Sensitivity Labelling
Propagating a classification through joins and derived tables so controls follow the data.
3 to work through
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intermediate
A design platform holds user-generated content, customer brand assets and internal data in one estate. How should sensitivity labelling work so it is actually applied?
1 min answer -
intermediate
An insurer must classify data sensitivity across a growing estate. What makes classification work rather than becoming a stale register?
2 min answer -
advanced
Your catalogue labels columns Public, Internal, Confidential and Restricted, and propagation takes the maximum label of all inputs. A team joins a Restricted table of health claims to an Internal table of postcodes and publishes weekly counts by postcode and condition. What does the label say, what should it say, and what goes wrong with the rule?
2 min answer
6 terms in this topic
Classification Propagation
Carrying a column's sensitivity classification through every derived table, view and export, so that copying restricted data into an aggregate does n…
practiceClassification Propagation
Carrying a sensitivity label through every derivation, so a restricted column copied into an aggregate, a training set or an export remains subject t…
practiceLabel Inheritance
Automatically assigning a derived dataset the highest sensitivity of its inputs, so that aggregates and transformations do not silently escape the co…
conceptOver-Classification
The state in which so much of an estate carries the highest sensitivity label that its controls become unworkable, and people route around them - pro…
practiceSensitivity Labelling
Attaching a machine-readable classification to data at the column or asset level, so that downstream controls can be applied automatically.
conceptSensitivity Propagation
The rule that a derived dataset inherits the highest sensitivity of its inputs unless a named transformation demonstrably lowers it.
Neighbouring topics
Data Governance & Semantics
General material on ownership, meaning, quality and control of data at enterprise scale.
Data Mesh
Domain ownership, data as a product, self-serve platform, and federated governance.
Data Products
A dataset with an owner, an interface, an SLO, and consumers who can rely on it.
Data Contracts
Producers committing to schema, semantics and freshness, and breaking builds when they do not.
Data Catalog
Discovery, ownership and technical metadata, and why catalogues go stale.
Business Glossary
Agreeing what a term means before arguing about which number is right.
Semantic Layer
Metric definitions held once and served to every tool that asks.
Master Data Management
One authoritative record for a customer or product across systems that each have their own.
Reference Data
Code lists, hierarchies and currencies — small, shared, and quietly load-bearing.
Data Quality Dimensions
Completeness, accuracy, timeliness, consistency, validity and uniqueness as testable claims.
Data Observability
Freshness, volume, schema and distribution monitoring for pipelines that fail silently.
Data Stewardship
The operating model that makes ownership a role rather than a slide.
Data Access Models
Role, attribute and purpose-based access over analytical data, and how they compose.
Row & Column-Level Security
Restricting slices of a table rather than the whole table, and where it is enforced.
Tokenisation & Masking
Dynamic masking, deterministic tokens, and preserving joinability without exposure.
Retention & Purge
Deleting from an append-only estate, and proving the deletion happened.
Data Sharing & Clean Rooms
Collaborating on data neither party may hand over, with computation as the interface.
BI Governance
Dashboard sprawl, certified reports, and the number the board is allowed to see.
Self-Service vs Governed
Letting analysts move fast without four teams reporting four different revenues.