A company needs to implement Data Owner Exception so that incidents are avoided when employees send or receive their own personal information. What detection method should the company use?

A company needs to implement Data Owner Exception so that incidents are avoided when employees send or receive their own personal information. What detection method should the company use?

• A. Exact Data Matching
• B. Pattern Matching
• C. Keyword Search
• D. Anomaly Detection

Answer: (A)

Data Owner Exception hinges on recognizing the exact pieces of data that belong to a specific owner so that those items can pass without triggering DLP actions. Exact Data Matching is the right approach because it fingerprints and looks for precise, predefined data values—the exact personal data items of the owner, such as a unique employee identifier or a specific piece of personal information. When those exact values are detected, the policy can be configured to allow the transmission, avoiding incidents for the owner’s own data. Pattern Matching, while useful for detecting formats like SSNs or credit card numbers, would flag or block data based on general patterns rather than the exact items owned by the individual, making it less precise for owner-specific exceptions. Keyword Search relies on specific words, which is also too broad and easily misses or misclassifies data. Anomaly Detection focuses on unusual behavior rather than enforcing an allowed-list for known owner data, leading to inconsistent results.

Data Owner Exception hinges on recognizing the exact pieces of data that belong to a specific owner so that those items can pass without triggering DLP actions. Exact Data Matching is the right approach because it fingerprints and looks for precise, predefined data values—the exact personal data items of the owner, such as a unique employee identifier or a specific piece of personal information. When those exact values are detected, the policy can be configured to allow the transmission, avoiding incidents for the owner’s own data.

Pattern Matching, while useful for detecting formats like SSNs or credit card numbers, would flag or block data based on general patterns rather than the exact items owned by the individual, making it less precise for owner-specific exceptions. Keyword Search relies on specific words, which is also too broad and easily misses or misclassifies data. Anomaly Detection focuses on unusual behavior rather than enforcing an allowed-list for known owner data, leading to inconsistent results.