Artificial Intelligence (AI) is rapidly reshaping the field of auditing and assurance by shifting the focus from traditional sampling and manual procedures toward continuous, data-driven examination. Emerging tools such as machine learning, natural language processing, and robotic process automation now enable auditors to evaluate entire datasets, uncover subtle anomalies in real time, and integrate both structured information (ledgers, transactions) and unstructured evidence (contracts, correspondence, digital records). For India, this transformation is particularly significant given the rapid expansion of e-invoicing, digital payment ecosystems, and enterprise resource planning platforms, which generate vast volumes of auditable data. While these advances create opportunities to enhance audit efficiency, fraud detection, and governance insights, they also introduce new challenges related to explainability, ethical use of algorithms, regulatory oversight, and disparities in technology readiness across firms. This paper examines the dual dimensions of opportunity and risk in adopting AI for auditing, develops a conceptual framework linking AI capability to audit quality, and proposes a risk–control matrix for designing “assurance-grade AI.” Policy recommendations highlight the need for strong governance structures, transparent documentation, regulatory clarity, and educational reforms to ensure that AI adoption in India strengthens—not undermines—audit quality and public trust.