Law / Frameworks / Crosswalk / ETSI EN 304 223 to FINOS AIGF

The ETSI EN 304 223, Baseline Cyber Security Requirements for AI Models and Systems, mapped to the FINOS AI Governance Framework

19 of ETSI EN 304 223's provisions connect to 16 of FINOS AIGF's through 436 laws in 166 jurisdictions. Two provisions connect when the same duties, in at least 2 laws we track, reach both. The connections are our own reading of the law; no framework publisher has reviewed or endorsed them.

From
ETSI EN 304 223, Baseline Cyber Security Requirements for AI Models and Systems, version 2.1.1, December 2025, European Telecommunications Standards Institute (ETSI)
To
FINOS AI Governance Framework, version 2, as maintained on , FINOS
See also
FINOS AIGF to ETSI EN 304 223, the other way · every pair in the crosswalk · law library as of

Download this mapping, with every law behind each connection: finos-ai-governance-framework--etsi-en-304-223.csv · finos-ai-governance-framework--etsi-en-304-223.json

ETSI EN 304 223FINOS AIGFLawsJurisdictionsWhere the laws stand
5.1.2-1Business need and security risks written down and assessed before deciding to build an... AIR-PREV-005System Acceptance Testing 156 94 130 in force, 17 not yet in force, 1 blocked by a court, 8 proposed
The 156 laws behind 5.1.2-1 and AIR-PREV-005

and 146 more, every one in the free file.

AIR-DET-021Agent Decision Audit and Explainability 2 2 1 in force, 1 not yet in force
The 2 laws behind 5.1.2-1 and AIR-DET-021
5.1.2-2System built to hold up against adversarial attacks, unexpected inputs and failures. AIR-PREV-007Legal and Contractual Frameworks for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.2-2 and AIR-PREV-007
AIR-PREV-008Quality of Service (QoS) and DDoS Prevention for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.2-2 and AIR-PREV-008
AIR-PREV-012Role-Based Access Control for AI Data 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.2-2 and AIR-PREV-012
AIR-PREV-014Encryption of AI Data at Rest 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.2-2 and AIR-PREV-014
AIR-PREV-017AI Firewall Implementation and Management 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.2-2 and AIR-PREV-017
AIR-PREV-020MCP Server Security Governance 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.2-2 and AIR-PREV-020
AIR-DET-001AI Data Leakage Prevention and Detection 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.2-2 and AIR-DET-001
5.1.2-6Access the AI system gets to other systems and data sources is minimal and risk assessed. AIR-PREV-018Agent Authority Least Privilege Framework 145 107 142 in force, 3 not yet in force
The 145 laws behind 5.1.2-6 and AIR-PREV-018

and 135 more, every one in the free file.

AIR-PREV-023Agentic System Credential Protection Framework 145 107 142 in force, 3 not yet in force
The 145 laws behind 5.1.2-6 and AIR-PREV-023

and 135 more, every one in the free file.

AIR-PREV-007Legal and Contractual Frameworks for AI Systems 7 5 7 in force
The 7 laws behind 5.1.2-6 and AIR-PREV-007
AIR-DET-013Providing Citations and Source Traceability for AI-Generated Information 5 3 5 in force
The 5 laws behind 5.1.2-6 and AIR-DET-013
5.1.2-7Due diligence on external providers, including whether they follow this standard. AIR-PREV-007Legal and Contractual Frameworks for AI Systems 2 2 1 in force, 1 not yet in force
The 2 laws behind 5.1.2-7 and AIR-PREV-007
5.1.3-1Threat modelling and risk management, reviewed regularly and covering AI-specific attacks. AIR-PREV-005System Acceptance Testing 14 11 7 in force, 2 not yet in force, 5 proposed
The 14 laws behind 5.1.3-1 and AIR-PREV-005

and 4 more, every one in the free file.

