TL;DR

August 2, 2026 is not the date when every high-risk AI obligation suddenly becomes enforceable. It is the point when the EU AI Act becomes a much more immediate evidence problem for enterprise technology leaders.

Article 50 transparency duties begin to apply. The European Commission gains enforceable authority over general-purpose AI model providers, including the ability to request documentation, evaluate models, require corrective measures, and impose fines. National authorities begin operating in a broader enforcement environment for obligations already in effect.

The core Chapter III requirements for Annex III high-risk AI systems are now scheduled for December 2, 2027. The equivalent requirements for high-risk AI systems integrated into Annex I regulated products are scheduled for August 2, 2028.

That delay is not permission to wait. CIOs need an engineering evidence architecture now: a trustworthy AI system inventory, role classification by system, model and supplier documentation, transparency controls, versioned runtime records, data lineage, incident evidence, and a tested regulatory response process. A policy states what should happen. Evidence shows what actually happened, on which system version, under whose authority, and with which control result.

Introduction

For the last several years, enterprise AI governance has been built primarily through policies, principles, committees, intake forms, and review boards. Those structures are still necessary, but they are no longer sufficient.

A governance committee can approve a customer-service chatbot. It cannot, by itself, prove which notice appeared at the first interaction, which model version generated a response, whether synthetic content marking survived the publishing pipeline, which data sources were retrieved, or who changed the system after approval.

A policy can require human review. It cannot prove that the review happened before publication, that the reviewer had authority to reject the content, or that the approved version was the version actually released.

This is the change CIOs need to recognize before August 2, 2026. The EU AI Act is moving from policy interpretation toward demonstrable implementation. The compliance question is no longer only, “Do we have an AI policy?” It is increasingly, “Can we produce reliable evidence that the correct control operated for this specific AI system, role, model version, use case, and date?”

This article is an engineering and operating-model analysis, not legal advice. Legal counsel should validate system classification, territorial scope, exceptions, and sector-specific obligations. The CIO’s responsibility is to ensure that the technical estate can support those determinations with accurate, reproducible evidence.

August 2 Is an Enforcement Boundary, Not the High-Risk Deadline

The most important starting point is the timeline. Many organizations still describe August 2, 2026 as if the entire AI Act arrives at once. That is no longer accurate after the 2026 Digital Omnibus amendments.

The Act now has several operationally distinct dates:

The practical consequence is a two-speed program.

The first track must prove the obligations that apply now, particularly transparency and applicable GPAI duties. The second track must build the evidence and control foundation required for future high-risk compliance without incorrectly claiming those Chapter III duties are already enforceable.

That distinction matters. Overstating the August 2026 deadline creates unnecessary panic and weakens credibility. Understating it creates a different problem: organizations may discover that their transparency controls, supplier records, system inventories, and response workflows cannot produce usable evidence when an authority asks for it.

Why Governance Documents Are No Longer Enough

Traditional compliance evidence is often document-centric. The organization publishes a policy, records committee minutes, completes an assessment, and stores an approval artifact. That works reasonably well when the controlled object changes slowly.

AI systems do not change slowly.

A production AI service may change when the provider updates a hosted model, a team switches model routing, a prompt is revised, a retrieval index is rebuilt, a tool permission is expanded, an agent workflow adds a new action, a content pipeline strips metadata, or a business unit changes the intended audience. Each of those changes can alter the system’s role, transparency behavior, risk profile, or evidence requirements.

The evidence problem is therefore temporal and technical. The organization must be able to connect the approval state to the system state that existed when the AI interaction or output occurred.

A defensible evidence record should answer:

• Which AI system was operating?
• Which provider, deployer, or other operator role applied to the organization for that system?
• Which model, model version, prompt, workflow, retrieval configuration, and tool set were active?
• Which control was required?
• Did the control execute successfully?
• What evidence was produced?
• Who owned the control and who accepted any exception?
• How long is the evidence retained?
• Can the organization reproduce the answer without relying on one engineer’s memory?

A static policy can define those questions. It cannot answer them at runtime.

The Engineering Evidence Chain CIOs Need

The central architecture pattern is simple: every regulatory requirement must map to an engineering control, every engineering control must produce an evidence artifact, and every artifact must have an accountable owner.

