AI Governance
AI Governance for Supply Chain Operations
How EU AI Act, NIST AI RMF, and ISO/IEC 42001 apply to freight, customs, logistics, and procurement AI — and what supply chain leaders need to do about it.
Last updated March 23, 2026
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The AI governance gap in supply chain
Supply chain is one of the most AI-intensive operational domains in the enterprise. Document processing, HTS classification, compliance screening, demand forecasting, route optimization, and workforce allocation are all active areas of AI deployment. Yet governance frameworks are typically discussed in abstract or sector-agnostic terms — leaving supply chain leaders without concrete guidance on what applies to them and what they need to do.
That gap is closing. Three frameworks now define the AI governance landscape, each with direct implications for supply chain operations:
- EU AI Act (Regulation (EU) 2024/1689) — binding law with penalties, deadlines, and explicit supply chain-relevant risk categories
- NIST AI RMF (AI RMF 1.0, NIST.AI.100-1) — voluntary operational framework with four functions applicable to any AI deployment
- ISO/IEC 42001:2023 — the first international certifiable standard for AI management systems
This guide maps each framework specifically to supply chain operations, so you can assess where you stand and what steps are appropriate for your organization.
Why these three frameworks matter now
The EU AI Act's high-risk obligations for supply chain-relevant AI systems take effect in August 2026. The NIST AI RMF, while voluntary, is increasingly referenced in enterprise procurement requirements and financial sector guidance. ISO 42001 certification is beginning to appear as a supplier qualification requirement in enterprise and government procurement.
According to Accenture research covering more than 1,600 C-suite executives, only 12% of companies had achieved the level of AI maturity associated with superior growth outcomes — suggesting that most organizations are deploying AI before governance structures are in place to manage it responsibly.1
Supply chain organizations that build governance programs now will be better positioned for regulatory compliance, enterprise customer requirements, and the operational risks that come with AI at scale.
1 Accenture, "The Art of AI Maturity," 2022, survey of 1,600+ C-suite executives. accenture.com
EU AI Act
Risk-based classification system
Regulation (EU) 2024/1689 — published in the Official Journal on July 12, 2024 and in force from August 1, 2024 — organizes AI systems into four categories: prohibited practices, high-risk systems, systems with transparency obligations, and minimal-risk systems. Obligations scale with risk level.
The most onerous obligations attach to high-risk AI systems, which must satisfy requirements for risk management, data governance, technical documentation, transparency, human oversight, accuracy, robustness, and cybersecurity before they are placed on the EU market or put into service. Providers of high-risk systems must register them in an EU database.
The distinction between provider (the organization that develops or significantly modifies an AI system) and deployer (the organization that uses an AI system in a professional context) is critical — obligations differ substantially between the two roles. Many supply chain organizations are deployers, not providers, but deployer obligations are still meaningful.
High-risk supply chain AI systems
Annex III of Regulation (EU) 2024/1689 explicitly lists the categories of high-risk AI. Two are directly relevant to supply chain operations:
Critical infrastructure
Annex III covers AI used as a safety component in the management and operation of critical digital infrastructure, road traffic, and the supply of water, gas, heating, and electricity. AI systems that manage logistics networks, routing, or infrastructure operations in these categories may be in scope.
Employment, workers management, and access to self-employment
AI used for recruitment, task allocation, work monitoring, performance and behavior evaluation, and promotion or termination decisions is high-risk. This directly applies to AI used in warehouse and logistics workforce management — including systems that allocate picking tasks, monitor worker performance, or influence scheduling and productivity assessments.
AI used for spam filtering, basic analytics dashboards, or general demand forecasting that does not feed into high-risk decisions is likely to fall into the minimal-risk category. Whether a specific system is high-risk requires analysis of its intended purpose and deployment context against Annex III criteria.
Extraterritorial scope
The EU AI Act applies to providers regardless of whether they are established in the EU, if the AI system's output is used in the EU. It also applies to third-country deployers where the AI system is used in the EU. Additionally, importers and distributors of AI systems have specific obligations under the Regulation.
For supply chain organizations based in North America or Asia that serve EU customers, route freight through the EU, or operate EU facilities: if AI systems deployed in those operations have outputs used in the EU, the organization may be within scope. Legal counsel with EU AI Act expertise should assess the specific situation.
