The article also highlights the risk of inbound threats, where attackers could compromise genAI applications to deliver dangerous payloads back to the enterprise. Despite these risks, the author argues that genAI apps do not require new security tools, but rather the extension of existing security best practices. The author encourages embracing genAI while applying the same common sense used with any new tool to ensure data safety.
Key takeaways:
- Generative AI (genAI) applications are open by default, making them risky for sensitive information as they retain a history of user interactions for training purposes.
- Companies can mitigate the risks of sensitive information appearing in public genAI applications by controlling access to the data supplied to genAI apps, controlling genAI application access and permissions, and gaining visibility with inline data loss prevention (DLP).
- Attackers can compromise genAI applications to deliver dangerous payloads back to the enterprise, posing a significant inbound threat.
- Security leaders can reduce their genAI risk exposure by employing existing security techniques, tools and knowledge, without the need for new security tools specifically for genAI.