When you start using DLP policy templates for the first time, it's likely that you'll run into situations where a high number of false positives come up, depending on the locale, as well as the data types you use frequently in your organization. In these situations, you can modify the DLP policies or create replacement policies to lessen the likelihood of locking people out or improperly retaining or deleting data.
If you're modifying a policy, you may consider adjusting the following properties:
- Confidence level (perhaps instead of 65%, you'll go with 80% confidence on credit card numbers)
- Sensitive data types and labels – perhaps a specific type of data is constantly being flagged but incorrectly
- The number of instances of a data type that's needed before a policy is triggered
- Override ability
- Policy tips
- Restrict (or...
