This work-in-progress innovative practice paper describes how integrating data privacy education into a computer science/technology or engineering curricula will prepare students to adopt a privacy by design (PbD) approach when designing, building, and maintaining data driven systems. Artificial Intelligence (AI) is at the forefront of technological advancements and is revolutionizing everyday lives through aspects such as smart healthcare, self-driving cars, voice activation, and automated decision making. With exponential AI market growth, the need to better train AI models to behave in ways that emulate or complement human behavior necessitates large data sets often containing personal health, biometric, location and other personally identifiable information (PII). The data used for training AI applications has great transformative potential to improve our lives, but it offers a significant privacy risk to individuals. Using data without consent is a violation of global data protection laws and could result in financial and/or criminal penalties. The need to educate students about privacy is clear, but the literature is vague in addressing how to integrate privacy education into engineering, technology, or computer science programs. Given the complex and ever-changing privacy requirements, this paper discusses a roadmap for how data privacy can be integrated into AI, computer science and engineering curricula to help students comply with laws and ethical challenges when developing AI applications.

This research-to-practice full paper addresses the important need for relevant and comprehensive datasets to advance cybersecurity research by proposing methods for curating open datasets and generating synthetic datasets. Cybersecurity research is a rapidly evolving scientific field, making robust datasets crucial for empirical analysis. Unfortunately, current doctoral research is hindered by the scarcity, limited accessibility, and outdated or irrelevant nature of existing open-source datasets. This paper tackles these challenges by focusing on two main initiatives: (1) curating a pilot collection of open datasets aligned with the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework, and (2) using Generative Adversarial Networks (GANs) to generate synthetic datasets. Our research highlights the obstacles faced by doctoral students due to fragmented, outdated data and underscores the importance of accessible datasets for rigorous scientific inquiry. We also demonstrate how synthetic data can ease privacy concerns while still offering researchers realistic data. By incorporating these approaches into doctoral curricula, we aim to equip future cybersecurity researchers with the skills resources for impactful research. The authors will continue to expand their dataset curation efforts and study how discoverable, high-quality datasets can influence doctoral research, particularly empirical studies and their outcomes.

The origins of the cybersecurity field were highly practice-oriented, often from an individual Community of Practice (CoP), such as the military. In many cases, emergent cybersecurity academic programs relied on these practitioners as adjunct faculty to transfer and impart knowledge to students. However, this approach proved insufficient to develop an overall academic discipline of cybersecurity with effective educational programs to meet the growing and changing need for cybersecurity professionals. To address this gap, the authors designed and developed a Doctor of Science (D.Sc.) program focusing on applied research with knowledge conversion from multiple individual CoPs and a strong workforce academia interchange. The program leverages Nonaka's knowledge conversion model and Wenger's communities of practice (CoP) theory to catalyze innovation and cultivate an integrated community of cybersecurity practices. Doctoral students conduct in-depth research in a specific cybersecurity area of their choice, developing cybersecurity products, frameworks, tools, and solutions for their CoP and often for a wider audience. Faculty gain knowledge of advancements in the current practices of cybersecurity through integrating individual CoPs that they can then use in developing their cybersecurity academic programs at the undergraduate and graduate levels. The chosen research method for this work-in-progress paper is a qualitative thematic analysis through open-ended questionnaires. The research sample will consist of doctoral students currently in the program or recently graduated and faculty teaching the courses in the D.Sc. program. The study aims to explore the knowledge interchange and learning experiences within the CoP regarding research scholarship, dissertation development, and cybersecurity innovation. The insights and lessons learned from this program can serve as a model for other academic institutions seeking to enhance the practicality and innovation within their cybersecurity programs while contributing to developing a much-needed, more robust, and secure cyber ecosystem.

Organizations increasingly use artificial intelligence (AI) technologies in their screening and recruiting process. However, AI-enabled recruiting and talent management tools have also introduced risks of unfair bias that may compromise workforce diversity. This conceptual paper frames an ethical discussion regarding gender equity in AI-enabled workforce decision applications. The authors examined several real-world cases in which AI was used in talent acquisition. The ensuing question is whether the use of AI in the hiring process can be turned into an advantage to improve gender equity. To address the question, a multi-faceted trustworthy AI framework was reviewed and applied to the workforce decision context. A list of implementation guidelines is proposed to mitigate bias and increase diversity in the IT workforce. The authors aim to stimulate further discussions and investigation on this complex topic and to call for action to develop educational programs or awareness campaigns on trustworthy AI, so improving gender equity in technology hiring.

This Innovative Practice Full Paper presents a case study on constructing and implementing a connected program in the Doctor of Science (D.Sc.) in Cybersecurity at Marymount University. We adapted a connected curriculum framework to this professional doctorate program based on two overarching pillars: andragogy theory and a multidisciplinary perspective. We use an andragogical instructional methodology that follows a learner-centered teaching philosophy to promote professional and adult learner engagement. Furthermore, the D.Sc. program takes a multidisciplinary approach to bridge and integrate domain-specific silos such as technology and its evolution, risk management, legal compliance, human factors, machine learning, business impact, and more. This integration helps enhance students' knowledge in multiple disciplinary specializations, focusing on problem-solving skills across the various domains important in cybersecurity. Building on these two pillars, our connected program model integrates five dimensions of connectivity to connect 1) academic learning and research with the workplace, 2) research activities and research-related curriculum over time, 3) various disciplines related to real-world cybersecurity challenges, 4) students with each other, across phases and with alumni, and 5) students with research and researchers across multiple domains. This paper's primary contribution is to demonstrate an innovative andrological and connected approach to tackle the ever-changing cybersecurity threats by cultivating the next generation of cybersecurity leaders with both advanced technical and refined management skills. We further showcase the potential of externalizing this framework in other settings and discuss future research work.

Cybersecurity concerns are in the news on a daily basis, from large data breaches to attacks on power stations. Equally widely acknowledged in the press is the shortage of cybersecurity professionals to protect our digital world, particularly the lack of females in the profession (current estimates are around eleven percent). This paper looks at the concept of “cybersecurity for all” emphasizing personal responsibility for security and privacy as well as the development of a more inclusive cybersecurity workforce. The paper examines strategies for teaching cybersecurity at all ages (from K through Gray, including incorporating the scientific method. It also discusses techniques for making the cybersecurity career more attractive and accessible to females, with a view to recruiting and retaining more talented females into this growing profession.