AI initiatives stall when teams can’t answer basic questions about data sensitivity, access, and movement. Data discovery and classification are a strong start, but they don’t explain how data is used, where it flows, and what risks get created across AI systems. This white paper shows how contextual data and AI intelligence helps teams make faster, safer decisions.
73%
of organizations cite data privacy and security as their top AI risks.1
65%
of shadow AI security incidents involve compromised customer PII.2
32%
of companies effectively use data to drive business value.3
This white paper explains why classification alone is not enough for safe, scalable AI. You’ll learn what “context” really means, why tool silos slow investigations, and how to connect the insights security, privacy, and governance teams need to act with confidence.
What you’ll learn:
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How to answer the core questions fast: What sensitive data you have, who can access it, where it moves, and whether it is being used by AI agents.
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What “context” adds beyond classification: Data flows, access and entitlements, security posture findings, cross-border movement, AI usage, and regulatory obligations, all tied together for quicker decisions.
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How to operationalize investigations and controls: Practical strategies to proactively address AI data risks, assess privacy impacts, and reduce manual effort to enforce policies more effectively.
If you want to scale AI agents without adding risk, you need context, not more manual work. Download the white paper to learn how to connect data, access, and AI usage insights, so you can move faster, reduce blind spots, and enforce the right controls with confidence.
1 Deloitte AI Institute, 2026
2 Ponemon Institute research, 2025
3 New Vantage Partners, 2023