Neutralization Theory-Based Model for the alleviation of Shadow IT-Induced security threats

Normally, employees bypass security measures to meet productivity goals, inadvertently creating significant cybersecurity risks. This is because they are increasingly reliant on digital tools and cloud-based workflows. Shadow IT is categorized as either a software or hardware utilized by either a department or an individual in an organization without the knowledge of the central IT unit. Institutions deploy DLP, NIDS, EDR, Zero Trust, and CASBs to monitor unauthorized data/device activities, complemented by Models like ISO 27001 and COBIT for governance. However, these struggle with rapid shadow IT adoption due to user resistance, high costs, and inability to fully cover personal devices or decentralized workflows. Regulatory mandates enforce compliance but face gaps from bureaucratic delays and evolving threats. Agile governance integrates grassroots tools into innovation pipelines and emerging risks like Generative Artificial Intelligence data leaks and quantum-vulnerable cryptography require specialized solutions. Yet, resource constraints and dynamic threats persist, necessitating real-time monitoring and behavioral incentives. This study aimed to develop a Neutralization Theory-Based Model for mitigation of Shadow IT-Induced vulnerabilities. The entire population included 150 staff from various departments within ICT Authority, Kenya. The sampling was done using Yamane’s formula, yielding 110 respondents.  The data was collected using an online questionnaire on google forms, whose link was shared to the 110 respondents. Cronbach’s Alpha was deployed for assessment of reliability of the research tool. On the other hand, validity was tested by piloting within the security department. The obtained data was first coded on the five Likert scale prior to being fed to the SPSS software. The analysis included the computation of frequencies, percentages, multilinear regression analysis of variance and model fit tests. The results indicated that among the nine factors studied (Authorization, Role-based Access, Filtering, Logging and Auditing, Security Policies, Education and Training, Zero Trust Architecture, AI Governance, Crypto-Agility), seven had a significant positive impact on reducing Shadow IT-induced vulnerabilities. The most influential factor is AI Governance (with the highest beta of 0.312), followed by Crypto-Agility beta=0.205) and Authorization (beta=0.195). Two factors (Filtering and Security Policies) did not show a statistically significant impact in this model leading to their automatic elimination from the attuned model.  AI Governance and Crypto-Agility demonstrated the strongest direct impact on reducing vulnerabilities. This directly addresses critical risks: GenAI data leakage and future quantum attacks on deprecated cryptography in shadow code. Centralized governance prevents sensitive data exposure via unauthorized AI tools, while crypto-agility mitigates long-term supply chain risks in unsanctioned scripts. Subsequently we should enforce dynamic Authorization controls integrated with Zero Trust Architecture at Policy Enforcement Points. Apply micro-segmentation specifically to isolate shadow IoT/legacy systems and enforce Role-Based Access Controls based on continuous risk assessment, not static roles. Utilize CASB/SSPM tools for real-time SaaS authorization checks.