Compliance deadlines
- Feb 2025 Prohibited AI practices enforceable. Article 4 AI Literacy obligation applies to providers and deployers.
- Aug 2025 General-purpose AI (GPAI) model obligations apply.
- Aug 2026 High-risk AI system obligations under Annex III apply — this includes critical infrastructure and employment/worker management AI.
- Aug 2027 High-risk AI system obligations under Annex I apply.
Source: Future of Life Institute, "High-level summary of the AI Act," artificialintelligenceact.eu, based on Regulation (EU) 2024/1689.
Penalties and AI Literacy
Penalties for violations involving prohibited AI practices can reach up to EUR 35 million or 7% of global annual turnover, whichever is higher. For other violations of the Regulation, the ceiling is EUR 15 million or 3% of global annual turnover. SME-specific caps apply where lower.
Article 4 of the Regulation, which requires providers and deployers to ensure their personnel have sufficient AI literacy, has been in effect since February 2, 2025. In supply chain contexts, this means teams using AI for trade compliance, customs operations, or logistics decisions need documented training on what the AI does and how to exercise appropriate oversight — not just IT system training.
NIST AI RMF
Overview and current status
The NIST AI Risk Management Framework (AI RMF 1.0, document ID NIST.AI.100-1) was released on January 26, 2023 by the National Institute of Standards and Technology. It is a voluntary framework designed to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.
The AI RMF was published independently and predates the executive orders that have since been rescinded. NIST continues to maintain and develop the framework. A White House AI Action Plan released July 23, 2025 names NIST in numerous recommended policy actions, reinforcing the framework's continued relevance in U.S. AI policy discussions.
While voluntary, the AI RMF is increasingly referenced in enterprise procurement requirements, financial sector supervisory guidance, and federal contracting. Organizations that adopt it gain a structured, credible foundation for demonstrating AI risk management — which is valuable regardless of regulatory status.
Source: NIST, AI Risk Management Framework. Full document: NIST.AI.100-1.
Four functions applied to supply chain
The AI RMF organizes risk management into four core functions: Govern, Map, Measure, Manage. Each maps directly to operational challenges in supply chain AI governance.
Govern — Organizational accountability
The Govern function asks: who owns AI risk decisions in your supply chain organization? This includes defining roles and responsibilities, establishing policies for AI system approval and monitoring, and creating accountability structures that can support external audit. Supply chain organizations often lack a defined owner for AI risk — procurement, compliance, IT, and operations each have partial visibility but no single governance point.
Practical steps: designate an AI governance owner or committee that spans procurement, trade compliance, and operations; establish an AI system approval policy that includes risk classification; and document decision rights for AI-assisted versus AI-autonomous actions.
Map — AI risk identification in context
The Map function focuses on understanding the context in which AI operates and identifying the risks present in that context. For supply chain, this means cataloging every AI touchpoint: which systems handle HTS classification, compliance screening, freight audit, demand forecasting, route optimization, and workforce scheduling. For each, Map asks: what could go wrong, and what are the downstream consequences?
A mis-classification that results in an incorrect tariff rate, a missed denied-party screening hit, or an inaccurate demand forecast that drives excess inventory — each carries different risk profiles and requires different governance responses. The Map function forces explicit documentation of those profiles.
Measure — Metrics and evaluation
The Measure function asks: how do you know the AI is performing as intended, and how do you detect degradation? Supply chain-specific metrics include: accuracy rates for AI-assisted HTS classification versus customs authority outcomes; false positive and false negative rates on compliance screening; forecast error rates versus actuals; and freight audit accuracy against carrier invoices.
Measurement also includes bias and fairness evaluations where workforce management AI is in use, and adversarial robustness assessments for AI systems that could be targeted for manipulation in trade compliance contexts.
Manage — Incident response and treatment
The Manage function covers what you do when AI risk materializes. For supply chain, that means having defined incident response protocols for AI failures: what happens when an AI system produces a systematic mis-classification, flags a compliant transaction incorrectly, or produces a routing decision that results in regulatory exposure?
Effective risk management in this context requires audit trails that let you reconstruct what the AI did and why, human override capabilities, and escalation paths that include trade compliance counsel where regulatory exposure is involved.
GenAI Profile and supply chain LLM deployments
NIST released the GenAI Profile (NIST-AI-600-1) on July 26, 2024, extending the AI RMF to address risks specific to generative AI systems. The profile covers risks including confabulation (hallucination), data privacy violations, and value chain and component integration risks — all directly relevant to supply chain LLM deployments.
