Technical

Traceability

Audit logs and provenance enabling system review and forensics.

How the laws define this

Regulators consistently define traceability as the ability to maintain comprehensive documentation, audit trails, and provenance records across a lifecycle to establish accountability and allow independent review or audit. The primary divergence lies in whether this obligation applies to internal system architecture or external outputs. Frameworks such as Resolución PGN 14/25 – Creación del Programa de Inteligencia Artificial del Ministerio Público Fiscal, the Public Service AI Framework, and the Artificial Intelligence (AI) Ethics Guidelines for Digital Thailand focus on the AI system itself, requiring records of dataset provenance, model versions, design choices, and decision logs to reconstruct development history and operational behavior. Conversely, the Code of Practice on AI-Generated Content Transparency targets the output, defining traceability as the capability to follow AI-generated content from its initial creation to its dissemination in order to verify authenticity and combat deceptive synthetic media.

Synthesised from the 4 statutory definitions below. Each one is quoted in full, with its source.

How jurisdictions differ

What is distinctive in each definition, binding law in force first.

Jurisdiction	Instrument	Status	What is distinctive
Argentina	Resolution PGN 14/25 – Creation of the Artificial Intelligence Program of the Public Prosecutor's Office	In Force	Adds support for testing, impact assessment and remedial action beyond audits, while applying specifically to public prosecutor's office AI tools.
European Union	Code of Practice on AI-Generated Content Transparency	In Force	Limits traceability to the lifecycle of AI-generated content only, from creation to dissemination, to verify authenticity and combat deceptive media.
New Zealand	Public Service AI Framework	In Force	Emphasizes enabling redress, ministerial inquiry and independent review in addition to audit and oversight for public service agencies.
Thailand	Artificial Intelligence (AI) Ethics Guidelines for Digital Thailand	Adopted	Specifies audit trails for datasets, model versions, training processes and decision logs to reconstruct origins, history and behavior over lifecycle.

What it triggers

In Argentina's Ministerio Público Fiscal, traceability supports testing, impact assessment and remedial action by ensuring system actions can be traced.source
The Public Service AI Framework sets out traceability to support internal audit, ministerial inquiry and independent review.source
Thailand's AI Ethics Guidelines call for traceability so that the origins, development history, and operational behavior of an AI system can be reconstructed and audited.source

Definitions (4)

The maintenance of audit logs, data provenance records, model documentation and change histories that enable reconstruction of system inputs, design choices and decision paths for accountability, audits and post‑deployment evaluation. Traceability supports testing, impact assessment and remedial action by ensuring system actions can be traced to sources and processes.

Resolución PGN 14/25 – Creación del Programa de Inteligencia Artificial del Ministerio Público FiscalDefinition 1 of 4

The requirement to maintain traceable data lineage, comprehensive documentation, model documentation and testing records to enable audit, oversight, investigation, and redress, supporting internal audit, ministerial inquiry and independent review.

Public Service AI FrameworkDefinition 2 of 4

The requirement to maintain provenance and audit trails for datasets, model versions, training processes, and decision logs so that the origins, development history, and operational behavior of an AI system can be reconstructed and audited over its lifecycle.

Artificial Intelligence (AI) Ethics Guidelines for Digital ThailandDefinition 3 of 4

Traceability refers to the capability to follow the entire lifecycle of AI-generated content, from its initial creation to its dissemination. This is crucial for establishing accountability, verifying authenticity, and combating the spread of deceptive synthetic media.

Code of Practice on AI-Generated Content TransparencyDefinition 4 of 4

Not to be confused with

Audit Trail

Audit trail refers specifically to immutable logs of inputs, prompts, outputs and system versions, while traceability encompasses broader records like data provenance and model documentation.

data use audit trail

Data use audit trail is limited to automated logging of dataset access and processing activities, narrower than traceability's full system lifecycle records.

Questions and answers

What does traceability mean for AI in Argentina's Public Prosecutor's Office?

It involves maintaining audit logs, data provenance, model documentation and change histories to reconstruct inputs, design choices and decisions. This enables accountability, audits, post-deployment evaluation, testing, impact assessment and remedial action.

How is traceability defined for AI-generated content in the EU?

Traceability is the capability to follow the entire lifecycle of AI-generated content from creation to dissemination. It establishes accountability, verifies authenticity and combats deceptive synthetic media.

What records are needed for traceability under Thailand's AI Ethics Guidelines?

Provenance and audit trails are described for datasets, model versions, training processes and decision logs. This allows reconstruction and auditing of the AI system's origins, development history and operational behavior over its lifecycle.