What privacy risks do generative AI tools pose inside enterprises, and how can they be mitigated?

Generative AI tools pose several privacy risks in enterprises, including data leakage, retention, model memorization, and prompt injection. Sensitive data can unintentionally become accessible through these tools, leading to privacy violations and regulatory non-compliance^[6]. Employees may input confidential information, increasing risks of exposure and data breaches^[5].

To mitigate these risks, organizations can implement access controls to limit who can use sensitive data, conduct regular red-teaming to identify vulnerabilities, and consider on-premises deployment of AI tools to enhance security. Additionally, employing privacy-preserving techniques, such as data anonymization and encryption, further protects against unauthorized access^[6][5].