AIR-PREV-007Legal and Contractual Frameworks for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-1 and AIR-PREV-007
AIR-PREV-008Quality of Service (QoS) and DDoS Prevention for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-1 and AIR-PREV-008
AIR-PREV-012Role-Based Access Control for AI Data 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-1 and AIR-PREV-012
AIR-PREV-014Encryption of AI Data at Rest 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-1 and AIR-PREV-014
AIR-PREV-017AI Firewall Implementation and Management 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-1 and AIR-PREV-017
AIR-PREV-020MCP Server Security Governance 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-1 and AIR-PREV-020
AIR-DET-001AI Data Leakage Prevention and Detection 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-1 and AIR-DET-001
AIR-PREV-006Data Quality & Classification/Sensitivity 2 1 2 in force
The 2 laws behind 5.1.3-1 and AIR-PREV-006
AIR-PREV-024Human-in-the-Loop Action Approval Gate 2 1 2 in force
The 2 laws behind 5.1.3-1 and AIR-PREV-024
AIR-DET-004AI System Observability 2 1 2 in force
The 2 laws behind 5.1.3-1 and AIR-DET-004
AIR-DET-011Human Feedback Loop for AI Systems 2 1 2 in force
The 2 laws behind 5.1.3-1 and AIR-DET-011
AIR-DET-021Agent Decision Audit and Explainability 2 1 2 in force
The 2 laws behind 5.1.3-1 and AIR-DET-021
5.1.3-2Threats developers cannot resolve are passed to operators and on to end-users. AIR-DET-021Agent Decision Audit and Explainability 70 44 42 in force, 16 not yet in force, 12 proposed
The 70 laws behind 5.1.3-2 and AIR-DET-021

and 60 more, every one in the free file.

AIR-DET-004AI System Observability 7 5 4 in force, 3 not yet in force
The 7 laws behind 5.1.3-2 and AIR-DET-004
AIR-PREV-005System Acceptance Testing 3 3 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-2 and AIR-PREV-005
AIR-PREV-006Data Quality & Classification/Sensitivity 3 3 2 in force, 1 not yet in force
The 3 laws behind 5.1.3-2 and AIR-PREV-006
AIR-PREV-024Human-in-the-Loop Action Approval Gate 2 2 2 in force
The 2 laws behind 5.1.3-2 and AIR-PREV-024
AIR-DET-011Human Feedback Loop for AI Systems 2 2 2 in force
The 2 laws behind 5.1.3-2 and AIR-DET-011
5.1.4-1Capability for human oversight designed in and maintained. AIR-PREV-005System Acceptance Testing 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.1.4-1 and AIR-PREV-005

and 68 more, every one in the free file.

AIR-PREV-006Data Quality & Classification/Sensitivity 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.1.4-1 and AIR-PREV-006

and 68 more, every one in the free file.

AIR-PREV-024Human-in-the-Loop Action Approval Gate 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.1.4-1 and AIR-PREV-024

and 68 more, every one in the free file.

AIR-DET-004AI System Observability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.1.4-1 and AIR-DET-004

and 68 more, every one in the free file.

AIR-DET-011Human Feedback Loop for AI Systems 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.1.4-1 and AIR-DET-011

and 68 more, every one in the free file.

AIR-DET-021Agent Decision Audit and Explainability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.1.4-1 and AIR-DET-021

and 68 more, every one in the free file.

AIR-PREV-007Legal and Contractual Frameworks for AI Systems 2 2 2 in force
The 2 laws behind 5.1.4-1 and AIR-PREV-007
5.1.4-3Where oversight is a risk control, the technical measures behind it are built,... AIR-PREV-006Data Quality & Classification/Sensitivity 5 3 3 in force, 2 not yet in force
The 5 laws behind 5.1.4-3 and AIR-PREV-006
AIR-PREV-005System Acceptance Testing 4 2 2 in force, 2 not yet in force
The 4 laws behind 5.1.4-3 and AIR-PREV-005
AIR-PREV-024Human-in-the-Loop Action Approval Gate 4 2 2 in force, 2 not yet in force
The 4 laws behind 5.1.4-3 and AIR-PREV-024
AIR-DET-004AI System Observability 4 2 2 in force, 2 not yet in force
The 4 laws behind 5.1.4-3 and AIR-DET-004
AIR-DET-011Human Feedback Loop for AI Systems 4 2 2 in force, 2 not yet in force
The 4 laws behind 5.1.4-3 and AIR-DET-011
AIR-DET-021Agent Decision Audit and Explainability 4 2 2 in force, 2 not yet in force
The 4 laws behind 5.1.4-3 and AIR-DET-021
5.1.4-5End-users told which uses of the system are prohibited. AIR-PREV-005System Acceptance Testing 143 86 124 in force, 15 not yet in force, 1 blocked by a court, 3 proposed
The 143 laws behind 5.1.4-5 and AIR-PREV-005

