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

Fragmented regulations create confusion and inefficiency: The paper identifies fragmented regulatory frameworks across critical sectors (healthcare, finance, energy, defense) as a major barrier, meaning organizations must navigate different compliance requirements depending on their industry, making it difficult to develop unified AI governance approaches.

Smaller and medium-sized enterprises (SMEs) face disproportionate compliance burdens: The review finds that compliance costs and complexity disproportionately affect SMEs compared to large corporations, potentially slowing innovation and creating barriers to entry in critical infrastructure sectors where responsible AI deployment is most needed.

"Compliance by design" and risk-tiered regulation offer practical solutions: Rather than one-size-fits-all rules, the paper recommends tailoring compliance requirements based on risk levels and building compliance considerations into AI systems from the start, which could reduce both regulatory burden and deployment delays.

Explainable AI is essential for trustworthy deployment: The study highlights explainable AI as a key governance strategy, suggesting that making AI decision-making transparent and understandable is critical for maintaining public trust and enabling effective oversight in critical sectors.

Governance models must evolve to keep pace with innovation: The paper emphasizes that misaligned governance models—where regulations lag behind technological capabilities—create conflicts between compliance and innovation, requiring more adaptive regulatory frameworks that can accommodate rapid technological change while maintaining safety standards.