Data masking explained: techniques, use cases, and limits
Data masking limits unnecessary exposure when teams copy sensitive production values into testing, analytics, and sharing workflows. It replaces those values with realistic substitutes, but weak implementations still permit inference, linkage, or reversal. Effective programs select techniques based on downstream use and test each output against its intended workflow.