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--- ai_data_masking [2025/05/25 15:54] – [Extensibility and Advanced Use Cases] eagleeyenebula
+++ ai_data_masking [2025/05/25 16:03] (current) – [Basic Example] eagleeyenebula
@@ Line 85: / Line 85: @@
   * Returns the modified DataFrame with the specified columns masked.
-Example Workflow:
+**Example Workflow:**
   * Import the **DataMasking** class.
@@ Line 130: / Line 130: @@
 ==== Basic Example ====
-Mask specific columns using the default placeholder `"[MASKED]"`.
+Mask specific columns using the default placeholder **"[MASKED]"**.
 <code>
@@ Line 313: / Line 313: @@
 ===== Conclusion =====
-The **`ai_data_masking.py`** module provides fast, flexible, and secure masking capabilities for sensitive data. With its Pandas DataFrame integration, logging, and extensibility, this module is a powerful tool for ensuring data privacy in modern AI and data science workflows. Use it to safeguard sensitive columns and create secure datasets that meet the highest privacy standards.
+The **ai_data_masking.py** module provides fast, flexible, and secure masking capabilities for sensitive data. With its Pandas DataFrame integration, logging, and extensibility, this module is a powerful tool for ensuring data privacy in modern AI and data science workflows. Use it to safeguard sensitive columns and create secure datasets that meet the highest privacy standards.