and 133 more, every one in the free file.

5.2.1-1Full inventory of assets, including their dependencies and connections. AIR-PREV-005System Acceptance Testing 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.1-1 and AIR-PREV-005

and 68 more, every one in the free file.

AIR-PREV-006Data Quality & Classification/Sensitivity 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.1-1 and AIR-PREV-006

and 68 more, every one in the free file.

AIR-PREV-024Human-in-the-Loop Action Approval Gate 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.1-1 and AIR-PREV-024

and 68 more, every one in the free file.

AIR-DET-004AI System Observability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.1-1 and AIR-DET-004

and 68 more, every one in the free file.

AIR-DET-011Human Feedback Loop for AI Systems 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.1-1 and AIR-DET-011

and 68 more, every one in the free file.

AIR-DET-021Agent Decision Audit and Explainability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.1-1 and AIR-DET-021

and 68 more, every one in the free file.

AIR-PREV-007Legal and Contractual Frameworks for AI Systems 2 2 2 in force
The 2 laws behind 5.2.1-1 and AIR-PREV-007
5.2.1-4Sensitive data such as training and test sets protected from unauthorised access. AIR-PREV-007Legal and Contractual Frameworks for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.2.1-4 and AIR-PREV-007
AIR-PREV-008Quality of Service (QoS) and DDoS Prevention for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.2.1-4 and AIR-PREV-008
AIR-PREV-012Role-Based Access Control for AI Data 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.2.1-4 and AIR-PREV-012
AIR-PREV-014Encryption of AI Data at Rest 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.2.1-4 and AIR-PREV-014
AIR-PREV-017AI Firewall Implementation and Management 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.2.1-4 and AIR-PREV-017
AIR-PREV-020MCP Server Security Governance 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.2.1-4 and AIR-PREV-020
AIR-DET-001AI Data Leakage Prevention and Detection 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.2.1-4 and AIR-DET-001
5.2.2-5AI-specific incident management and recovery plans that are created, tested and maintained. AIR-DET-004AI System Observability 27 16 19 in force, 7 not yet in force, 1 proposed
The 27 laws behind 5.2.2-5 and AIR-DET-004

and 17 more, every one in the free file.

AIR-DET-021Agent Decision Audit and Explainability 26 16 18 in force, 7 not yet in force, 1 proposed
The 26 laws behind 5.2.2-5 and AIR-DET-021

and 16 more, every one in the free file.

AIR-PREV-005System Acceptance Testing 12 9 9 in force, 2 not yet in force, 1 proposed
The 12 laws behind 5.2.2-5 and AIR-PREV-005

and 2 more, every one in the free file.

AIR-PREV-006Data Quality & Classification/Sensitivity 12 9 9 in force, 2 not yet in force, 1 proposed
The 12 laws behind 5.2.2-5 and AIR-PREV-006

and 2 more, every one in the free file.

AIR-PREV-024Human-in-the-Loop Action Approval Gate 12 9 9 in force, 2 not yet in force, 1 proposed
The 12 laws behind 5.2.2-5 and AIR-PREV-024

and 2 more, every one in the free file.

AIR-DET-011Human Feedback Loop for AI Systems 12 9 9 in force, 2 not yet in force, 1 proposed
The 12 laws behind 5.2.2-5 and AIR-DET-011

and 2 more, every one in the free file.