Supply chain organizations using large language models for document processing (commercial invoices, bills of lading, certificates of origin), HTS classification assistance, denied-party screening, or contract review should consult the GenAI Profile alongside the core AI RMF. The confabulation risk — where the model generates plausible but incorrect outputs — is particularly consequential in trade compliance contexts where errors carry regulatory and financial penalties.
ISO/IEC 42001
What ISO/IEC 42001 is
ISO/IEC 42001:2023 — full title: Information technology — Artificial intelligence — Management system for artificial intelligence — is the first international certifiable standard for AI management systems. Published in December 2023, it enables organizations to be independently audited and certified against its requirements, similar to how ISO 27001 works for information security or ISO 9001 works for quality management.
The standard defines requirements for establishing, implementing, maintaining, and continually improving an AI management system (AIMS). Key requirements include: an AI policy, AI risk assessment, AI impact assessment, documented organizational roles and responsibilities, monitoring and measurement programs, and processes for continual improvement.
Unlike the EU AI Act (which applies to specific AI system categories) or the NIST AI RMF (which provides operational guidance), ISO 42001 creates a certifiable organizational governance structure. Certification demonstrates AI governance maturity to customers, partners, auditors, and regulators in a standardized, third-party-verified way.
Source: ISO/IEC 42001:2023, International Organization for Standardization.
Relationship to ISO 27001 and ISO 9001
ISO/IEC 42001 follows the Harmonized Structure (Annex SL) — the common framework used by all modern ISO management system standards. This means organizations already certified to ISO 27001 (information security) or ISO 9001 (quality management) will find the structure immediately familiar: the same Plan-Do-Check-Act cycle, the same clause numbering pattern, and the same integration points for combining management systems.
For supply chain organizations that already hold ISO 27001 certification — which covers information security risks including data processed by AI systems — ISO 42001 can typically be implemented as an extension of the existing management system rather than a separate program. The documentation, audit, and review processes are compatible.
Companies already certified to ISO 27001 or ISO 9001 have a structural advantage in pursuing ISO 42001 — the management system framework is familiar, and the gap analysis is substantially narrower than building from scratch.
Certification as competitive advantage
For supply chain organizations that sell AI-enabled services to enterprise customers — including freight audit, classification, compliance screening, or logistics optimization — ISO 42001 certification is emerging as a supplier qualification differentiator. Enterprise procurement teams and information security reviewers increasingly ask for evidence of AI governance. Certification provides a standardized, auditable answer.
In regulated industries that participate in global supply chains — pharmaceuticals, aerospace, defense, financial services — AI governance certification may become a prerequisite for supplier approval programs, especially as the EU AI Act's deployer obligations create downstream requirements on the organizations that procure AI-enabled services.
Comparison
How the three frameworks compare
These frameworks are complementary, not competing. Each operates at a different layer of AI governance. Together, they provide a complete picture.
EU AI Act
- •Scope: EU market and any AI system whose output is used in the EU, regardless of provider location
- •Enforceability: Binding law with enforcement authority, supervisory bodies, and financial penalties
- •Certification: Conformity assessments for high-risk systems; EU database registration required
- •Supply chain specificity: Explicit — Annex III names critical infrastructure and employment/worker management as high-risk
- •Key timeline: High-risk Annex III obligations from August 2, 2026
NIST AI RMF
- •Scope: Voluntary; applicable to any organization deploying or developing AI, regardless of geography or sector
- •Enforceability: No regulatory authority; increasingly expected in procurement and sector guidance
- •Certification: No formal certification program; NIST maintains playbook and companion resources at airc.nist.gov
- •Supply chain specificity: Applicable through Map and Measure functions; GenAI Profile (NIST-AI-600-1) covers LLM-specific risks
- •Key use: Building internal governance programs and operationalizing AI risk management
ISO/IEC 42001:2023
- •Scope: Any organization developing or deploying AI; internationally applicable
- •Enforceability: Voluntary standard; carries weight in procurement, customer due diligence, and enterprise qualification
- •Certification: Yes — third-party certifiable, similar to ISO 27001. Integrates with existing management systems via Harmonized Structure
- •Supply chain specificity: Applicable through AI impact assessment and risk management requirements; adaptable to any operational domain
- •Key use: Demonstrating AI governance maturity to customers, partners, auditors, and regulators
Implementation Roadmap
Practical steps for supply chain organizations
Step 1 — AI inventory
Begin with a complete inventory of every AI system in use across your supply chain operations. This includes commercial tools, internally developed systems, and AI components embedded in ERP, WMS, TMS, or customs management platforms. For each system, document: what it does, who uses it, what decisions it influences, and who is accountable for it.
