The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, AI Governance & Regulation · Depth: Expert, quick

Summary

A systematic literature review by Ayush Enkhtaivan and Chinazunwa Uwaoma examines the challenges of integrating artificial intelligence into critical infrastructure sectors like healthcare, finance, energy, and defense while adhering to evolving regulatory frameworks. The review, synthesizing peer-reviewed articles and institutional sources published between 2020 and 2025, identifies three primary challenges: fragmented regulations, excessive compliance burdens for small to medium enterprises (SMEs), and misaligned governance models. To address these issues, the study highlights practical governance strategies, including risk-tiered regulation, compliance by design, and explainable AI. These strategies aim to support scalable and trustworthy AI deployment in critical sectors. The authors contribute a concise mapping of core AI-governance challenges, a conceptual diagram illustrating their overlap, and actionable strategies for policymakers and practitioners to harmonize oversight with innovation.

Key takeaway

For policymakers developing AI governance frameworks, you must address fragmented regulations and disproportionate compliance burdens on SMEs. Implement risk-tiered regulation and compliance by design to foster innovation while ensuring trustworthiness. Prioritize explainable AI to enhance transparency and facilitate scalable deployment in critical sectors. Your strategies should harmonize oversight with technological advancement, avoiding misaligned governance models.

Key insights

Balancing AI innovation with compliance in critical sectors faces fragmented regulations, SME burdens, and misaligned governance, requiring strategic solutions.

Principles

• AI integration in critical sectors creates regulatory conflict.
• Fragmented regulations hinder AI deployment.
• Governance models often misalign with AI innovation.

Method

A systematic literature review (SLR) followed established guidelines to extract and synthesize insights from peer-reviewed articles, reports, and institutional sources published from 2020-2025.

In practice

• Implement risk-tiered AI regulation.
• Adopt compliance by design principles.
• Utilize explainable AI for trustworthiness.

Topics

• AI Compliance
• Critical Infrastructure
• AI Governance
• Regulatory Fragmentation
• Explainable AI
• Compliance by Design

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Policy Maker, Legal Professional, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.