I am looking for best-practice advice on how to tune our inline Real-Time Protection policies to eliminate high-volume DLP false positives caused by automated tracking and analytics scripts logging standard website content.
Because the script scrapes raw elements directly from the page framework, the payload packages underlying site styling code and public reference strings side-by-side. Our Predefined PII rule triggers on a standard threshold count, but a single tracking transaction easily multiplies that count and floods our incident logs because the inspection engine captures things like
- Raw CSS Styling Frameworks: The payload packages background font layout code from icon libraries (e.g.,
.fa-square-1{--fa:"\\e256"} ... .fa-birthday-cake,.fa-cake). - 75\"}.fa-square-1{--fa:\"\\e256\";--fa--fa:\"\\e256\\e256\"}.fa- birthday -cake,.fa-cake,.fa-cake-candles{--fa:\"\\f1fd\";--fa--fa:\"\\f1f ...Kolodzey, Evan Hughes\",\"Publication date\":\"2026- 07-10 \",\"Name
- Public Directory / Author Metadata: It captures names of public researchers, book authors, and standard catalog publication dates listed on the article page (e.g.,
\"Authors\":\"[Author Name]\"or listed publication fields). - User Account Info: The active employee's name and email address scraped from the logged-in portal header.
How are you isolating and bypassing these background analytics payloads?
Has anyone successfully used Advanced DLP Expressions (like an AND NOT statement) to ignore strings when contained within explicit JSON tracking schemas or alongside CSS selectors?
Thanks in advance.
