Bayer Cybersecurity - Strategic Analysis and Recommendation

Course: Managing IT for Business Value (BUS 468)

Executive Summary

This report examines the future of cybersecurity at Bayer, with a focus on their systems and data in healthcare. We evaluate the critical issues in protecting these valuable digital assets from cyber attacks and provide recommendations based on the latest innovations in accurate predictive intelligence solutions that proactively use machine learning techniques to mitigate threats.

Given the lack of publicly available information on Bayer's current cybersecurity systems and practices, core assumptions were made and clearly outlined to facilitate effective analysis and recommendation. Notably, it is assumed that Bayer has a solely reactive approach to protecting itself against cyber attacks. This remains the industry standard for organizations of this size and industry. Our research is grounded in the fact that the practice of reacting to threats is rapidly becoming insufficient protection for the increasingly complex cloud computing systems that Bayer relies on to store and process its vast amounts of sensitive and valuable data. Additionally, we focus on malware as the most prevalent and damaging cyber attack.

Our recommendation is that Bayer implement a Predictive Intelligence Technology (PIT) solution which would be added as a proactive layer of security, ahead of the existing and continually improving reactive protection. This would accurately predict and help cybersecurity professionals prevent incoming attacks, reducing and often eliminating the load on reactive security systems and procedures. These outcomes would be achieved through a robust Predictive Intelligence Model, which applies static analysis, dynamic analysis, and image processing techniques for malware detections, which perform at a 96% accuracy level in real-time based on the latest research and testing.

The implementation roadmap for the proposed solution would depend on a phased approach using iterative prototyping, testing, and data training before being fully integrated into Bayer's systems. Details on costs and performance requirements would directly depend on the details of Bayer's existing systems, to which we do not have access. Thus, our recommendations established a framework for the most important considerations and risk mitigation strategies based on the available information and outlined assumptions. The system's overall success would depend on many technically informed, context-specific decisions that we believe Bayer is well equipped to undertake.

The future of cybersecurity is more important than ever. Our research and analysis demonstrate that recovering from cyber attacks cost companies millions to recover from and that Bayer's reactive approaches to securing their valuable healthcare systems and data are not enough for the exponential digitization of the future. With a focus on effective and timely risk exposure reduction, this report proposes a dynamic and reliable new digital solution hoping that Bayer might implement the necessary advances for it to thrive in its endeavours of aiding the global population with innovations in healthcare. By implementing predictive intelligence solutions in addition to the cutting edge of reactive techniques, risk exposure would effectively be reduced, cybersecurity processes would be more efficient, and customer trust will not only be maintained but enhanced.