Productivity vs. Compliance: The New Engineering Challenge of AI Coding Assistants in Regulated Codebases
Abstract
Background: Large language model (LLM)-based coding assistants have moved from experimental developer utilities into everyday software engineering practice. Contemporary studies show that these tools can improve task completion, code comprehension, and routine implementation speed, while also introducing verification costs, usability limits, and security uncertainty. In regulated industries, the relevant question is not only whether AI assistance improves productivity, but whether AI-assisted changes can be governed as part of an auditable control environment. Objective: This article develops a codepath-aware governance framework for AI-assisted engineering in regulated codebases, with emphasis on financial services, payments, healthcare, and other domains where software changes may affect legal, operational, privacy, and audit obligations. Methods: A targeted integrative conceptual review was conducted using recent evidence from 2024-2026 across AI-assisted programming, code-generation security, secure software development, responsible AI governance, and regulated-sector technology risk. Sources were selected for recency, authenticity, publisher quality, regulatory relevance, and direct usefulness for software delivery governance. Results: The synthesis identifies a productivity-compliance asymmetry: AI tools can accelerate code production faster than many organizations can adapt review, provenance, and evidence practices. The article proposes a five-layer governance model comprising regulated scope mapping, AI assistance policy, provenance and accountability, reviewer and control-owner routing, and audit evidence generation. Conclusion: Regulated organizations should avoid both blanket bans and unconstrained adoption. A more defensible approach is controlled acceleration: AI assistance is permitted, but its behavior changes according to the regulatory sensitivity of the codepath being modified. The resulting engineering system should make AI-assisted changes useful, bounded, attributable, reviewable, and auditable.