What security measure must AI systems employ to protect data?

Encryption is a fundamental security measure that AI systems must employ to protect sensitive data. The process of encryption transforms data into a coded format that can only be read or decrypted by someone who possesses the correct key or password. This is essential in safeguarding information from unauthorized access and breaches, particularly when dealing with personal or confidential data used in AI training and operations.

Implementing encryption ensures that data remains secure as it is processed, stored, or transmitted. This is especially critical in AI systems, which often deal with large volumes of data that may include sensitive information about individuals or proprietary business data. By using encryption, organizations can comply with legal and regulatory requirements regarding data protection and maintain the trust of their users.

While anonymization, data cataloging, and bias mitigation are also important considerations in the governance of AI, they serve different purposes. Anonymization focuses on removing personally identifiable information to protect user identities, data cataloging assists in organizing and managing data assets, and bias mitigation aims to address fairness and equity in AI algorithms. However, these do not directly address the security aspect of safeguarding data against unauthorized access, a core function of encryption.