Tightening federal cybersecurity requirements - including updated Software Bill of Materials (SBOM) guidance and two executive orders on software supply chain security - are accelerating the deployment of AI-based defect detection systems across U.S. metal fabrication facilities. Plant managers and process engineers now navigate an environment where regulatory compliance and quality assurance technology are inseparable decisions, particularly as the sector absorbs a record surge in cyberattacks.
Background
The U.S. Cybersecurity and Infrastructure Security Agency (CISA) released draft 2025 Minimum Elements for an SBOM in August 2025, updating guidance originally published by the National Telecommunications and Information Administration in 2021. The new draft introduces additional elements - including Component Hash, License Information, and Generation Context - designed to produce machine-readable, verifiable inventories of every software component deployed across an organization. A 2022 OMB directive already requires federal agencies to use SBOMs aligned with CISA guidance, and legislative frameworks including EO 14028 and EO 14144 mandate SBOM attestations linked to secure software development lifecycle practices for federal contractors.
For metal fabrication operations supplying aerospace, defense, or government-adjacent customers, these requirements now extend directly to the AI inference engines and vision-system software running on the shop floor. CISA and G7 international partners also released joint guidance on Software Bill of Materials for AI, providing minimum elements specifically applicable to AI systems and their supply chains. AI defect detection platforms - which typically bundle third-party deep learning frameworks, edge compute drivers, and proprietary model weights - fall squarely within scope.
The compliance pressure arrives against a severe threat backdrop. According to KELA data, ransomware attacks against manufacturing surged 61% year-over-year in 2025, from 520 incidents to 838 - the highest growth rate of any critical sector. According to IBM X-Force's 2025 Threat Intelligence Index, manufacturing ranked as the top targeted industry for cyberattacks globally for the fourth consecutive year, accounting for 26% of all documented incidents within critical sectors. In May 2025, Nucor, North America's largest steel producer, detected unauthorized third-party access to its IT systems and halted production at multiple sites as a precautionary measure.
Details
The intersection of SBOM compliance and AI tooling is reshaping how fabricators evaluate and procure inspection software. TXOne Networks reported that 96% of OT incidents in 2025 could be traced back to IT system compromises, a finding that has heightened scrutiny of any software with network-facing components - including AI vision platforms integrated with PLCs, MES, and SCADA systems. Procurement teams at multi-site fabricators now request SBOM documentation from AI inspection vendors as part of standard supplier qualification, a practice previously confined to defense-tier supply chains.
To address site-specific data governance requirements while scaling model performance across facilities, fabricators and their technology vendors increasingly turn to federated learning architectures. Legal, privacy, and proprietary constraints often prevent centralized aggregation of industrial inspection data across distributed production sites, making federated approaches - where model weights are aggregated centrally without transmitting raw image or sensor data - a practical fit for multi-site operations. By training AI defect detection models on diverse defect data from multiple production lines using federated learning, detection accuracy improves while raw production data remains on-premises at each facility.
Manufacturers deploying AI inspection systems typically achieve return on investment within 6-12 months through reduced scrap, lower manual inspection labor, and fewer customer returns. State-of-the-art AI vision platforms detect surface defects at sub-millimeter resolution and operate continuously without the accuracy degradation associated with human inspector fatigue. AI streamlines regulatory compliance by automatically collecting and classifying data, generating audit reports, conducting proactive risk assessments, and maintaining explainable decision-making trails required by industry standards including IEC 62443 and ISO frameworks. The audit trail generated by modern AI inspection systems also supports SBOM-linked traceability requirements, allowing quality records to be tied directly to the software version and model weights that produced them.
In 2025, supply chain attacks on industrial organizations nearly doubled, from 154 incidents in 2024 to 297, as threat actors increasingly compromised smaller vendors, managed service providers, or SaaS platforms to gain indirect access to larger industrial targets. For fabricators operating embedded AI systems with cloud-connected update mechanisms, this expansion of the software supply chain attack surface makes SBOM hygiene a direct operational risk management concern - not merely a procurement formality.
Outlook
The EU Cyber Resilience Act went into effect in December 2024 and will be fully enforced starting in 2026, requiring all organizations bringing products with digital elements to market - including connected AI inspection hardware - to provide detailed SBOMs and deliver vulnerability patches in a timely manner. Fabricators with European export exposure face a hard compliance deadline that is pulling forward AI inspection upgrades otherwise likely to be deferred. Industry analysts and OT security practitioners expect the combination of federal SBOM finalization, defense industrial base procurement requirements, and CRA enforcement to drive measurable increases in AI inspection deployments at precision machining, sheet metal, and structural fabrication facilities over the next 12-18 months - with cybersecurity documentation becoming as fundamental to system selection as detection accuracy and throughput specifications.
