Authors: Malini R. De Silva
Abstract: The increasing complexity of regulatory requirements and the growing volume of enterprise data have made IT compliance auditing a challenging process for organizations. Traditional audit methods, reliant on static rule sets and manual inspection, often struggle to maintain accuracy and timeliness in dynamic digital environments. Cognitive analytics, integrating artificial intelligence, machine learning, and natural language processing, has emerged as a transformative approach to enhance audit precision and adaptability. By simulating human reasoning and contextual understanding, cognitive systems can analyze structured and unstructured data, interpret compliance documentation, and detect anomalies with greater accuracy. These systems not only automate evidence collection but also prioritize high-risk areas using predictive insights, thereby improving overall compliance efficiency. Furthermore, cognitive analytics enables continuous learning from historical audit outcomes, refining its models to adapt to evolving regulations. As a result, IT audit processes become more proactive, data-driven, and transparent. This paper reviews how cognitive analytics reshapes compliance auditing, improving reliability, speed, and accuracy while reducing operational overhead. The integration of cognitive intelligence represents a paradigm shift toward intelligent, adaptive, and autonomous compliance ecosystems capable of ensuring governance integrity in modern IT infrastructures.
DOI: http://doi.org/10.5281/zenodo.17880764