5.2.2-6Contracts with cloud providers that back the requirements above. AIR-PREV-007Legal and Contractual Frameworks for AI Systems 2 2 1 in force, 1 not yet in force
The 2 laws behind 5.2.2-6 and AIR-PREV-007
5.2.4-1Developers keep an audit trail of system design and post-deployment maintenance plans... AIR-PREV-005System Acceptance Testing 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.4-1 and AIR-PREV-005

and 68 more, every one in the free file.

AIR-PREV-006Data Quality & Classification/Sensitivity 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.4-1 and AIR-PREV-006

and 68 more, every one in the free file.

AIR-PREV-024Human-in-the-Loop Action Approval Gate 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.4-1 and AIR-PREV-024

and 68 more, every one in the free file.

AIR-DET-004AI System Observability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.4-1 and AIR-DET-004

and 68 more, every one in the free file.

AIR-DET-011Human Feedback Loop for AI Systems 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.4-1 and AIR-DET-011

and 68 more, every one in the free file.

AIR-DET-021Agent Decision Audit and Explainability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.2.4-1 and AIR-DET-021

and 68 more, every one in the free file.

AIR-PREV-007Legal and Contractual Frameworks for AI Systems 2 2 2 in force
The 2 laws behind 5.2.4-1 and AIR-PREV-007
5.2.4-2.1Minimum record for training data: its source, such as a page URL, and when it was collected. AIR-PREV-007Legal and Contractual Frameworks for AI Systems 18 16 17 in force, 1 not yet in force
The 18 laws behind 5.2.4-2.1 and AIR-PREV-007

and 8 more, every one in the free file.

AIR-PREV-018Agent Authority Least Privilege Framework 2 2 2 in force
The 2 laws behind 5.2.4-2.1 and AIR-PREV-018
AIR-PREV-023Agentic System Credential Protection Framework 2 2 2 in force
The 2 laws behind 5.2.4-2.1 and AIR-PREV-023
5.3.1-1Users told where and how their data is used, accessed and stored, with developers... AIR-DET-021Agent Decision Audit and Explainability 70 44 42 in force, 16 not yet in force, 12 proposed
The 70 laws behind 5.3.1-1 and AIR-DET-021

and 60 more, every one in the free file.

AIR-DET-004AI System Observability 7 5 4 in force, 3 not yet in force
The 7 laws behind 5.3.1-1 and AIR-DET-004
AIR-PREV-005System Acceptance Testing 3 3 2 in force, 1 not yet in force
The 3 laws behind 5.3.1-1 and AIR-PREV-005
AIR-PREV-006Data Quality & Classification/Sensitivity 3 3 2 in force, 1 not yet in force
The 3 laws behind 5.3.1-1 and AIR-PREV-006
AIR-PREV-024Human-in-the-Loop Action Approval Gate 2 2 2 in force
The 2 laws behind 5.3.1-1 and AIR-PREV-024
AIR-DET-011Human Feedback Loop for AI Systems 2 2 2 in force
The 2 laws behind 5.3.1-1 and AIR-DET-011
5.3.1-2.1That guidance covers proper use, limits and likely failure modes. AIR-DET-021Agent Decision Audit and Explainability 70 44 42 in force, 16 not yet in force, 12 proposed
The 70 laws behind 5.3.1-2.1 and AIR-DET-021

and 60 more, every one in the free file.

AIR-DET-004AI System Observability 7 5 4 in force, 3 not yet in force
The 7 laws behind 5.3.1-2.1 and AIR-DET-004
AIR-PREV-005System Acceptance Testing 3 3 2 in force, 1 not yet in force
The 3 laws behind 5.3.1-2.1 and AIR-PREV-005
AIR-PREV-006Data Quality & Classification/Sensitivity 3 3 2 in force, 1 not yet in force
The 3 laws behind 5.3.1-2.1 and AIR-PREV-006
AIR-PREV-024Human-in-the-Loop Action Approval Gate 2 2 2 in force
The 2 laws behind 5.3.1-2.1 and AIR-PREV-024
AIR-DET-011Human Feedback Loop for AI Systems 2 2 2 in force
The 2 laws behind 5.3.1-2.1 and AIR-DET-011
5.4.2-1Operators log system and user actions. AIR-PREV-005System Acceptance Testing 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.4.2-1 and AIR-PREV-005

and 68 more, every one in the free file.

