Successfully Managing Cybersecurity Projects in the Age of AI



By Yogi Schulz, BComm

Calgary, Alberta, Canada

Managing cybersecurity projects in the age of AI has become more demanding. The stakes are higher. The cost to recover from a successful cyberattack is typically millions of dollars. The damage to reputation is significant but difficult to estimate.

In the age of large language models (LLMs) and generative AI, organizations must confront the security implications associated with these powerful technologies. Widespread attacker adoption of these technologies requires heightened responses to:

    • Raise cybersecurity defenses.
    • Maintain data privacy.
    • Prevent data breaches and ransomware attacks.
    • Reduce risks posed by shadow AI.

On a more positive note, adding LLMs and AI features to cybersecurity defenses can strengthen an organization’s defenses against cybercriminals and keep its data safe.

Here’s a list of topics cybersecurity project managers should address with their teams in their project management plan to ensure a successful cybersecurity project.

Project management best practices apply

Cybersecurity projects, with or without an LLM and AI, are not different from other IT projects. Sometimes, project teams convince themselves that cybersecurity projects are so profoundly technical that specialized individuals should be let loose to deliver them and that project management best practices don’t apply.

Don’t fall into this trap. Some cybersecurity deliverables are deeply technical. However, that’s a reason to emphasize project management best practices, not abandon them.

Data scientists require management

Data scientists will be valued members of the cybersecurity project team. However, as their name states, these individuals are scientists, not IT professionals. Their culture, education, work practices, organization expectations, attitudes, and reward systems differ from those of IT professionals. These differences can lead to conflicts and performance frustrations.

Project managers can mitigate these risks by coaching data scientists to:

    • Focus on the cybersecurity deliverables and not be distracted by the many exciting insights they discover in the data.
    • Restrict their work to the project scope and not explore the many enticing ideas that emerge during design discussions.
    • Build robust software and avoid too many exploratory prototypes.
    • Raise cybersecurity defenses and abandon the urge to write an academic paper about their project learnings.


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How to cite this article: Schulz, Y. (2023).  Successfully Managing Cybersecurity Projects in the Age of AI, PM World Journal, Vol. XII, Issue XII, December. Available online at https://pmworldlibrary.net/wp-content/uploads/2023/12/pmwj136-Dec2023-Schulz-Successfully-Managing-Cybersecurity-Projects-in-Age-of-AI.pdf

About the Author

Yogi Schulz

Calgary, Alberta, Canada


Yogi Schulz has over 40 years of Information Technology experience in various industries. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy and systems project management.

Mr. Schulz regularly speaks to industry groups and writes a regular column for IT World Canada and for Engineering.com. He has written for Microsoft.com and the Calgary Herald. His writing focuses on project management and IT developments of interest to management. Mr. Schulz served as a member of the Board of Directors of the PPDM Association for twenty years until 2015. Learn more at https://www.corvelle.com/. He can be contacted at yogischulz@corvelle.com

His new book, co-authored by Jocelyn Schulz Lapointe, is “A Project Sponsor’s Warp-Speed Guide: Improving Project Performance.”