The best course of action to prevent false positives from data loss prevention (DLP) tools is to re-

establish baselines for configuration rules. False positives are events that are triggered by a DLP

policy in error, meaning that the policy has mistakenly identified non-sensitive data as sensitive or

blocked legitimate actions. False positives can reduce the effectiveness and efficiency of DLP tools by

generating unnecessary alerts, wasting resources, disrupting workflows, and creating user

frustration. To avoid false positives, DLP tools need to have accurate and updated configuration rules

that define what constitutes sensitive data and what actions are allowed or prohibited. Configuration

rules should be based on clear and consistent criteria, such as data classification levels, data sources,

data destinations, data formats, data patterns, user roles, user behaviors, etc. Configuration rules

should also be regularly reviewed and adjusted to reflect changes in business needs, regulatory

requirements, or threat landscape.

Conducting additional discovery scans, suppressing the alerts generating the false positives, or

evaluating new DLP tools are not the best ways to prevent false positives from DLP tools. Conducting

additional discovery scans may help identify more sensitive data in the network, but it does not

address the root cause of false positives, which is the misconfiguration of DLP policies. Suppressing

the alerts generating the false positives may reduce the noise and annoyance caused by false

positives, but it does not solve the problem of inaccurate or outdated DLP policies. Evaluating new

DLP tools may offer some advantages in terms of features or performance, but it does not guarantee

that false positives will be eliminated or reduced without proper configuration and tuning of DLP

policies.

Reference: False Positives Handling| Endpoint Data Loss Prevention - ManageEngine …, Scenario-

based troubleshooting guide - DLP Issues, Respond to a DLP policy violation in Power BI - Power BI