AI-enabled Compliance and Data Privacy Governance for SMEs

Thesis Abstract

The rapid digitization of small and medium-sized enterprises (SMEs) has heightened exposure to data privacy risks and regulatory complexity, yet SMEs often lack affordable, scalable governance mechanisms aligned with evolving AI-enabled processes. This study develops and empirically evaluates a technology-driven compliance and data privacy governance framework for SMEs, integrating AI-assisted risk assessment, automated policy enforcement, and continuous monitoring to bridge the gap between regulatory requirements and SME operational realities. The aim is to design, implement, and assess a deployable model that enhances data privacy posture while preserving business agility. Specific objectives are (1) to identify key regulatory obligations affecting SME data practices in the context of AI-enabled operations; (2) to construct an AI-assisted governance architecture comprising risk scoring, policy inference, and automated controls; (3) to evaluate the framework’s effectiveness in reducing privacy incidents and improve compliance readiness; (4) to examine organizational determinants (digital maturity, governance culture, and leadership support) that modulate framework adoption; and (5) to articulate implementation guidelines and a scalable roadmap for SMEs. A mixed-methods approach is employed. The study adopts a multi-stage research design combining a Delphi-informed requirements analysis, a quasi-experimental pilot, and a longitudinal survey. The population comprises 600 SMEs across manufacturing, retail, and professional services sectors within a three-country region, with a purposive sample of 200 SMEs for in-depth data collection and a nested sample of 12 SME units for the pilot deployment. Data collection instruments include (i) a structured compliance and data privacy survey administered to 200 SME managers to gauge baseline posture and perceived barriers; (ii) semi-structured interviews with 40 compliance officers and IT leaders to capture contextual insights; (iii) system-generated logs and audit trails from the pilot to measure objective outcomes; and (iv) a post-implementation evaluation using a 6-month follow-up survey. Validity and reliability are ensured through instrument triangulation, pilot testing, and Cronbach’s alpha reliability checks (targeting ? ? 0.75). Data analysis employs descriptive statistics, regression analysis to identify determinants of compliance improvements, and time-series analysis to evaluate incident trends; thematic analysis is applied to interview transcripts to extract governance-enabling factors; and propensity score matching is used to control for pre-existing differences between pilot and non-pilot SMEs. The analytical framework is anchored in the Technology-Organization-Environment (TOE) model and the Information Privacy Theory, complemented by the Institutional Theory lens to explain adoption dynamics. Expected findings indicate that the AI-enabled governance framework reduces data privacy incidents by an estimated 28% within six months of deployment, improves regulatory readiness scores by 35%, and shortens incident response time by 40%. It is anticipated that higher digital maturity, supportive governance culture, and proactive leadership significantly strengthen adoption outcomes, while smaller firms may require enhanced external support and simpler user interfaces. The study also anticipates identifying critical success factors such as transparent explainability of AI-driven decisions, robust data lineage and access controls, and continuous monitoring capabilities that adapt to regulatory updates. The contribution to knowledge includes (i) a novel, scalable AI-enabled governance architecture tailored to SME constraints; (ii) empirical evidence on the effectiveness of automated policy inference and continuous monitoring in reducing privacy risk; (iii) a theoretically grounded understanding of adoption determinants in the SME sector; and (iv) pragmatic implementation guidelines, including tool configurations, governance metrics, and a phased rollout blueprint adaptable to diverse regulatory landscapes. The study concludes that AI-enabled compliance and data privacy governance can substantially elevate SME privacy resilience without compromising operational efficiency when designed with SME-specific constraints and continuous stakeholder engagement. Recommendations emphasize (a) investment in modular, cost-accessible AI governance tooling; (b) development of sector-specific policy templates and regulator-aligned data maps; (c) capacity-building programs to improve digital literacy among SME leaders; and (d) policy considerations for encouraging ecosystem-based support and standardized interoperability to facilitate broader adoption.