Governance, security and technical debt challenges in AI-enabled low-code development

Organizations across sectors are rapidly adopting low-code development platforms to accelerate digital transformation, reduce time-to-market, and broaden participation in software creation. As these platforms increasingly incorporate artificial intelligence capabilities, including machine learning-driven decision logic, automation, and predictive analytics, new governance, security, and sustainability challenges emerge. From a broad perspective, AI-enabled low-code development reshapes traditional software engineering boundaries by abstracting code, decentralizing development responsibility, and embedding adaptive logic into business workflows. While these shifts deliver speed and flexibility, they also complicate oversight, risk management, and long-term system maintainability. This paper examines the governance, security, and technical debt implications of integrating AI into low-code environments. It analyzes how distributed development models challenge established accountability structures, policy enforcement, and auditability when decision-making logic is learned rather than explicitly defined. Security risks are explored across the data, model, and orchestration layers, including vulnerabilities related to data leakage, model misuse, inference manipulation, and overprivileged integrations. The study further investigates how AI components introduce new forms of technical debt, such as model drift, opaque dependencies, lifecycle misalignment, and hidden operational costs that accumulate over time. Narrowing the focus, the paper proposes a structured analytical framework that links governance mechanisms, security controls, and technical debt management practices to the architectural characteristics of AI-enabled low-code platforms. By synthesizing insights from software engineering, enterprise architecture, and responsible AI research, the study identifies design principles and mitigation strategies that support scalable, secure, and sustainable adoption. The findings provide practical guidance for organizations seeking to balance rapid innovation with long-term control, resilience, and trust in AI-augmented low-code development. These insights inform policy, design, and governance decisions across enterprise digital transformation initiatives.