AI Security Gap: Are You Protected?

The rapid evolution of artificial intelligence has outpaced the development of corresponding security protocols. Many organizations are still applying outdated security models to AI, creating a significant vulnerability during the runtime phase when AI systems are actively processing data.

Traditional enterprise security primarily focuses on data at rest and in transit. However, AI introduces a complex third state: data in use. During runtime, sensitive data, model weights, and generated outputs are processed in memory, often becoming visible to the underlying system. This oversight leaves valuable intellectual property and sensitive information exposed.

Security issues in AI typically emerge during training, inference, and runtime. Training can lead to unintentional data leakage into models, while inference involves multiple streams of sensitive data being processed. The runtime phase, where models execute and data is decrypted, remains the most critical yet least protected, acting as the true security boundary for AI systems.

As AI adoption scales, risks multiply due to distributed environments and continuous operation. This compounds the challenge by increasing data flow and deployment scope. Protecting AI requires a shift from assumption-based trust to proof-based trust, ensuring data remains protected during processing and workloads run in verified secure environments.