Foreword
Preface
Acknowledgments
Contents
List of Figures
List of Tables
Chapter 1: Summary Introduction
1.1 Summary Overview
1.2 Motivations and Audience
1.3 Research Questions and Management Problems Addressed
1.4 Research Process and Methods
1.5 Theoretical Contributions
1.6 Contributions to Management Practice
Chapter 2: Phase I: CSDS as an Emerging Profession—Diagnostic Literature Analysis
2.1 Research Objectives
2.2 CSDS in a Management Research Context
2.2.1 Working Definition
2.2.2 Expanding Scope, Scale, and Risk
2.2.3 Adversarial Advantages
2.2.4 Managerial Responses to Growing Challenges
2.2.5 Market Responses and Distortions
2.2.6 Management of Information Systems (MIS) Context
2.2.7 Professional Maturity Model
2.3 Cybersecurity as a Profession
2.3.1 Origins and Definitions
2.3.2 Managing and Assuring
2.3.3 Academic Programs and Research
2.3.4 Professional Organizations and Certifications
2.3.5 Frameworks
2.3.6 Professional Status
2.4 Data Science as a Profession
2.4.1 Origins and Definitions
2.4.2 Organizational Job Role
2.4.3 Academic Programs and Research
2.4.4 Professional Organizations and Certifications
2.4.5 Frameworks
2.4.6 Professional Status
2.5 CSDS as an Emerging Hybrid Profession
2.5.1 Origins and Definitions
2.5.2 The Data-Driven Paradigm in Cybersecurity
2.5.3 Academic Programs and Research
2.5.4 Professional Organizations and Certifications
2.5.5 Frameworks
2.5.6 Professionalization Status
2.6 Literature Analysis Outcomes
2.6.1 Result 1: CSDS Comparative Professional Maturity Gaps
2.6.2 Result 2: CSDS Functional Demand Model
2.6.3 Result 3: CSDS Literature Corpus
2.6.4 Result 4: CSDS Literature Gap Analysis
2.6.5 Result 5: CSDS Sensitizing Concepts
2.6.6 Result 6: Methodological Concepts
2.7 Summary of Literature Analysis Results
2.7.1 Research Relevance
2.7.2 Meta-theoretical Implications
2.7.3 Managerial Relevance
2.7.4 Research Questions Addressed
Chapter 3: Phase II: CSDS Practitioners—Diagnostic Opinion Research and Gap Analysis
3.1 Research Objectives
3.2 Qualitative Component: Diagnostic Interview Research
3.2.1 Interview Research Preparation and Planning
3.2.1.1 Establishing Context
3.2.1.2 Qualitative Research Overview
3.2.1.3 Interview Research Process
3.2.1.4 Methodological Underpinnings
3.2.1.5 Sensitizing Concepts
3.2.1.6 Interview Questions and Format
3.2.1.7 Interviewee Selection
3.2.1.8 Interviewee Demographics
3.2.1.9 Logistics and Responses
3.2.2 Initial Coding and Refinement
3.2.2.1 Open Coding and Refinement Process
3.2.2.2 Open Codes and Categories
3.2.2.3 Memoing Insights
3.2.2.4 Final Ordered Codebook
3.2.3 Structured Coding and Results
3.2.3.1 Selective Axial Coding Process
3.2.3.2 Challenge Codes and Results
3.2.3.3 Best Practice Codes and Results
3.2.3.4 Adjacent Domains Codes and Results
3.2.3.5 Adversarial Trends Codes and Results
3.2.3.6 Demographic and Professional Experience Observations
3.2.3.7 Thematic Insights via Text Analytics
3.2.3.8 Summary of Mixed Method Qualitative Insights
3.3 Quantitative Component: Diagnostic Gap Analysis
3.3.1 Overview of Analytical Methods
3.3.2 Methodological Underpinnings
3.3.3 Logistic Fitting of Challenges with Best Practices
3.3.4 Exploratory Factor Analysis and Interpretations
3.3.5 Factor Substantiation
3.3.6 Factor-to-Factor Fitting
3.3.7 Gap-Prescription Extrapolation
3.4 Summary of Extrapolated Insights
3.4.1 Mixed Method Diagnostic Gap Analysis
3.4.2 Research Relevance
3.4.3 Managerial Relevance
3.4.4 Research Questions Addressed
Chapter 4: Phase III: CSDS Gap-Prescriptions—Design Science Problem-Solving
4.1 Research Objectives
4.2 Framing CSDS Design Prescriptions
4.2.1 CSDS Gaps in Context
4.2.2 Cybersecurity Challenges Redux
4.2.3 Data Science Challenges Redux
4.2.4 Towards Design Science Prescriptions
4.3 Design Prescription I: Data Management
4.3.1 Problem-Solving Requirements
4.3.2 Data Preparation as a Process
4.3.3 Specifying Data Structures
4.3.4 Exploratory Data Analysis (EDA)
4.3.5 Feature Engineering
4.3.6 Analytics Process Models
4.3.7 Summary of Design Prescription I: Data Management
4.4 Design Prescription II: Scientific Processes
4.4.1 Problem-Solving Requirements
4.4.2 Operationalizing Scientific Discovery
4.4.3 Extrapolating Experimental Evidence
4.4.4 Integrating Prediction and Explanation
4.4.5 Model Context and Validation
4.4.6 Towards Cybersecurity Theory
4.4.7 Summary of Design Prescription II: Scientific Processes
4.5 Design Prescription III: Cross-Domain Collaboration
4.5.1 Problem-Solving Requirements
4.5.2 Collaborative Science in the Enterprise
4.5.3 Aligning Organizational Incentives
4.5.4 CSDS Curricula for Cross-Training
4.5.5 Summary of Design Prescription III: Cross-Domain Collaboration
4.6 Summary of Design Prescriptions
4.6.1 Overview of Prescribed Designs
4.6.2 Research Relevance
4.6.3 Managerial Relevance
4.6.4 Research Questions Addressed
Chapter 5: Research Conclusions and Discussion
5.1 Summary of Research Motivations
5.2 Summary of Research Objectives and Questions Addressed
5.2.1 Phase 1 (Section 1)
5.2.2 Phase 2 (Section 2)
5.2.3 Phase 3 (Section 3)
5.3 Summary of Research Process, Methods, and Outcomes
5.3.1 Phase I: Diagnostic Background (Literature) Analysis—CSDS as an Emerging Profession
5.3.2 Phase II: Diagnostic Opinion Research and Gap Analysis—Interviews and Mixed Method Analysis
5.3.3 Phase III: Design Science Problem-Solving—CSDS Gap-Prescription Designs
5.4 Academic, Theoretical, and Methodological Contributions
5.5 Limitations of Research
5.6 Future Research Foundations
Chapter 6: Managerial Recommendations
6.1 Management Problems
6.2 Management Problems Addressed
6.3 Summary of Stakeholders Served
6.4 Guidance for CSDS and Related Practitioners
6.5 Guidance for Managers and Security Stewards
6.6 Guidance for Firms and Service Providers
6.7 Guidance for Nation-States and Regulators
References