Monitor and secure SaaS applications like ChatGPT with advanced risk categorization and real-time access control to manage how employees access generative AI applications.
Netskope DLP identifies flows of sensitive data with the highest level of precision, preventing any unsafe exposure on SaaS applications like ChatGPT, as well as on personal instances. Real-time coaching guides users to adopt safe business practices when interacting with sensitive data. With Netskope DLP, organizations can:
The Netskope SkopeAI for GenAI Hands-On Lab gives you a unique opportunity to learn how to detect new usage of GenAI apps such as ChatGPT, prevent accidental or intentional sensitive data exposures, and further enhance your security posture using the Netskope One platform.
Netskope provides automated tools for security teams to continuously monitor what applications (such as ChatGPT) corporate users attempt to access, how, when, from where, with what frequency etc.
With Netskope’s data loss prevention (DLP), powered by ML and AI models, thousands of file types, personally identifiable information, intellectual property (IP), financial records and other sensitive data are confidently identified and automatically protected from unwanted and non-compliant exposure.
Netskope detects and secures sensitive data in-motion, at-rest and in-use and through every possible user connection, in the office, in the datacenter, at home and on the road.
Netskope DLP offers several enforcement options to stop and limit the upload and posting of highly sensitive data through ChatGPT. This real-time enforcement applies to every user connection, ensuring data protection in the modern hybrid work environment where corporate users connect from the office, home, and while on the road.
Netskope provides automated tools for security teams to continuously monitor what applications (such as ChatGPT) corporate users attempt to access, how, when, from where, with what frequency etc.
With Netskope’s data loss prevention (DLP), powered by ML and AI models, thousands of file types, personally identifiable information, intellectual property (IP), financial records and other sensitive data are confidently identified and automatically protected from unwanted and non-compliant exposure.
Netskope detects and secures sensitive data in-motion, at-rest and in-use and through every possible user connection, in the office, in the datacenter, at home and on the road.
Netskope DLP offers several enforcement options to stop and limit the upload and posting of highly sensitive data through ChatGPT. This real-time enforcement applies to every user connection, ensuring data protection in the modern hybrid work environment where corporate users connect from the office, home, and while on the road.
Whenever possible, deploy AI models locally on your company’s machines. This eliminates the need for data to leave your company’s network, reducing the risk of data leakage.
Instruct corporate users to spend some time anonymizing or pseudonymizing sensitive data before utilizing it in AI models. This involves replacing identifiable data with artificial identifiers. Even if leaked, the data would be useless without the original identifiers.
Whenever possible, implement encryption both at rest and in transit for the most confidential corporate data. This ensures that even if the data is exposed, it remains unreadable without a decryption key.
Utilize robust access control mechanisms to corporate resources and data repositories to restrict interaction with AI models and the associated data.
Maintain detailed audit logs of all activities related.to data handling and AI model operations. These logs aid in identifying suspicious activities and serve as a reference for future investigations.
Train all employees to adhere to the principle of using the minimum amount of data necessary for effective functioning of the AI model. By limiting data exposure, the potential impact of a breach can be reduced.
Whenever possible, deploy AI models locally on your company’s machines. This eliminates the need for data to leave your company’s network, reducing the risk of data leakage.
Instruct corporate users to spend some time anonymizing or pseudonymizing sensitive data before utilizing it in AI models. This involves replacing identifiable data with artificial identifiers. Even if leaked, the data would be useless without the original identifiers.
Whenever possible, implement encryption both at rest and in transit for the most confidential corporate data. This ensures that even if the data is exposed, it remains unreadable without a decryption key.
Utilize robust access control mechanisms to corporate resources and data repositories to restrict interaction with AI models and the associated data.
Maintain detailed audit logs of all activities related.to data handling and AI model operations. These logs aid in identifying suspicious activities and serve as a reference for future investigations.
Train all employees to adhere to the principle of using the minimum amount of data necessary for effective functioning of the AI model. By limiting data exposure, the potential impact of a breach can be reduced.