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Policy tips with entity condition results in "no match"
Asian language support for person name (Chinese, Japanese, Korean)
Named entities supported for Latin-based character set only (that is, kanji isn't supported) for person name
On-premises repositories
Not supported as a workload
Power BI (preview)
Not supported
Best practices for using named entity SITs
Here are some practices you can use when you create or edit a policy that uses a named entity SIT.
Use low instance counts (three to five) when you're looking for data that's in a spreadsheet and the keyword that's required by the SIT for that data is only in the column header. For example, let's say you're looking for US Social Security numbers, and the keyword Social Security Number only occurs in the column header. Since the values (the corroborative evidence) are in the cells below, it's likely that only the first few instances would be in close enough proximity to the keyword to be detected.
If you're using a named entity SIT, like All Full Names, to help find US Social Security numbers, use larger instance counts such as 10 or 50. Then, when both the person names and the SSNs are detected together, you're more likely to get true positives.
You can use Autolabeling simulations to test the accuracy of named entity SITs. Run a simulation using a named entity SIT to see what items match the policy. With this information, you can fine tune accuracy by adjusting the instance counts and confidence levels in your custom policies or the enhanced template conditions. You can iterate simulations until the accuracy is where you want it before deploying a DLP or autolabeling policy containing named entities in production. Here's an overview of the flow:
Identify the SIT or combination of SITs you want to test in simulation mode, either custom or cloned and edited
Identify or create a sensitivity label to be applied when the autolabeling policy finds a match in Exchange, SharePoint sites, or OneDrive accounts
Create a sensitivity autolabeling policy that uses the SIT from step 1 and with same Conditions and Exceptions that are used in your DLP policy
Run the policy simulation
View the results
Tune the SIT or policy and the instance count and confidence levels to reduce false positives.
Repeat until you get the accuracy results you want
This module examines the data loss prevention features in Microsoft 365 that help organizations identify, monitor, report, and protect sensitive data through deep content analysis while helping users understand and manage data risks.