Use case
Every figure provable — whoever produced it
Evidence-backed lineage for BCBS 239, DORA, GDPR and the EU AI Act — for figures produced by people and by agents alike.
The situation
BCBS 239 expects demonstrable risk-data aggregation and lineage. DORA expects you to know your ICT dependencies. GDPR expects you to know where personal data lives and flows. The EU AI Act expects governed data behind AI. Each audit season, teams rebuild that evidence by hand — and it is stale before it is submitted. The ECB’s guide on risk data aggregation expects lineage at data-attribute level starting from data capture. In the large Nordic banks, data capture happens on the mainframe — which is exactly where most lineage tooling stops. And as agents start producing figures too, a supervisor’s question — how was this number made? — applies to their answers as well.
What Semantic Scout does about it
Lineage with evidence and change history
Column-level evidence of where every figure in a report comes from, each fact citing the file and line behind it, generated continuously rather than assembled in the weeks before an audit.
Automated PII classification
Personal data classified and linked to glossary terms automatically, so GDPR mapping falls out of normal operation instead of becoming its own project.
Views auditors can read
Business lineage that a risk officer or auditor follows without an engineer in the room, backed by the technical lineage underneath when they want to drill in.
Drift detection
Alerts when a flow, an owner, or a classification moves away from policy — before it reaches a report.
Proof
A Nordic bank licenses Semantic Scout for business lineage across mainframe and modern platforms.
“PII classification and linking assets with terms is now a breeze, we feel much more confident in our compliance efforts.”
Customer names are withheld by agreement. Roles and industries are accurate.
Other use cases
Context for modernisation
Lineage and meaning from mainframe code, so modernisation agents and engineers work from the same verified map — before, during and after migration.
Read moreContext for engineering agents
Coding and data-engineering agents see what a column means, what depends on it and what a change will break — across legacy and modern systems.
Read moreData stewardship
AI-assisted descriptions, tags and cross-platform lineage, so stewards review the context agents rely on instead of authoring it by hand.
Read more
Bring your hardest audit question
The useful demo is the one where you ask where a specific number came from, and we show you the path.