SAP BTP AI Best Practices #2: Data Masking скачать в хорошем качестве

SAP BTP AI Best Practices #2: Data Masking

For more information: https://sap.to/60514D367 Description Data masking is a crucial technique used to protect sensitive information when interacting with Large Language Models (LLMs). It involves replacing identifiable or confidential data within prompts with placeholder text, ensuring that such information is not exposed to third-party models. This approach is particularly important for safeguarding personally identifiable information (PII) and other sensitive details. Advanced pattern matching and machine learning algorithms are used to identify sensitive data within prompts. Once identified, the sensitive data is replaced with generic placeholders, such as "Person_0" for names or "Organization_1" for company names. The masked prompt is then processed by the LLM without exposing the original sensitive information. After receiving the response from the LLM, the masked data can be reverted back to its original form if necessary, ensuring that any generated output remains relevant and accurate. AI Core supports data masking as a feature. Expected Outcome The expected outcome after implementing data masking is to ensure that sensitive information is protected and not exposed during data processing and analysis. This helps in maintaining data privacy and security, which is crucial for compliance with various regulations and standards. Benefits Protects Sensitive Information: Data masking ensures that personally identifiable information (PII), financial data, or any confidential business information isn’t exposed to the LLM. Prevents Data Leakage into Model Training: Even if the LLM operates in a stateless or non-retentive mode, some implementations may store prompts for debugging, analytics, or fine-tuning. Masking ensures that any accidentally stored data won’t include sensitive or proprietary information. Enables Safe Testing and Debugging: By using masked data, developers and data scientists can test prompts, fine-tune models, and debug workflows without risking exposure of real user data. Key Concepts Tokenization/Redaction: Replacing sensitive data (e.g., names, IDs) with placeholders before sending the prompt. Context Preservation: Ensuring the masked data maintains enough context for the LLM to generate meaningful responses. Reversible Mapping: Optionally mapping masked outputs back to real data after inference for post-processing or display.