Adaptive audit frameworks for autonomous and AI-assisted decision ecosystems

The growing integration of autonomous and AI-assisted decision mechanisms within enterprise environments is quietly reshaping the foundations of information systems auditing. Traditional audit models, which largely assume deterministic system behavior and stable evidence trails, appear increasingly strained when confronted with probabilistic outputs, continuous learning processes, and opaque model logic. This study explores the emerging need for adaptive audit frameworks capable of responding to these evolving technological conditions without abandoning core assurance principles. Rather than proposing a complete methodological rupture, the paper argues that effective adaptation is likely to emerge through incremental reinterpretation of existing audit constructs particularly in relation to evidence reliability, control evaluation, and governance oversight. Conceptual analysis is combined with insights from contemporary AI governance discourse to outline a flexible assurance approach that accommodates uncertainty while preserving accountability. The findings suggest that future audit effectiveness may depend less on rigid procedural expansion and more on the auditor’s capacity to interpret dynamic system behavior, integrate continuous monitoring, and align governance mechanisms with autonomous decision architectures.