Many organizations discover that their actual AI footprint is significantly larger than formally acknowledged — AI components embedded in vendor software often go undocumented at the governance level.
Step 2 — Risk classification
Classify each AI system by risk level using the EU AI Act's categories as a baseline, even if your organization is not immediately in EU AI Act scope. The Annex III categories provide a structured, internationally referenced starting point. Key questions: does the system qualify as critical infrastructure management? Does it influence employment or worker management decisions?
For each high-risk classification, identify whether your organization is acting as a provider or a deployer — this determines the specific obligations that apply under the EU AI Act and informs the governance measures required.
Step 3 — Governance gap assessment
Assess governance gaps against the four NIST AI RMF functions for each high-risk system. For each system, ask: Is there a defined owner (Govern)? Is the risk context documented (Map)? Are there defined metrics and evaluation protocols (Measure)? Is there an incident response process (Manage)?
This assessment will typically reveal immediate quick wins — systems with no documented owner, no performance metrics, or no human override capability — alongside longer-term structural gaps that require program investment.
Step 4 — ISO 42001 certification planning
For organizations with EU exposure, enterprise customer requirements, or significant AI-enabled service delivery, evaluate whether ISO 42001 certification is appropriate. Organizations already holding ISO 27001 or ISO 9001 have a structural head start — engage your existing certification body to assess the gap and estimate the integration effort.
Certification planning typically takes 6–18 months depending on the size and complexity of the AI footprint. Beginning that process now positions organizations well ahead of the August 2026 EU AI Act high-risk deadlines.
Quick wins that apply to most supply chain organizations
- Document ownership for every AI system currently in use
- Implement audit logging on AI-assisted decisions in trade compliance and compliance screening
- Establish human approval requirements for consequential AI outputs (classification decisions, compliance flags, large purchase orders)
- Begin AI literacy training documentation to satisfy Article 4 obligations (EU AI Act, in effect February 2025)
- Review vendor AI governance practices for AI components embedded in existing platforms
Not sure where your governance gaps are?
Authentica can assess your AI governance posture against EU AI Act, NIST AI RMF, and ISO 42001 requirements — and show you what governance looks like when it is built into the platform itself.
Authentica
How Authentica addresses AI governance
Most supply chain AI governance challenges arise from the same root problem: AI is deployed to accelerate workflows, but the governance structures needed to maintain oversight — audit trails, approval workflows, risk-tiered autonomy — are added later or not at all. Authentica is designed with governance built in from the start, rather than applied as a layer on top.
Complete audit trail on every agent action
Every action taken by an Authentica agent is logged with full context — what input it received, what it decided, and why. This satisfies the documentation and transparency requirements central to the EU AI Act's high-risk system obligations and the NIST AI RMF's Measure and Manage functions.
Human-in-the-loop governance
Agents create proposals; humans approve or reject them. This structure is the practical implementation of human oversight requirements under the EU AI Act and ISO 42001 — consequential decisions require human authorization rather than autonomous execution.
Risk-tiered trust levels
Three trust levels — Restricted, Standard, and Elevated — map directly to risk classifications. Restricted is used for high-risk or novel decisions requiring stringent human review. Elevated is granted only after a proven operational track record. This tiered structure implements the risk-proportionate approach central to both the EU AI Act and the NIST AI RMF.
Hybrid Determinism
Authentica uses AI for ambiguity resolution and deterministic engines — including a MILP solver and rule engines — for precision-critical outputs. This architecture provides mathematical guarantees on critical decisions such as duty calculations and compliance screening results, addressing the NIST AI RMF Measure function requirement for accuracy and the EU AI Act's accuracy and robustness requirements for high-risk systems.
OrgBench deployment certification
OrgBench provides organization-specific deployment certification with a BenchID and audit artifacts. This directly supports the AI impact assessment and documentation requirements in ISO 42001, and provides the kind of deployment-specific evidence needed to demonstrate conformity under the EU AI Act.
