"Generative AI has gone from a curiosity to a cornerstone of enterprise productivity in just a few short years. From copilots embedded in office suites to dedicated large language model (LLM) platforms, employees now rely on these tools to code, analyze, draft, and decide. But for CISOs and security architects, the very speed of adoption has created a paradox: the more powerful the tools, the more porous the enterprise boundary becomes."
"On paper, this seems to offer clarity. In practice, it muddies the waters. The truth is that most legacy architectures, designed for file transfers, email, or network gateways, cannot meaningfully inspect or control what happens when a user pastes sensitive code into a chatbot, or uploads a dataset to a personal AI tool. Evaluating solutions through the lens of yesterday's risks is what leads many organizations to buy shelfware."
Generative AI adoption has accelerated across enterprises, with employees using copilots and LLM platforms for coding, analysis, drafting, and decision-making. Rapid adoption increases the permeability of enterprise boundaries and creates a security paradox for CISOs and security architects. The primary risk stems from applying legacy mental models and controls to novel AI risk surfaces they were not designed to cover. The AI data security market is crowded and often confusing as vendors rebrand existing solutions as AI security. Legacy architectures built for file transfers, email, and network gateways cannot inspect or control user actions in browser-based AI tools. Buyer evaluation must prioritize last-mile, browser-native protections rather than legacy feature checklists.
Read at The Hacker News
Unable to calculate read time
Collection
[
|
...
]