The acceptable level of PII reduction accuracy in an AI pipeline depends on various factors, including the specific use case, industry standards, regulatory requirements, and the sensitivity of the data involved. Generally, a higher level of accuracy is desirable to minimize the risk of exposure of sensitive information. However, it's essential to balance accuracy with other considerations such as performance, efficiency, and usability. Here are some factors to consider when determining acceptable PII reduction accuracy:
1. Regulatory requirements and industry standards: If your AI pipeline deals with PII, it must comply with relevant data protection regulations such as GDPR, CCPA, HIPAA, etc. These regulations may specify certain standards or requirements for the handling and protection of PII. Some industries may have established best practices or standards for handling sensitive data, including PII. Compliance with these regulations and standards may therefore necessitate a higher level of accuracy in PII reduction.
2. Risk assessment: It’s a good practice to conduct a risk assessment to evaluate the potential consequences of inaccuracies in PII reduction. Consider the impact on individuals' privacy, the organization's reputation, legal liabilities, and the likelihood of data breaches or misuse.
4. Data sensitivity: The sensitivity of the PII involved should also influence the acceptable level of accuracy. Highly sensitive information, such as financial data or health records, may require a higher degree of accuracy compared to less sensitive information like public contact details.
5. Performance trade-offs: Achieving higher accuracy in PII reduction may come with trade-offs in terms of computational resources, processing time, and system complexity. Consider the practical limitations and performance requirements of your AI pipeline.
6. User expectations: Understand the expectations of your users regarding the protection of their PII. Strive to meet or exceed these expectations to maintain trust and confidence in your AI application.
As with many topics in MLOps, there is no one-size-fits-all answer to what constitutes acceptable PII reduction accuracy. It's essential to assess the specific context, risks, and requirements of your AI pipeline and strive to achieve a balance between accuracy, performance, compliance, and user expectations.
MLRun provides a function for PII removal that uses deep learning and NLP models to perform actions like entity detection. This goes beyond regex, since PII detection requires understanding the context of the word in the sentence for accurately identifying, for example, a name or an email address. The function substitutes them with an anonymized version.
Read more about the function here, and check it out on github here.