The Future of Zero Trust and SASE is Now! Watch on-demand

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Safely enable the use of generative AI applications with application access control, real-time user coaching, and best-in-class data protection.

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Safely Enable ChatGPT and Generative AI
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How to Use a Magic Quadrant and Other Industry Research
In this episode Max Havey, Steve Riley and Mona Faulkner dissect the intricate process of creating a Magic Quadrant and why it's much more than just a chart.

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Safely Enable ChatGPT and Generative AI

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Generative AI SaaS applications, like ChatGPT, have revolutionized corporate workflows, but have also introduced significant data security risks. Now you can safely enable the use of generative AI applications with application access control, real-time user coaching, and best-in-class data protection.
Safely Enable ChatGPT and Generative AI
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Achieve full visibility and control of cloud applications across your organization

Monitor and secure SaaS applications like ChatGPT with advanced risk categorization and real-time access control to manage how employees access generative AI applications.

  • Discover usage of ChatGPT and other SaaS apps within the organization
  • Attain best-in-class app risk categorization via Cloud Confidence Index™ (CCI)
  • Leverage granular context and application instance awareness
  • Gain app usage, data movement, and user behavior insights via Advanced Analytics
  • Use real-time user coaching to enforce best practices in application usage
Netskope for Generative AI such as ChatGPT
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Ensure the confidentiality and security of sensitive data

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:

  • Achieve precise and automated discovery of sensitive data
  • Leverage the power of AI and ML for accurate data classification
  • Train your own classifier (TYOC) to enhance the system’s ability to recognize unique data sets
  • Implement real-time user coaching to guide and educate users on safe data handling practices
  • Prevent unauthorized cross-application data flows
Safely enabling ChatGPT
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Netskope secures sensitive data in the use of generative AI applications

Application access control

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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.

Advanced detection and safeguarding of sensitive data

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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.

Real-time data protection and automatic user coaching

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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.

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Key Statistics
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Use of generative AI applications like ChatGPT is exploding

100M+
ChatGPT users gained between February and April, 2023
Source: The Guardian
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Key Statistics
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Use of generative AI applications like ChatGPT is exploding

1.8B
ChatGPT visits over the past 30 days
Source: SimilarWeb
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Key Statistics
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Use of generative AI applications like ChatGPT is exploding

74%
of data theft flows into personal cloud storage instances of popular apps
Source: Netskope Cloud & Threat Report
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General precautions and security recommendations with the use of Generative AI applications

Local deployment

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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.

Data anonymization

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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.

Data encryption

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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.

Strict access control

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Utilize robust access control mechanisms to corporate resources and data repositories to restrict interaction with AI models and the associated data.

Audit trails

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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.

Data minimization

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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.

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