Security and data governance baseline
SOC 2 Type II compliance, AES-256 encryption in transit and at rest, and a firm no-training-on-customer-data policy provide the data governance baseline required by all three frameworks. Customer data is processed only for authorized tasks and remains customer-owned throughout.
Frequently Asked Questions
Does the EU AI Act apply to my company if we are based in North America?
Potentially, yes. The EU AI Act has extraterritorial scope. It applies to providers regardless of where they are established if the AI system's output is used in the EU. It also applies to third-country deployers where the AI system's output is used in the EU. If you supply goods or services to EU customers and use AI systems in that process — for classification, compliance screening, demand forecasting, or logistics — those systems may fall within scope. Regulation (EU) 2024/1689 entered into force on August 1, 2024.
What supply chain AI systems are classified as high-risk under the EU AI Act?
Annex III of Regulation (EU) 2024/1689 explicitly lists critical infrastructure as high-risk — including safety components in the management and operation of critical digital infrastructure, road traffic, and the supply of water, gas, heating, and electricity. Employment and worker management AI is also high-risk: AI used for recruitment, task allocation, performance monitoring, and promotion decisions in warehouse and logistics environments falls into this category. Whether a specific system is high-risk depends on its function and context of deployment, so a qualified legal review is advisable for your specific situation.
Is the NIST AI RMF mandatory?
No. The NIST AI Risk Management Framework (AI RMF 1.0, NIST.AI.100-1, released January 26, 2023) is a voluntary framework. NIST describes it as intended to 'improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.' It is not a regulation and does not carry penalties for non-adoption. However, it is increasingly referenced in enterprise procurement requirements, financial sector guidance, and U.S. government contracting — making adoption effectively expected in many commercial contexts.
What are the penalties for non-compliance with the EU AI Act?
For violations involving prohibited AI practices, penalties can reach up to EUR 35 million or 7% of global annual turnover, whichever is higher. For other violations, the ceiling is EUR 15 million or 3% of global annual turnover. For small and medium enterprises, turnover-based caps apply instead if lower. These are maximum figures; actual enforcement actions will depend on the nature of the violation and the relevant national supervisory authority.
When do the EU AI Act high-risk obligations take effect?
Prohibited AI practices became enforceable on February 2, 2025. General-purpose AI model obligations apply from August 2, 2025. High-risk AI system obligations under Annex III (which includes supply chain-relevant categories such as critical infrastructure and employment/worker management) apply from August 2, 2026. High-risk systems under Annex I apply from August 2, 2027. Organizations that may be in scope should begin compliance work well ahead of those dates.
What is AI Literacy under Article 4 of the EU AI Act?
Article 4 requires providers and deployers of AI systems to ensure their staff have sufficient AI literacy — meaning the knowledge and skills to understand the AI systems they work with, make informed decisions about AI deployment, and apply appropriate oversight. This obligation has applied since February 2, 2025. In practice, it means supply chain teams deploying or using AI for classification, compliance, forecasting, or logistics need documented AI literacy programs, not just technical training for IT.
What is the difference between the EU AI Act and the NIST AI RMF?
The EU AI Act is binding law with enforcement authority, penalties, and compliance deadlines. It is risk-based and prescriptive for high-risk systems, requiring conformity assessments, registration, human oversight mechanisms, and documentation. The NIST AI RMF is a voluntary, non-prescriptive framework offering structured guidance on how to govern AI trustworthiness. The RMF is better suited for building internal governance programs, while the EU AI Act defines minimum legal obligations for organizations operating in or selling into the EU. They are complementary: adopting the NIST AI RMF can accelerate compliance with the EU AI Act's documentation and governance requirements.
How does ISO 42001 relate to ISO 27001?
ISO/IEC 42001:2023 (AI management systems) follows the same Harmonized Structure (Annex SL) as ISO/IEC 27001 (information security management). This means organizations already certified to ISO 27001 or ISO 9001 will find the framework familiar: it uses the same Plan-Do-Check-Act cycle and integrates with existing management system structures. An organization can extend its existing management system to cover AI governance rather than building a separate program from scratch. This significantly reduces the effort required to achieve ISO 42001 certification for companies that already hold ISO 27001.
What is ISO/IEC 42001 and how does it differ from the EU AI Act and NIST AI RMF?