The control is not complete when it exists in the architecture diagram. It is complete when it produces evidence that can be tied to a system, version, time, and owner.

That evidence should have five properties.

It Must Be System-Specific

“Microsoft Copilot,” “OpenAI,” or “internal chatbot” is not a sufficient inventory record. One provider may support dozens of enterprise AI systems with different purposes, users, data sources, interfaces, and legal roles.

The evidence unit should be the deployed AI system or governed service, not the vendor name.

It Must Be Version-Aware

A screenshot of a disclosure is weak evidence if the organization cannot connect it to the release that handled the interaction. The same problem applies to model cards, prompt approvals, evaluation results, and security reviews.

Evidence should reference the deployed build, model identifier, prompt or policy version, content-marking component, and effective date.

It Must Be Reproducible

A regulator, auditor, or internal reviewer should be able to rerun the relevant test or reconstruct the decision. That may require test inputs, configuration snapshots, output samples, detection results, change records, and retention of the policy logic that made the decision.

It Must Be Protected

Compliance evidence may contain personal data, confidential prompts, model details, security findings, or incident records. Centralizing it without classification, access control, integrity protection, retention rules, and legal hold support creates a new risk surface.

It Must Have an Owner

Shared responsibility is not evidence ownership. The AI platform team may operate the logging pipeline, but the product owner may own the disclosure. Security may own incident handling, while the data steward owns lineage. Legal may interpret the requirement, but it should not be expected to reconstruct technical facts from six disconnected platforms.

Legal Role Classification Must Be Per System and Per Layer

The AI Act’s provider and deployer definitions create an engineering inventory problem because legal role is not a single enterprise-wide label.

An organization can be a deployer of one AI system, a provider of another, and both at different layers of the same solution. A company that consumes a third-party model through an API may still become the provider of the customer-facing AI system it develops or has developed and puts into service under its own name. The upstream company may remain the provider of the general-purpose AI model.

The evidence handoff should reflect that supply chain:

The same enterprise may occupy the middle and bottom boxes. A supplier contract does not remove the enterprise’s own system-level obligations. Conversely, an enterprise using a third-party service should not claim to possess upstream training and model-development evidence it does not control.

The inventory therefore needs a role record with at least:

• organization role by system and supply-chain layer
• rationale and approving legal owner
• upstream model provider
• downstream deployer or customer, where relevant
• branding and placement-in-service facts
• substantial modification or fine-tuning assessment
• applicable geographic scope
• effective date and review trigger

Role classification should be reviewed when ownership, branding, model architecture, intended purpose, or distribution changes. Treating it as a one-time questionnaire will not survive a fast-moving AI portfolio.

The Eight Evidence Domains Every CIO Should Establish

A practical EU AI Act compliance architecture does not need one giant governance platform. It needs a consistent evidence model across the systems the enterprise already uses.

Enterprise AI System Inventory

The inventory is the root of the evidence graph. Every control, document, incident, model, owner, and deadline should connect back to a unique system identifier.

At minimum, capture:

• system name and unique ID
• business purpose and intended users
• countries and populations affected
• business and technical owners
• provider and deployer roles
• upstream models and providers
• interfaces, including chat, API, embedded workflow, agent, and content pipeline
• data sources and output destinations
• tools, actions, and authority
• transparency obligations
• provisional Annex III or Annex I classification
• deployment environments and current versions
• lifecycle state, including pilot, production, suspended, and retired

Discovery should combine procurement, identity, network, API gateway, cloud, SaaS, code repository, endpoint, CI/CD, Kubernetes, and finance data. A manually maintained spreadsheet can start the process, but it should not be the only discovery mechanism for a large enterprise.

Provider, Deployer, and Supply-Chain Classification

Classification must be stored as a reasoned decision, not a drop-down value. The record should identify the facts used, the legal interpretation, the accountable approver, and the conditions that trigger reassessment.

A system built on an external foundation model may have at least three separate role questions:

• Who provides the GPAI model?
• Who provides the integrated AI system?
• Who deploys the system in the actual business context?

This separation prevents a common mistake: assuming that buying the model transfers every compliance duty to the vendor.

