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About this Conversation:
Security teams are not short on findings—they are short on confidence in what matters. Teams need to move beyond raw vulnerability counts and toward a clearer understanding of exposure. That means improving risk scoring by factoring in exploitability, business context, and how assets are actually used.
This session examines how organizations can shift from reactive to more informed decision-making. By leveraging AI Agents to look at patterns and fill in the missing data, teams can prioritize with greater precision. The outcome is a move away from broad, unstructured remediation requests toward targeted actions that align with business priorities and operational constraints.
The discussion also challenges a common assumption: that organizations must fully organize and classify their data before making progress. In reality, the volume and growth of data make that approach difficult to sustain. AI can help accelerate discovery and classification, enabling teams to improve data quality while simultaneously acting on what matters most.
Key Takeaways:
- How to distinguish “vulnerable” from truly “exposed” assets
- Why predictive context improves prioritization and decision-making
- How to align remediation with business criticality
- Why waiting for perfect data limits progress—and how to work in parallel
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