Netskope Integrates With OpenAI’s ChatGPT Enterprise To Improve Data Governance, Compliance
Netskope recently reported that gen AI application usage among users more than tripled since this time last year.
Netskope, a provider of secure access service edge, has integrated with OpenAI’s ChatGPT Enterprise Compliance API to deliver API-enabled controls that bolster security and compliance for enterprises using generative artificial intelligence applications.
Through this integration with the ChatGPT Enterprise, the Netskope One platform will provide organisations with enhanced security features such as application visibility, policy enforcement, data security and security posture management, the company said in a press release.
Netskope recently reported that gen AI application usage among users more than tripled since this time last year. While the average activity per gen AI user has also doubled, maintaining compliance standards, mitigating data policy violations and helping support secure usage of gen AI applications, such as ChatGPT Enterprise, is increasingly more critical.
Netskope CASB API protection leverages APIs available from major vendors, such as Box, Google Workspace, and Microsoft 365, to provide visibility into settings and data residing in the cloud service, enforcing policies to control access and protect data.
“By integrating the Netskope One platform into OpenAI’s advanced capabilities in ChatGPT Enterprise, Netskope continues to lead in providing comprehensive security solutions for enterprises adopting gen AI tools,” said Andy Horwitz, senior vice president of Global Partner Ecosystem at Netskope.
According to Netskope, its integration with ChatGPT Enterprise will enable enterprises to:
Adhere To Compliance Standards: With various compliance templates and data identifiers, organisations can enforce data loss prevention and compliance policies around sensitive data to support meeting compliance regulations.
Advance Detection And Safeguard Sensitive Data: Out-of-band visibility and control help protect sensitive information such as personal identifiable information and intellectual property. In addition, continuous data scanning identifies and takes action on sensitive data leakage in near real-time, and users can leverage DLP techniques, including machine learning and optical character recognition, to find sensitive information.
Protect Against Threats: Advanced ML models for malware detection complement more traditional signatures, heuristics methods and sandboxing techniques, further remediating risks by identifying potential threats in near real-time.
