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The article addresses how generative AI agents and probabilistic models introduce unique vulnerabilities like prompt injection and autonomous decision-making risks that traditional cybersecurity frameworks cannot handle. This shift necessitates a move from post-deployment security to building data protection directly into the AI infrastructure itself. Consequently, enterprise defense strategies must evolve to secure the entire "AI factory" lifecycle where data is continuously generated and consumed.
AI attack surface reshapes enterprise security
Artificial intelligence is making companies into factories of intelligence. As AI becomes a new workload, it has also become a new attack surface. The factory model that enterprises are building to produce knowledge at scale was not designed with security at its center. Energy, compute and data go in. Intelligence in the form of tokens comes out. But so do new risks that traditional cybersecurity was never built to handle. “AI factories introduce a new class of risks that extend beyond traditional cybersecurity models. In this world, data is not static, bounded or easily classified. Rather it is continuously generated, transformed and consumed across distributed environments,” said Dave Vellante, chief analyst at theCUBE Research, in a recent analysis. Models are supported by deterministic systems that behave predictably, but they are also adaptive, probabilistic engines that evolve over time, Vellante explained: “Agents increasingly act autonomously, interacting with enterprise systems, executing workflows and making decisions with limited – or sometimes no – human intervention.” This feature is part of SiliconANGLE Media’s exploration of…
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