool for achieving a balance between security and information management in the digital workplace, but its successful implementation requires careful consideration of organizational context and stakeholder needs.