Model and Supplier Documentation

For most enterprises, model documentation begins as supplier evidence intake. It should include the current model identifier, version or release family, capabilities, limitations, supported use, known restrictions, evaluation information, data-handling terms, security documentation, incident contacts, change-notification terms, and downstream compliance documentation.

For organizations that are themselves GPAI model providers, the burden is much deeper. The technical dossier must cover training and testing processes, evaluation results, downstream information on capabilities and limitations, copyright policy, and the public training-content summary. Providers of GPAI models with systemic risk also need documented evaluations, adversarial testing, systemic-risk assessment, incident reporting, and cybersecurity evidence.

A procurement portal attachment is not enough if nobody can connect the document to the model version used in production.

Transparency Notices and Synthetic-Content Marking

Article 50 turns transparency into an implementation control.

Where the organization is the provider of an AI system that interacts directly with people, evidence should show that the notice appears clearly by the first interaction unless the AI nature is genuinely obvious under the applicable test. That requires more than a sentence in a privacy policy. It requires interface tests across supported channels, languages, accessibility modes, embedded experiences, mobile clients, and fallback paths.

For synthetic audio, image, video, or text content, providers need evidence that machine-readable marking is applied where required and remains detectable. The pipeline should test what happens after resizing, transcoding, format conversion, document export, content-management ingestion, social publishing, and downstream editing.

For deployer disclosures involving deepfakes, emotion recognition, biometric categorization, or public-interest text, the organization needs a publishing or operational gate that records the label, the exception basis if used, the human review or editorial-control evidence where relevant, and the accountable publisher.

The transparency control should produce both design evidence and runtime or release evidence.

Runtime, Change, and Decision Evidence

Logging is essential, but CIOs should be precise about why.

The formal automatic record-keeping requirement in Article 12 belongs to the delayed core high-risk regime. Organizations should not describe every 2026 AI log as proof that Article 12 is already applicable to their Annex III or Annex I systems.

However, runtime and change evidence is still necessary now to prove transparency behavior, reconstruct incidents, validate supplier changes, investigate complaints, and produce reliable regulatory responses. It is also the foundation that future high-risk logging requirements will depend on.

A useful minimum record includes:

• system and request identifier
• timestamp and jurisdictional context
• application and workflow version
• model and routing decision
• prompt or policy version reference
• transparency control result
• content-marking result where applicable
• retrieval source references and policy decisions
• tool invocations and approval results
• human review status
• exception or override identifier
• output destination
• incident correlation ID

Content does not always need to be retained. In many environments, storing complete prompts and outputs would create privacy, security, and retention risks. The evidence design should distinguish metadata needed for traceability from sensitive content that requires stronger justification.

Data Lineage and Provenance

Data lineage is another area where current and future duties must be separated.

The detailed high-risk data-governance requirements are part of the delayed Chapter III regime. GPAI model providers already have specific documentation and training-content summary obligations. Enterprise deployers still need practical lineage now because they must understand what data enters the system, what sources ground outputs, where generated content is published, and which evidence may contain personal or confidential data.

The lineage model should cover more than model training. It should include:

• prompt and user-input sources
• retrieved documents and access-control context
• embeddings and vector indexes
• fine-tuning and evaluation datasets
• tool inputs and outputs
• generated content destinations
• telemetry and evidence stores
• backups, exports, and support bundles
• retention and deletion paths

Without that map, an organization cannot answer whether a problematic output came from the model, the prompt, the retrieval source, the tool result, or a downstream publishing transformation.

Incident, Complaint, and Corrective-Action Evidence

Incident handling must distinguish between the current GPAI regime and the future high-risk system regime.

Providers of GPAI models with systemic risk already have duties to track, document, and report serious incidents and possible corrective measures. From August 2, 2026, the Commission can enforce GPAI provider obligations.

For most enterprise deployers, the immediate engineering requirement is to make AI incidents discoverable and reconstructable. The organization should define how complaints, harmful outputs, failed disclosures, missing labels, model changes, data exposures, and unsafe agent actions are identified, contained, preserved, investigated, and escalated.