ISO/IEC 42001:2023 is the first international certifiable standard for AI management systems. Published in December 2023, it enables organizations to be independently audited and certified against its requirements — similar to how ISO 27001 works for information security. Unlike the EU AI Act (regulatory law) or the NIST AI RMF (voluntary guidance), ISO 42001 is a management system standard that creates a certifiable governance structure. Certification can demonstrate AI governance maturity to customers, partners, auditors, and regulators in a standardized, verifiable way.
Do I need to notify a regulatory body about my AI systems?
Under the EU AI Act, providers of high-risk AI systems listed in Annex III must register those systems in an EU database before placing them on the market or putting them into service. The registration requirement applies to AI system providers, not to every deployer. Obligations for deployers of high-risk AI are distinct and include conducting fundamental rights impact assessments in certain cases, implementing human oversight measures, and maintaining logs. Organizations should assess whether they are acting as a provider (developing or significantly modifying an AI system) or a deployer (using an AI system developed by a third party), as the obligations differ.
How should supply chain companies prepare for AI governance?
Start with an AI inventory: catalog every AI system in use across freight, customs, procurement, compliance, and workforce management. Classify each system by risk level using EU AI Act categories as a baseline. Assess governance gaps against the four functions of the NIST AI RMF — Govern, Map, Measure, Manage. For organizations with significant EU exposure or those providing AI-enabled services to enterprise customers, begin evaluating ISO 42001 certification. Quick wins include documenting AI system ownership, establishing human oversight protocols, and implementing audit logging. The goal is a governance program that can scale as your AI footprint grows.
What role does human oversight play in AI governance?
Human oversight is a core requirement across all three frameworks. The EU AI Act mandates human oversight measures for high-risk AI systems, including the technical capability for human intervention during operation. The NIST AI RMF's Manage function includes human review as a component of risk response. ISO 42001 requires documented roles and responsibilities for AI system oversight. In practice, this means supply chain AI deployments should have defined approval workflows for consequential decisions — such as trade classification, compliance screening outcomes, and procurement commitments — rather than fully autonomous execution.
Can one governance program cover all three frameworks?
Yes, and that is the recommended approach. The three frameworks are complementary. ISO 42001 provides the management system structure. The NIST AI RMF provides the operational playbook for identifying, measuring, and managing AI risks. The EU AI Act defines the minimum legal obligations for organizations in scope. A well-designed governance program uses ISO 42001 as the structural backbone, maps NIST AI RMF functions to internal processes, and documents EU AI Act obligations as a compliance layer. This avoids redundant programs and makes governance auditable across all three dimensions.
What is the NIST GenAI Profile and does it apply to supply chain?
The NIST GenAI Profile (NIST-AI-600-1, released July 26, 2024) extends the AI RMF specifically to generative AI risks including confabulation, data privacy violations, and value chain and component integration risks. It is directly relevant to supply chain organizations using large language models for document processing, HTS classification assistance, compliance screening, or customer-facing applications. The GenAI Profile should be consulted alongside the core AI RMF when assessing governance requirements for any generative AI deployment in supply chain operations.
How does Authentica support AI governance for supply chain?
Authentica is designed with governance built in. Every agent action generates a complete audit trail. The human-in-the-loop model — where agents create proposals and humans approve or reject them — satisfies the oversight requirements central to the EU AI Act, NIST AI RMF, and ISO 42001. Trust levels (Restricted, Standard, Elevated) map directly to risk classifications, so high-risk decisions require more stringent human review. SOC 2 Type II compliance, AES-256 encryption, and a no-training-on-customer-data policy address the data governance requirements that run through all three frameworks. OrgBench provides organization-specific deployment certification with BenchID and audit artifacts.
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See how Authentica's built-in governance aligns with EU AI Act, NIST AI RMF, and ISO 42001 requirements — and what it means for your supply chain operations.
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Built-in audit trails, human oversight, and risk-tiered autonomy — designed for the governance requirements supply chain operations now face.
Disclaimer: This guide is provided for informational purposes only and does not constitute legal, regulatory, or compliance advice. Framework obligations described are based on publicly available sources as of March 23, 2026 and may change as regulations evolve or are clarified by enforcement authorities. Organizations should consult qualified legal counsel and compliance professionals regarding their specific obligations under the EU AI Act and applicable frameworks. Authentica is a technology company and does not provide legal or regulatory advisory services.