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Enterprises face the challenge of securing and managing the growing ecosystem of AI applications that handle sensitive data. A governance framework is essential for categorizing and securing these generative AI applications, enabling organizations to mitigate risks and ensure compliance. The article outlines various types of AI applications and their unique security implications, emphasizing the need for effective governance strategies.
Three main categories of generative AI applications are identified: web-based tools, embedded AI products, and integrated enterprise solutions. Each category presents distinct challenges regarding data privacy and security controls. By implementing a structured governance framework, organizations can better manage access, data sensitivity, regulatory compliance, and incident response related to AI technologies.
• Generative AI models like GPT-4 deliver significant enterprise value.
• A governance framework is crucial for managing AI-related risks.
Generative AI refers to models that can create content, such as text or images, based on input data.
Data sensitivity mapping involves classifying data to ensure sensitive information is not exposed to public AI models.
Incident response planning outlines protocols for addressing data leaks or security incidents related to AI applications.
OpenAI develops advanced AI models like GPT-4, which are widely used in various applications.
IBM offers AI solutions like Watson, which are integrated into enterprise products for specific business functions.
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