AIR-PREV-006Data Quality & Classification/Sensitivity 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.4.2-1 and AIR-PREV-006

and 68 more, every one in the free file.

AIR-PREV-024Human-in-the-Loop Action Approval Gate 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.4.2-1 and AIR-PREV-024

and 68 more, every one in the free file.

AIR-DET-004AI System Observability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.4.2-1 and AIR-DET-004

and 68 more, every one in the free file.

AIR-DET-011Human Feedback Loop for AI Systems 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.4.2-1 and AIR-DET-011

and 68 more, every one in the free file.

AIR-DET-021Agent Decision Audit and Explainability 78 37 50 in force, 27 not yet in force, 1 proposed
The 78 laws behind 5.4.2-1 and AIR-DET-021

and 68 more, every one in the free file.

AIR-PREV-007Legal and Contractual Frameworks for AI Systems 2 2 2 in force
The 2 laws behind 5.4.2-1 and AIR-PREV-007
5.4.2-2Logs analysed for anomalies, breaches and effects of drift or poisoning. AIR-PREV-007Legal and Contractual Frameworks for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.4.2-2 and AIR-PREV-007
AIR-PREV-008Quality of Service (QoS) and DDoS Prevention for AI Systems 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.4.2-2 and AIR-PREV-008
AIR-PREV-012Role-Based Access Control for AI Data 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.4.2-2 and AIR-PREV-012
AIR-PREV-014Encryption of AI Data at Rest 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.4.2-2 and AIR-PREV-014
AIR-PREV-017AI Firewall Implementation and Management 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.4.2-2 and AIR-PREV-017
AIR-PREV-020MCP Server Security Governance 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.4.2-2 and AIR-PREV-020
AIR-DET-001AI Data Leakage Prevention and Detection 3 2 2 in force, 1 not yet in force
The 3 laws behind 5.4.2-2 and AIR-DET-001
Not connected to the FINOS AIGF: 53 of ETSI EN 304 223's provisions

Short of 2 laws

The same duties reach these and a FINOS AIGF control, in fewer than 2 of the laws we track.

No law we track reaches these

No duty in the laws we track reaches these provisions.

