First, our solution applies ML to scan data sources to map sensitive data. Then, it parses the activity logs to analyze the activities. Then, it applies privacy engineering to identify and quantify privacy risks. Finally, it prioritizes data protection risks based on likelihood and impact.
Here are some of the benefits:
- Map your sensitive data -> Know what data you hold
- Identify teams and users using sensitive data -> Know who uses the data
- Find data tables that are the most vulnerable to breaches and privacy risks ->Prevent issues early
- Locate cross border activities -> Meet GDPR requirements for data transfers
- Find stale unused data assets that can be deleted or archived -> Minimize data and risk
- Audit access privileges to find data assets that have too broad -> Remove outdated/unused privileges
- Meet compliance CCPA/GDPR