The record should include:

• affected system and versions
• incident start and detection time
• users, jurisdictions, and data affected
• transparency and control status
• model, prompt, retrieval, and tool context
• containment action
• preserved evidence
• vendor notification
• corrective action and validation
• decision to restore, restrict, or retire the system

An incident ticket without system-version evidence is an administrative record, not a technical reconstruction.

Regulatory Response Package

The final domain is response orchestration. Evidence spread across GRC, ticketing, CI/CD, model registries, cloud logs, identity platforms, data catalogs, contract repositories, and email is not a response package until it can be assembled coherently.

Each system should have a defined evidence manifest and response owner. The response package should be scoped to the request, reviewed for confidentiality and legal privilege, and generated from authoritative sources.

A strong package may contain:

• system identity and ownership
• role and risk classification
• architecture and data-flow summary
• applicable obligation map
• current model and supplier documentation
• control definitions and implementation evidence
• release and change history
• transparency test results
• runtime and incident evidence
• exceptions and compensating controls
• corrective-action status
• contact and escalation information

The target is not to export every log. The target is to produce the smallest complete evidence set that answers the authority’s question accurately.

What CIOs Must Be Able to Prove on August 2, 2026

The exact proof set depends on the organization’s systems and legal roles, but the following control map is a practical baseline.

Article 50 noncompliance is not a documentation formality. The Act places transparency failures in a penalty tier that can reach EUR 15 million or 3 percent of worldwide annual turnover for an undertaking, subject to the facts, proportionality, and the applicable enforcement process. GPAI providers face a separate Commission fine ceiling of EUR 15 million or 3 percent for specified failures, including failures involving required documentation, corrective measures, or model access.

Those are maximum ceilings, not automatic outcomes. The engineering lesson is not to design for fear. It is to design so the organization can show what it did, identify failures quickly, cooperate accurately, and correct the system with evidence.

What Is Not Yet Enforced, But Must Be Engineered Now

The December 2, 2027 and August 2, 2028 dates delay the core Chapter III high-risk requirements. They do not remove the implementation work.

For candidate Annex III and Annex I systems, CIOs should create a separate readiness backlog covering:

• lifecycle risk management
• training, validation, and testing data governance where applicable
• technical documentation
• automatic event logging
• instructions and information for deployers
• effective human oversight
• accuracy, robustness, and cybersecurity
• provider quality-management processes
• conformity assessment and registration dependencies
• deployer operating procedures
• post-market monitoring and incident processes
• fundamental-rights and data-protection assessment integration where applicable

The key is to label the work correctly. It is high-risk readiness, not a claim that every future obligation is already enforceable.

Waiting until late 2027 creates four predictable failures.

First, historical evidence cannot be reconstructed reliably. A system that has been changing for eighteen months will not suddenly produce a trustworthy lineage and release history because a deadline arrives.

Second, architecture changes take time. Logging, marking, model version capture, identity correlation, approval gates, and evidence retention often require changes across several platforms and vendors.

Third, supplier contracts may not contain the documentation, notification, access, or assistance terms needed for future compliance. Those changes move at procurement speed, not sprint speed.

Fourth, business owners may not know which systems are candidate high-risk systems. Inventory and classification must precede remediation.

The correct posture is to enforce the August 2026 controls now while building reusable components that can support the later high-risk evidence model.

Build an Evidence Plane, Not Another Governance Portal

Many organizations will respond to the AI Act by buying or building a central portal. A portal can help, but it should not become another place where people manually copy stale information.

The stronger architecture is an evidence plane that links existing systems of record.

The evidence plane does not need to duplicate every artifact. It can maintain trusted references, hashes, version identifiers, ownership, retention metadata, and retrieval permissions while the authoritative evidence stays in the source platform.

The architecture should support these capabilities:

• automated discovery and reconciliation
• policy and obligation mapping by role and date
• versioned control definitions
• evidence collection from delivery and runtime systems
• integrity and provenance checks
• access controls and confidentiality boundaries
• retention, deletion, and legal hold
• exception expiration
• regulatory request workflow
• response completeness checks
• dashboards for missing or stale evidence

The design principle is simple: evidence should be produced by normal engineering and operating workflows, not assembled manually only when compliance asks for it.

