From Data Lakes to Trusted AI Infrastructure: Assessing Data Quality, Governance Maturity, and AI Readiness in U.S. Critical Infrastructure Agencies.

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The transition from siloed data lakes to enterprise-grade, trusted artificial intelligence (AI) infrastructure represents one of the most consequential technological challenges facing U.S. critical infrastructure agencies today.This paper presents a comprehensive assessment of data quality standards, governance maturity frameworks, and AI readiness levels across the 16 federally designated critical infrastructure sectors, with particular focus on civilian federal agencies serving as Sector Risk Management Agencies (SRMAs).Drawing on the latest federal audit reports from the Government Accountability Office (GAO), Office of Management and Budget (OMB) memoranda M-25-21 and M-25-22 (April 2025), CISA's Cybersecurity Performance Goals 2.0 (December 2025), the Stanford AI Index 2025, and IDC's enterprise AI maturity study (2025), this research synthesizes quantitative and qualitative evidence to construct a multidimensional AI readiness index for critical infrastructure agencies.Findings reveal that while federal agencies nearly doubled their reported AI use cases from 571 in 2023 to 1,110 in 2024 with generative AI use cases increasing ninefold fundamental data governance gaps persist.Only 28% of organizations have formally defined AI oversight roles, and fewer than 15% of agencies have networks fully optimized for AI workloads.This paper proposes a five-tier Trusted AI Infrastructure Maturity Model (TAIMM), sector-specific governance recommendations, and a strategic roadmap for closing the gap between data lake accumulation and operationalized AI capability.