A regional bank's fraud detection team trains a model on years of customer transaction history. Before any engineer can query the training data, the security team requires the data set to be encrypted at rest, restricts access to a named list of approved data scientists, and logs every query run against the data for later audit. Which Responsible AI principle is the security team primarily enforcing?

The principle being enforced is privacy and security. Encrypting the transaction data at rest, limiting who can query it to a named list of approved data scientists, and logging every access for audit are all controls aimed at keeping sensitive customer financial data safe from unauthorized access and misuse, which is the definition of the privacy and security principle. Transparency, explaining to customers how the fraud model reaches a decision, is incorrect because transparency is about disclosing how the fraud model reaches its decisions to the people affected by them, not about controlling who can access the underlying data set. Accountability, designating a governance body that answers for the model's outcomes, is incorrect because accountability concerns having identifiable people or a governance body answerable for the model's behavior and outcomes, whereas the measures described here are technical access controls over data, not an oversight structure. Reliability and safety, ensuring the model performs consistently once deployed, is incorrect because reliability and safety addresses the model performing consistently and predictably in production, which is a separate concern from protecting the data used to train it. The scenario describes encryption, restricted access, and audit logging, all of which map directly to safeguarding sensitive information rather than any of the other three principles.