  • 5.1.1-1 AI security content in the regular staff training programme, kept current.
  • 5.1.1-1.1 That training is adjusted to each person's role.
  • 5.1.1-2 All staff kept aware of current AI-related threats and the mitigations that exist.
  • 5.1.1-2.1 Threat updates circulated through several internal channels.
  • 5.1.1-2.2 Developers trained in secure coding and system design for AI work.
  • 5.1.2-1.1 Data custodians inside the developer's own organisation join the requirements discussion.
  • 5.1.2-3 Audit trail covering models, datasets and prompts through their lifecycle.
  • 5.1.2-4 Security risk assessment and due diligence before using an external component.
  • 5.1.2-5 Data custodians check that the intended use suits how sensitive the training data is.
  • 5.1.2-5.1 Staff encouraged to raise AI security concerns, with safeguards in place for doing so.
  • 5.1.3-1.1 Threat modelling repeated whenever a setting or configuration option is added or changed.
  • 5.1.3-1.2 Developers manage the risk from model capabilities the system does not need.
  • 5.1.3-1.3 Operators apply controls to the risks found, weighed against cost and their risk tolerance.
  • 5.1.3-3 Operators seek assurance from outside parties who hold responsibility for identified risks.
  • 5.1.3-4 Continuous monitoring and review of infrastructure against the organisation's risk appetite.
  • 5.1.4-2 Systems designed so people can judge the outputs they answer for.
  • 5.1.4-4 Developers verify that the data custodian's security controls made it into the build.
  • 5.2.1-2 Tooling and processes to track, authenticate, version and secure AI assets.
  • 5.2.1-3 Disaster recovery plans adapted to attacks aimed at AI systems.
  • 5.2.1-3.1 A known good state can be restored.
  • 5.2.1-4.1 Checks and sanitisation applied to data and inputs, and repeated when the model is revised.
  • 5.2.1-4.2 Proportionate protection where training data or model weights may be confidential.
  • 5.2.2-1 Access-control review, and measures to secure APIs, models, data and pipelines.
  • 5.2.2-2 Controls, such as rate limits, on any API offered to outside parties.
  • 5.2.2-3 Separate environments for development and tuning, with least privilege enforced technically.
  • 5.2.2-4 A published, easy-to-find vulnerability disclosure policy.
  • 5.2.3-1 Secure software supply-chain practice for developing models and systems.
  • 5.2.3-2 Written justification when using models or components that are poorly documented or secured.
  • 5.2.3-2.1 Mitigating controls and a risk assessment for those components.
  • 5.2.3-2.2 That justification shared with end-users in an accessible way.
  • 5.2.3-3 Evaluations run again on released models before they are adopted.
  • 5.2.3-4 End-users told before a model is updated.
  • 5.2.4-1.1 That documentation records security-relevant facts such as data sources, scope,...
  • 5.2.4-1.2 Cryptographic hashes published for model components handed to others.
  • 5.2.4-2 Records of how publicly sourced training data was obtained, where it came from and how...
  • 5.2.4-3 Audit log of changes to system prompts and other configuration that alter behaviour.
  • 5.2.5-1 Every model, application and system is security-assessed before release.
  • 5.2.5-2 Operators test before deployment, with developer support.
  • 5.2.5-2.1 Independent testers with relevant AI skills used for security testing.
  • 5.2.5-3 Developers pass their test findings to operators.
  • 5.2.5-4 Outputs evaluated so that non-public details of the model or its training data cannot...
  • 5.2.5-4.1 Outputs evaluated so users cannot gain unintended influence over the system.
  • 5.3.1-2 Accessible guidance on using, managing, integrating and configuring the system.
  • 5.3.1-2.2 Users proactively told about security-relevant updates.
  • 5.4.1-1 Developers issue security patches and notify operators, who pass them to users.
  • 5.4.1-1.1 Contingency mechanisms for cases where an update cannot be issued.
  • 5.4.1-2 A major update is treated as a new model version and re-tested.
  • 5.4.1-3 Operators helped to evaluate model changes, for example with preview access and versioned APIs.
  • 5.4.2-3 Internal system states monitored where that helps address security threats.
  • 5.4.2-4 Model and system performance tracked over time for shifts in behaviour that touch security.
  • 5.5.1-1 Data custodians involved and assets securely disposed of when training data or a model...
  • 5.5.1-2 Data custodians involved and data and configuration securely deleted when a model or...

How these connections are drawn

Each requirement line in the laws we track is read against the framework for its subject (the AI, scraping and news aggregation laws against the NIST AI RMF, the privacy, communications and age assurance laws against the NIST Privacy Framework, the cybersecurity laws against the NIST CSF 2.0); laws on liability and content moderation are not read against any framework yet. A line reaches either framework through a kind of duty its law carries, and only where that kind of duty also reaches, on that same line, the NIST framework for the line's own subject. Two provisions connect when lines in at least 2 laws reach both. That says the same duties reach both; it does not say the two are alike, or that running either one does what a law asks.

Every duty line in the AI, scraping and news aggregation laws we track has been read against the framework for its subject.

Sources and licenses

Identifiers refer to ETSI EN 304 223 V2.1.1 (2025-12), © ETSI 2025, all rights reserved. ETSI owns the standard; no ETSI text (titles or sentences) is reproduced here. The standard is free to download from ETSI.

FINOS AI Governance Framework, copyright 2025 FINOS, licensed under CC BY 4.0. Titles and purpose text are quoted; markdown emphasis and links were removed.