Configuring LLM Data Masking Policies in Einstein AI

Protecting sensitive customer data is a critical requirement when integrating large language models into enterprise workflows. This course guides you through securing your generative AI applications using Einstein AI's native data masking capabilities. You will learn how to identify personally identifiable information (PII) and configure automated policies that mask sensitive records before they ever reach the LLM. By the end of this course, you will be able to confidently set up, test, and audit privacy-preserving data rules to maintain strict data compliance. What you'll learn: - Understand foundational LLM privacy concepts, including the Einstein Trust Layer and zero-data retention policies. - Configure data masking rules to automatically detect and obscure PII, PHI, and custom sensitive patterns. - Apply tokenization and masking techniques to maintain context while protecting user confidentiality. - Verify policy effectiveness by analyzing masked inputs and testing model responses. - Implement security best practices to align your generative AI pipelines with modern corporate compliance standards. The training begins with key terminology and the architecture of AI trust frameworks before moving into step-by-step configurations of masking rules and validation techniques. This text-based course is designed for administrators, data privacy officers, and AI implementers who are new to Einstein AI and want to secure their company's data. No prior programming experience is required. Start reading today to secure your AI-driven workflows and protect your enterprise data.