Security professionals installed safeguards, thinking they contained Generative AI by blocking direct employee access to large language models. The documented reality, shared here by AIceberg's Alex Schlager, is far more concerning: personnel are sidestepping these blocks by copying proprietary corporate information, emailing it to private accounts, and then feeding it to the LLMs. This simple email workaround reveals the gap in perimeter security, prompting a pivot toward advanced scanning and Data Loss Prevention (DLP) solutions. Yet, the bottleneck remains the monumental effort required for companies to properly sort, index, and label all their sensitive data for precise DLP application. A pragmatic alternative is emerging—off-the-shelf tools offering a general indication of data sensitivity, even if they lack the granular accuracy of perfectly labeled systems.
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