An organization's research department plans to apply machine learning algorithms on a large data set containing customer names and purchase history. The risk of personal data leakage is considered…

Accepting high-impact personal data leakage risk is generally irresponsible and unethical, especially when effective mitigation strategies exist.

Applying anonymization on the data set is the best risk mitigation strategy because it transforms personal data into a format where individual identities cannot be determined, thus significantly reducing the risk of personal data leakage while still allowing the data to be used for machine learning analysis. This method directly addresses the "high impact" of personal data leakage by making the leaked data less sensitive.

Transferring risk through insurance doesn't prevent the actual data leakage or its immediate consequences; it only covers the financial fallout.

Encryting customer names protects the data at rest or in transit but does not mitigate the risk of leakage during processing when the data must be decrypted for machine learning algorithms to operate, or if the decryption key is compromised.