EU AI Act Art. 60: testing in real-world conditions outside sandboxes

Testing of high-risk (Annex III) AI systems in real-world conditions outside AI regulatory sandboxes

By Jorge García Aibar Reviewed 2026-09-24

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-EUAIA-ART60. Drawn from chapter 08.

Text alternative

Clause: EU AI Act, Art. 60.
Duty holder: Provider / prospective provider.
Applies from: 2026-08-02, In force.
Artefact: Real-world testing plan.
Layers: Layer 03, Layer 04.
Evidence record: Test plan.
Record schemas: Test plan .
The same topic in 19 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-EUAIA-ART60
Instrument: EU AI Act (post-Omnibus) law
Compared side by side: ISO 42001 vs EU AI Act · NIST AI RMF vs EU AI Act
Clause: Art. 60
Duty holder: Provider / prospective provider
Authority: National MSA
Applies from: 2026-08-02 In force
System class: High-risk (Annex III)

The artefact that evidences it

Real-world testing plan; Art. 61 informed-consent records; test monitoring, logging and incident hooks.

Patterns that build it

Staged Rollout with Rollback Criteria (layer 4)

The same topic in other frameworks

From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.

Robustness, security and evaluations

EU AI Act Art. 15 Accuracy, robustness and cybersecurity (core)
EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk (core)
ISO 42001 A.6 AI system life cycle (core)
NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics (core)
TC260 Framework 3.0 TC260 3 Technological countermeasures (core)
TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming (core)
China Deep Synthesis DeepSyn Art. 15 Technology management and algorithm verification (core)
China Deep Synthesis DeepSyn Art. 20 Security assessment of new products (core)
GB/T 45654 GB/T 45654 Security assessment Security-assessment requirements for generative AI services (core) (clause not verified)
GPAI Code Safety 3.2 Measure 3.2: Model evaluations (core)
NIST AI RMF MEASURE 2.7 MEASURE 2.7: AI system security and resilience as identified in the MAP function are evaluated and documented (core)
CSA AICM MDS-06 Adversarial Attack Analysis (core)
CSA AICM MDS-07 Robustness against Adversarial Attack / Model Hardening (core)
Singapore GenAI GenAI 5 Testing and Assurance (core)
Singapore GenAI GenAI 6 Security (core)
Singapore Agentic Agentic 2.3.2 Before deploying, test agents (core)
CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified (core)
OECD AI Principles OECD 1.4 Robustness, security and safety (core)
G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing (core)
GAO AI Accountability 3.7 Assessment: assess performance against defined metrics to ensure the AI system functions as intended and is sufficiently robust (core)
ISO 42001 9.1 Monitoring, measurement, analysis and evaluation
TC260 Framework 3.0 TC260 5.3.14 Resilience
China GenAI Measures GenAI Art. 17 Security assessment
EU AI Act Art. 15(3) Declared accuracy levels and metrics
EU AI Act Art. 9 Risk management system
EU AI Act Art. 42(3) Presumption of conformity for cybersecurity (Cyber Resilience Act)
GPAI Code Safety C6 Commitment 6: Security mitigations
GDPR Art. 32 Security of processing
NIST AI RMF MEASURE 2.1 MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented
NIST AI RMF MEASURE 1 MEASURE 1: Appropriate methods and metrics are identified and applied
CSA AICM AIS-05 Application Security Testing
OWASP LLM LLM01:2026 Prompt Injection
OWASP Agentic ASI05 Unexpected Code Execution (RCE)
Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold
GAO AI Accountability 3.2 Metrics: define performance metrics that are precise, consistent, and reproducible

Sandboxes and real-world testing

EU AI Act Art. 57 AI regulatory sandboxes (core)
CoE Convention CoE Art. 13 Safe innovation (controlled testing environments) (core)
EU AI Act Art. 58 Detailed arrangements for, and functioning of, AI regulatory sandboxes
EU AI Act Art. 59 Further processing of personal data in the AI regulatory sandbox
EU AI Act Art. 61 Informed consent to participate in testing in real world conditions
ISO 42001 A.6.2.4 AI system verification and validation (clause not verified)
NIST AI RMF MEASURE 2.3 MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s)
CSA AICM AIS-13 AI Sandboxing
Singapore Agentic Agentic 2.3.2 Before deploying, test agents
TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming

Source

Chapter 08, section EU AI Act, post-Omnibus, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-euaia-art60.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). EU AI Act Art. 60: testing in real-world conditions outside sandboxes (AIGE-OBL-EUAIA-ART60). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-euaia-art60. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{EU AI Act Art. 60: testing in real-world conditions outside sandboxes (AIGE-OBL-EUAIA-ART60)}},
  howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
  year         = {2026},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.22956197},
  url          = {https://aigovernanceengineer.com/obligations/aige-obl-euaia-art60},
  note         = {Version 0.5.0}
}