Use a Machine-Readable Evidence Manifest

A machine-readable manifest gives every system a consistent evidence contract. The following vendor-neutral YAML is an implementation pattern, not a statutory format. Replace the identifiers, retention periods, owners, and control mappings with values approved for the actual environment.

evidence_manifest_version: 1

system:
  id: customer-service-assistant-042
  lifecycle_state: production
  business_owner: customer-operations
  technical_owner: ai-platform-team
  jurisdictions:
    - EU

classification:
  organisation_role:
    ai_system: provider_and_deployer
    gpai_model: downstream_integrator
  rationale_record: ROLE-2026-0042
  provisional_risk_class: transparency_risk
  next_review_trigger:
    - intended_purpose_change
    - model_provider_change
    - customer_population_change
    - new_tool_authority

release:
  application_version: 2026.08.02.3
  model_provider: approved-provider
  model_identifier: model-family-version
  prompt_policy_version: prompt-policy-17
  retrieval_policy_version: retrieval-policy-8
  deployed_at: 2026-08-02T08:30:00Z

transparency:
  first_interaction_notice:
    control_id: ART50-1
    test_result: pass
    evidence_refs:
      - EVID-UI-00881
      - EVID-A11Y-00214
  synthetic_content_marking:
    control_id: ART50-2
    applicable: true
    detector_test_result: pass
    evidence_refs:
      - EVID-MARK-00132

operations:
  runtime_event_schema: ai-evidence-event-v3
  content_retained: false
  metadata_retention_days: 395
  incident_runbook: RUNBOOK-AI-INC-6
  regulatory_response_owner: ai-assurance-office

exceptions:
  active: false

The manifest should be created or updated by the release process. Successful deployment should fail when mandatory evidence fields are absent, when a required transparency test fails, or when an exception has expired.

The most important field is not the compliance label. It is the version linkage. The manifest must identify the system state for which the evidence is valid.

A Practical 90-Day CIO Implementation Sequence

The goal is not to solve every future high-risk requirement in one quarter. The goal is to create a reliable August 2026 proof baseline and a reusable path toward the later deadlines.

Days 0 to 30: Discover and Classify

Create a cross-functional inventory team covering enterprise architecture, AI platform engineering, security, procurement, privacy, legal, data governance, HR, and major business units.

Prioritize systems that:

• interact directly with people in the EU
• generate or manipulate content
• publish public-interest information
• use emotion recognition or biometric categorization
• integrate external GPAI models
• operate under the company’s brand
• make or influence consequential decisions
• can take actions through tools or agents

Assign a unique system ID, business owner, technical owner, legal role, transparency applicability, model supplier, and provisional high-risk classification. Record unknowns explicitly rather than converting them into false certainty.

Days 31 to 60: Instrument and Remediate

Implement or validate first-interaction notices, content disclosures, machine-readable marking, editorial review gates, version capture, model documentation intake, change events, and incident correlation.

Test the complete delivery path. A marker added by the model endpoint is not effective if the document exporter, image processor, or content platform removes it. A notice in the main web interface is not sufficient if the mobile, voice, email, or embedded channel bypasses it.

Define the minimum metadata retained for traceability and apply privacy, security, and records-management controls to the evidence store.

Days 61 to 90: Exercise the Response

Run a tabletop exercise using a realistic regulatory or complaint scenario.

For example:

Provide the system classification, model and release versions, Article 50 control evidence, user-facing notice, content-marking test, change history, incident record, and accountable owners for a specific customer interaction from the previous month.

Measure how long it takes to identify the system, find the evidence, resolve conflicting records, obtain legal review, and generate a complete response.

Any answer that depends on a specific engineer manually searching logs is a control gap. Any evidence that cannot be tied to the production version is a traceability gap. Any system without a business owner is an accountability gap.

Use the exercise to create the 2027 and 2028 high-risk readiness backlog.

Common Failure Modes to Avoid

Inventorying Vendors Instead of Systems

A vendor list does not reveal intended purpose, users, data, interfaces, controls, or legal role. Inventory each deployed system and connect it to its suppliers.

Classifying the Company Once

The enterprise is not simply “a deployer.” Role depends on the system and supply-chain layer. Store classification per system and review it when the design changes.

Treating a Model Card as Complete Evidence

A model card can support supplier due diligence. It does not prove the enterprise’s interface notice, content pipeline, retrieval controls, user context, human review, release version, or incident process.

