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AI must be secured holistically within enterprises as employees leverage AI applications to enhance productivity. A significant percentage of employees use AI tools without IT's knowledge, leading to potential security risks. Securing AI by design is essential, involving visibility into application use, data protection measures, and threat management. Organizations face challenges such as unauthorized access to sensitive data through AI applications and the emerging risks associated with AI development. A platform-centric approach to network security will streamline protection across diverse applications, allowing organizations to capitalize on AI while minimizing security vulnerabilities.
AI is being integrated into enterprise security for customer environments.
Securing AI by design is crucial amidst rising application use.
Full visibility into AI application use is essential for organizational security.
Building AI-powered applications requires securing an entire AI ecosystem.
Organizations face risks from runtime threats, including prompt injection and data leakage.
The profound implications of shadow AI necessitate governance frameworks that prioritize transparency and accountability within organizations. With a significant number of employees using unapproved AI tools, organizations must implement policies that ensure compliance while fostering innovation. Adopting AI security by design allows organizations to mitigate risks associated with data breaches, emphasizing the importance of integrated oversight. For instance, companies like Palo Alto Networks have developed innovative solutions that enhance enterprise visibility, enabling better management of AI-related risks.
The rapid proliferation of AI applications introduces a complex threat landscape, demanding robust security measures. As organizations embrace AI to drive efficiency, they must simultaneously protect against vulnerabilities inherent in AI models. Strategies such as employing AI runtime security can safeguard against prompt injection attacks and data leakage, enhancing overall system resilience. Real-world examples illustrate attacks that exploit these weaknesses, underscoring the necessity for comprehensive security frameworks tailored to the unique challenges of AI technologies.
This presents risks involving sensitive data exposure and complicates organizational control over application use.
It ensures secure access to AI applications while protecting sensitive data and managing threats effectively.
Effective runtime security guards against various new threats unique to AI environments.
It focuses on securing enterprises against both traditional and emerging AI-specific threats.
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The partnership with Palo Alto Networks aims to build a reference architecture for securing AI applications in runtime.
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