AI-Driven Compliance Auditing for Data Protection Regulations

Thesis Abstract

The rapid expansion of digital ecosystems and the globalization of data flows have intensified regulatory scrutiny around data protection, compelling organizations to adopt automated, audit-ready systems to demonstrate compliance with frameworks such as the GDPR, CCPA, and emerging regional regimes. This study addresses the gap between existing manual or semi-automated compliance practices and the need for scalable, continuous assurance in complex ICT environments. The aim is to design, implement, and evaluate an AI-driven compliance auditing framework that automatically assesses data protection controls, detects policy deviations, and generates auditable evidence across heterogeneous data landscapes. Specific objectives include (1) to model data protection requirements as machine-interpretable rules aligned with key concepts from the GDPR and regional equivalents; (2) to develop an integrated AI-audit pipeline comprising data lineage tracing, access control verification, and data minimization checks; (3) to validate the framework in a real-world organizational setting using iterative pilot tests; (4) to evaluate the framework’s effectiveness, efficiency, and explainability against manual audits; and (5) to propose a governance and risk management paradigm for AI-driven compliance that integrates with existing information security management systems. The methodology combines a design science research approach with a mixed-methods evaluation. The study will be conducted in a mid-to-large multinational organization with active GDPR and local data protection obligations, selecting a population of information governance staff, data stewards, and IT security professionals. A purposive sample of 60 participants will be invited to participate in usability and acceptability studies, alongside a larger set of 20 business units for pilot deployment. The AI-audit framework will be developed as a modular pipeline incorporating (a) a knowledge graph representing data processing activities and compliance controls; (b) natural language processing for policy extraction and readability of consent notices; (c) computer vision and anomaly detection for monitoring data access patterns; and (d) explainable AI components providing justification traces for audit findings. Data collection will use (i) archival organizational data (policies, data inventories, access logs), (ii) structured interviews and surveys with stakeholders, and (iii) artifact analysis from pilot audits. Instrument validity and reliability will be established through pilot testing, triangulation of policy documents, and inter-rater reliability for human judgments in audit case reviews. Data analysis will proceed through multiple strands. Quantitative analysis will apply regression techniques and time-series analysis to measure improvements in audit efficiency (cycle time reduction, detection rates of non-compliance events, and false-positive rates) and will utilize ANOVA to compare performance across organizational units. Thematic analysis will be employed on qualitative interview data to uncover perceived barriers to adoption, trust in AI-driven outputs, and governance concerns. A validation study will compare AI-generated audit reports against independent manual audits to assess accuracy, completeness, and explainability. The study will situate findings within information systems theory, privacy governance frameworks, and the Fair Information Practice Principles, drawing on the technology acceptance model to interpret user adoption dynamics, and leveraging the theory of regulatory compliance as a socio-technical phenomenon. Expected findings include (i) a demonstrable reduction in audit cycle time by 40–60% and improved detection of data protection breaches through automated policy violation alerts; (ii) high alignment between AI-audit outputs and manual audit conclusions, with explainability mechanisms yielding actionable evidence for regulators; (iii) robust governance controls that mitigate risks associated with AI opacity and data minimization; and (iv) positive user reception with identified factors enhancing or impeding adoption. The study contributes to knowledge by operationalizing AI-enabled continuous compliance assurance in data protection regimes, providing a reusable architecture, validation evidence from a real-world setting, and a governance framework for responsible AI in compliance auditing. It offers practical guidance on integrating AI-driven audits with existing privacy programs, informs policy debates on audit standardization, and identifies best practices for ensuring transparency, accountability, and reliability in automated compliance activities. The main conclusion anticipates that AI-driven compliance auditing can substantially enhance regulatory posture while preserving governance, ethics, and trust, provided that explainability, human oversight, and robust data governance are embedded as core design principles. Recommendations include adopting modular, auditable AI architectures; establishing independent audit trails and regulatory reporting channels; and continuing longitudinal studies to assess long-term impact on regulatory compliance maturity and organizational risk.