Discover why AI-native security is essential for protecting AI systems, with insights on real-time threat detection, data protection, and lessons from cloud security.
From generative models to agentic AI, artificial intelligence promises to drive innovation across industries. However, as organizations increasingly integrate AI into their operations, a critical challenge arises: how do you secure something as dynamic, autonomous, and complex as AI?
The core challenge is maintaining control over what AI systems are doing and ensuring that sensitive data doesn’t leave the organization in the process.AI technologies constantly evolve, which is part of their value—but also part of their risk. Traditional security tools, which are designed for static environments, simply don’t have the capability to monitor AI systems’ behaviors effectively. These tools are unable to track how AI models learn, interact with data, or adapt to new inputs.
Khan drew a direct parallel between the early days of cloud security and the current state of AI security: “We are not creating a new market; we are solving the same problem that organizations faced 20 years ago with the internet. The only difference is that now we are dealing with AI applications that need to be understood in an entirely different way. It’s not just about blocking bad actors, it’s about understanding the specific use cases, interactions, and data flows of AI systems.
Aurascape AI Security AI-Native Security AI-Native Security Solutions Securing AI Applications AI Security Platform Real-Time AI Threat Detection Protecting AI-Driven Systems
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