It is easy to forget that the amount of modeling magic cannot compensate for low quality data in the generated AI gold rush. When companies compete to deploy artificial intelligence, they often overlook the unattractive but completely important tasks, securing the basis for data, governing and preparing them. That omission is more than operational monitoring. That's a strategic responsibility, especially when it comes to data protection for AI.
“The lifeline of AI is data,” says Scott Hebner (photo, right) of TheCube Research. “There is no AI without an information architecture. Data is a critical component of an AI system, so data must be of the highest quality. How it is processed, how it is used, and most importantly, it must be protected.
Hebner spoke with TheCube's Christophe Bertrand (left) during an exclusive broadcast on TheCube, the LiveStreaming Studio of Siliconangle Media. In their discussion, we investigated the data trust gap, the evolving risk environment around agent AI, and the fundamental role of protection in building resilience and intelligent AI infrastructure.
Why AI data protection is a limiting factor
Most enterprise data is unprotected and unprepared for AI. According to Hebner, much of enterprise data is effectively on the sidelines due to risk concerns and lack of governance. Without the strong data protection of AI, even advanced models can work with narrow slices of available information, limiting both quality and reliability.
“If you look at the majority of the data your organization has posted, it's rare that your own enterprise data is actually not actually used, but it's used a little deeper in these AI projects. Enterprise data is simply unprepared and protection is a big part of that.”
Effective protection not only makes your data safe, but it is ready for AI use and mass reuse. Therefore, according to Bertrand, AI data protection must be treated as a design requirement and after an after-installation.
“AI is important to you as a workflow. It's important to you as a workload and it's important to you as a business,” Bertrand said. “For this reason, anything that is part of the AI infrastructure must be protected. I think that's the baseline. Don't think about it again. It's actually a design requirement.”
Governance needs to stay paced as AI agents and automation generate new data at unprecedented speeds. According to Hebner, AI data protection plays a crucial role in that process, ensuring continuity, integrity and trust as systems evolve.
“I think [that AI] You learn from that data and start to understand what is really high quality data, what is protected, what isn't,” he said. As the system is built over time, it becomes better to what it does. Building on AI… you may wonder if a framework that includes governance and trust management, protection and regulatory compliance, and corporate policy will really be… table stakes. That's what you have to do. Otherwise, how do you follow all of this? ”
This is a complete video interview that is part of Siliconangle and part of TheCube's reporting on Data Protection & AI Summit.
https://www.youtube.com/watch?v=zupusndyqfc
Photo: Silicon Angle
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SilicOnangle Media is a leader in digital media innovation serving innovative audiences and brands, bringing cutting-edge technology, impactful content, strategic insights and real-time audience engagement. As the parent company of Silicon angle, TheCube Network, Research on TheCube, Cube365, TheCube AI TheCube Superstudios, including those founded in Silicon Valley and the New York Stock Exchange (NYSE). It works at the intersection of media, technology, and AI. .
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