Putting Disclosure Only in Policy Text

Article 50 is implemented through the user experience and content workflow. General terms, privacy notices, or policy pages may support the control, but they do not replace a clear disclosure at the required interaction or exposure point.

Logging Everything Without a Purpose

Unbounded prompt and output retention creates privacy, security, and cost problems. Capture the metadata needed to prove control execution and reconstruct events. Retain sensitive content only when there is a defined purpose, lawful basis, owner, protection model, and deletion path.

Assuming the High-Risk Delay Means No Work

The delayed dates move the enforcement boundary. They do not shorten inventory, procurement, architecture, integration, testing, and organizational lead times.

Centralizing Evidence Without Protecting It

An evidence repository can contain some of the most sensitive information in the AI estate. Apply strong identity, segregation, encryption, integrity, monitoring, retention, and export controls.

Building Evidence Only for Regulators

The same evidence improves incident response, supplier management, architecture reviews, release quality, security investigations, model change control, and executive accountability. A well-designed evidence plane is an operating capability, not a compliance archive.

The CIO Proof Test

Before August 2, the CIO should ask whether the organization can answer these questions with current evidence:

1. Can we list every production AI system that interacts with people or generates content in the EU?
2. Can we show our provider or deployer role for each system and explain the rationale?
3. Can we identify the exact application, model, prompt, policy, retrieval, and tool versions used for a specific event?
4. Can we prove that the required AI interaction notice appeared by the first interaction?
5. Can we prove that required machine-readable content marking was applied and survived downstream transformations?
6. Can we prove when a deepfake or applicable public-interest text was disclosed, reviewed, or handled under an exception?
7. Can we show the AI literacy measures assigned to the people operating each system without claiming a one-size-fits-all training program?
8. Can we retrieve the current supplier and model documentation that applies to the production version?
9. Can we reconstruct an AI incident without retaining more sensitive content than necessary?
10. Can we generate a reviewed regulatory response package from authoritative records in days rather than months?

A “yes” should mean the evidence has been tested, not that someone believes the records probably exist.

Conclusion

The EU AI Act is becoming an engineering evidence problem because enforcement depends on facts that policy documents cannot produce on their own.

Starting August 2, 2026, CIOs need to prove applicable transparency controls, support existing AI literacy measures, distinguish provider and deployer roles, manage GPAI evidence where their organization is in scope, and respond coherently when authorities request information. They must do that without incorrectly claiming that the core Annex III and Annex I high-risk engineering requirements have already reached their new deadlines.

The right response is not another static compliance binder. It is an evidence architecture that links regulation to controls, controls to artifacts, artifacts to system versions, and system versions to accountable owners.

That foundation will do more than satisfy August 2026. It will make the December 2027 and August 2028 high-risk programs achievable because the organization will already know what systems exist, how they are classified, which models and data they depend on, what controls operate, what evidence is retained, and who owns the answer.

The practical test is straightforward: when someone asks what happened, the enterprise should not respond with a policy. It should be able to produce a verified evidence chain.

External References

• European Commission: Commission starts enforcing AI Act rules and new transparency requirements on 2 August
  Canonical URL: https://digital-strategy.ec.europa.eu/en/news/commission-starts-enforcing-ai-act-rules-and-new-transparency-requirements-2-august
• European Commission: AI Act
  Canonical URL: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
• EUR-Lex: Regulation (EU) 2026/1744 of the European Parliament and of the Council of 8 July 2026 amending Regulations (EU) 2024/1689, (EU) 2018/1139 and (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI)
  Canonical URL: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:L_202601744
• EUR-Lex: Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act)
  Canonical URL: https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
• European Commission: Transparency obligations under Article 50 of the AI Act
  Canonical URL: https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act
• European Commission: Guidelines on transparency obligations for providers and deployers of AI systems
  Canonical URL: https://digital-strategy.ec.europa.eu/en/library/guidelines-transparency-obligations-providers-and-deployers-ai-systems
• European Commission: Guidelines for providers of general-purpose AI models
  Canonical URL: https://digital-strategy.ec.europa.eu/en/policies/guidelines-gpai-providers

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