Comply · International standards
ISO/IEC 5259:2024 — Data quality for analytics & ML
Data quality for analytics and machine learning.
The ISO/IEC 5259 series addresses data quality for analytics and machine learning — defining a data-quality model, measures, process frameworks, and governance so that the data feeding AI systems is fit for purpose and well-documented.
Who it applies to
Organizations that need to assure and evidence the quality of data used to train and run AI.
Key obligations
- Apply a data-quality model and measures
- Govern data-quality processes across the ML pipeline
- Document data provenance and fitness for purpose
How Maetra maps agents to ISO/IEC 5259
Maetra captures data-access and data-sensitivity signals per agent and links data-governance evidence to controls in Comply. (Licensed standard — activated on demand.)
In practice, Maetra:
- Scans and fingerprints each agent. Discover reads the agent’s code — its tools, data access and sensitivity, actions, model, and environment — into an evidence-backed profile tied to the exact file and commit.
- Decides what applies. That profile determines whether ISO/IEC 5259 is in scope for the agent and which of its requirements apply.
- Auto-detects controls and surfaces gaps. Controls your code already satisfies are detected automatically from the scan; the rest become a clear list of gaps, each tied to the requirement and the evidence it still needs.
- Proves it and keeps it current. Close gaps with linked evidence or generated documents — reused across every framework the same control supports — and Maetra re-checks on each rescan and flags evidence that has gone stale.
Related frameworks
Prove ISO/IEC 5259 compliance with Maetra
Classify your AI agents once and Maetra maps them to ISO/IEC 5259 and every other framework it supports — generating the evidence and documentation, tracking gaps and deadlines, and sealing every decision in an immutable audit trail.