The Data Dilemma: Classifying the Chaos in an AI-Driven World

Every enterprise has built a "giant lake," but most have inadvertently created a digital wilderness. As data volume explodes, the tools we rely on are struggling to keep pace with the sheer speed of modern databases and the sprawl of SaaS-based information. Between the hidden risks of public LLMs like ChatGPT and Claude and the fragmentation of traditional hardware, IT leaders are facing a "perfect storm" of unclassified data and security vulnerabilities.

Join VAST Data and [Your Name/Company] for a candid discussion on moving past the branded storage arrays of the past. We will explore how to achieve true data classification at scale, the realities of protecting massive datasets, and how to build a unified data platform that eliminates the speed bottlenecks of legacy infrastructure.

Key Discussion Points:

The Unclassified Burden: How to gain visibility into the "wild" data lakes that currently lack structure.

The Speed Gap: Why traditional storage and database architectures are failing the requirements of modern scale.

SaaS & AI Risks: Identifying the data leakage points within the "AI SaaS" ecosystem and public bots.

Hardware Evolution: Why the industry is shifting away from traditional branded arrays toward software-defined, high-performance architectures.

Matthew Rogers

Field CTO, AI & Security | VAST Data

Matt Rogers is the Field CTO for AI and Security at VAST Data, where he drives innovation at the intersection of artificial intelligence and enterprise-grade security. He pioneers AI-driven video intelligence solutions, including the technology behind VAST Data’s partnership with the NHL for real-time game analytics.

He holds a patent for Agentic Video Search and specializes in AI vectorization of video and text using intelligent agents, enabling organizations to transform petabytes of unstructured video data into searchable, classified, and actionable intelligence. His work empowers enterprises to perform instant natural language searches across massive video datasets, detect threats in real-time streams, generate intelligent summaries from extensive footage, and enforce governance policies at scale.

Matt has engineered autonomous agentic AI pipelines that ingest live streams, detect key events, extract multimodal metadata, perform reasoning, and deliver sub-second search across massive datasets — embedding classification, governance, and secure access directly into the data plane.

He is also an active open-source contributor and the creator of rfhunter (github.com/RamboRogers/rfhunter), a security-focused project that reached #1 on Hacker News, reflecting strong technical community validation and practical impact.

With over 18 years of cybersecurity leadership experience spanning nuclear facilities to Fortune 500 enterprises, and as a CISSP-certified professional, Matt bridges advanced AI capability with stringent enterprise security requirements, helping organizations deploy intelligent systems securely and responsibly at scale.