Data Security Posture Management (DSPM)

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    • Shadow Data, AI Pipelines, and the 802,000 Files You’re Oversharing Right Now

    The average organization has more than 800,000 data files at risk from oversharing, erroneous access permissions, and inappropriate classification. That number is climbing as AI pipelines generate and ingest data faster than any manual classification effort can keep up. Half of all enterprise workloads are now cloud-based, and the rise of AI is accelerating data creation without guardrails or oversight. The result is shadow data: sensitive information scattered across environments that security teams cannot see, classify, or protect.

    Traditional data security strategies assume that most data lives in known locations with defined access controls. That assumption broke years ago. Today, 90% of business-critical documents are shared outside the C-suite, AI models are training on datasets that may contain PII or intellectual property, and unstructured content is multiplying across SaaS, cloud storage, and collaboration platforms.

    Regaining control requires visibility and coordination across data discovery, classification, access governance, and data protection controls – from DSPM and DLP to SaaS security and AI data governance.

    Topics include:

    • Discovering and classifying sensitive data across cloud, SaaS, and AI environments
    • Addressing the shadow data problem created by AI-driven data proliferation
    • Reducing oversharing risk through automated access governance and posture management

    Join us to learn how organizations are regaining visibility and control over data they did not know they were exposing.

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    • AI Models Are Trained on Your Sensitive Data. Who's Watching What Goes In and What Comes Out?

    Three-quarters of organizations now run AI in production environments. Ninety-nine percent reported at least one attack on their AI systems within the past year. The data flowing into and out of these models, training datasets, fine-tuning inputs, prompt histories, and generated outputs, represents a category of data exposure that most security programs were never built to monitor. When a large language model is fine-tuned on customer records, internal strategy documents, or proprietary code, the question of who can access what the model learned becomes urgent.

    AI pipelines create data exfiltration paths that blend into legitimate business operations. A model query that returns sensitive information is not a traditional data breach, but the impact can be identical. Addressing this requires coordination across DSPM, DLP, SaaS security, cloud security, and AI data governance platforms to build visibility into what data feeds AI systems, what those systems can reveal through inference, and whether access controls exist at each stage of the pipeline. The data security perimeter has expanded, and the tools designed to protect structured databases and file shares are not sufficient for the unstructured, dynamic data flows that AI depends on.

    Topics include:

    • Gaining visibility into what sensitive data enters AI training pipelines and inference endpoints
    • Extending data loss prevention and SaaS security strategies to cover AI-specific exfiltration vectors
    • Building governance frameworks for AI data flows across development, staging, and production

    Discover how organizations are closing the data security gap created by enterprise AI adoption before it becomes their next breach headline.

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