What this type of data looks like
The core data for data masking processing comes from business databases, data dictionaries, masking tool operation logs, and internal enterprise access approval systems. Data is updated in project batches or single tasks. Each batch of data covers the dataset to be masked, sensitive field mapping rules, masking verification results, and approval vouchers. Daily output documents include masking solution documents, field mapping tables, masking verification reports, and approval records. Fields typically include sensitive field identifiers, masking rule types, execution times, and compliance check statuses. There are no unified fixed units, and they adjust based on data type.
What constraints do these characteristics impose on deployment and upgrade
Since data is updated in project batches and linked to approval records, deployment must support isolated configuration per project to avoid cross-project data confusion. Data sources include multi-system interfaces, so upgrades must be compatible with different versions of databases and data dictionary field formats to prevent parsing failures. Masking verification reports and approval records require persistent storage, so sufficient storage capacity must be configured during deployment. Additionally, configurations for sensitive field identifiers and rules must be fully retained after upgrades to avoid compliance risks caused by resetting. The requirement for multi-source data access also demands flexible data source adaptation parameters during deployment, to adapt to system interface formats across different environments and ensure stable data collection.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
MONGODB_URI | Point to the MongoDB cluster connection string containing the tmbId field | Used to store masking task records and historical version data, compatible with the new field format of FastGPT v4.17.0 |
REDIS_URL | Configure as the connection address of a local Redis instance on port 6379, with a memory quota set to 2000 MB | Caches masking rules and sensitive field mappings to reduce overhead from repeated database reads |
STORAGE_S3_ENDPOINT | Fill in according to the object storage service address of the deployment environment | Used to persistently store masking verification reports and approval records, ensuring compliant data is traceable |
ROOT_KEY | Generate a random 32-bit string as the secret key | Used to encrypt sensitive field mapping configurations to prevent compliance risks from configuration file leaks |
DB_MAX_LINK | Configure between 20–30 | Limit the number of database connections to avoid occupying excessive cluster resources during concurrent multiple masking tasks |
Three common mistakes
- Knowledge base queries return unorganized raw data, and model outputs are chaotic. The cause is that the
tmbIdfield of the masking configuration in the database pointed to byMONGODB_URIwas not updated synchronously after the upgrade, leading to failed rule loading. - Some documents in the application historical version list lack the
tmbIdfield. The cause is that the field completion script was not executed for legacy historical data during deployment, and legacy format data was not compatible after the upgrade. - Custom models perform inconsistently in conversations and workspaces. The cause is that masking configurations were not synchronized to the independent storage nodes of the workspace, leading to different rule loading statuses across scenarios.
How to confirm configurations are properly set
- Run the
docker logs fastgptcommand to view the masking service startup logs, and confirm there are no database connection failures or configuration parsing errors. - Upload a data set containing test sensitive fields, run a masking verification task, and check whether complete masking verification reports and approval records are generated.
- Log in to the database client, query the collection corresponding to
MONGODB_URI, and confirm that thetmbIdfield exists in the historical version data. - Access the configured
FE_DOMAINaddress, test the saving and loading functions of masking rules, and confirm that configurations take effect normally.
The values provided are common starting points and should be evaluated against the samples of the target deployment environment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.