AI Vulnerability Management: Why You Can't Patch a Neural Network

Jan 13, 2026

Join Sapna Paul, a Senior Manager at Dayforce with a robust background in cybersecurity and DevSecOps, as she unpacks the complexities of AI vulnerability management. Discover why traditional patching doesn’t apply to neural networks and delve into the three critical layers of AI vulnerabilities. Sapna highlights the importance of aligning AI risks with business goals and shares practical ways to use AI to combat alert fatigue. She also emphasizes mentoring and the essential skills needed for security professionals in an evolving AI landscape.

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INSIGHT

Vulnerability Management Reimagined

Vulnerability management's basic definition stays the same: find a flaw, patch it, verify the fix.
With AI you must observe, detect anomalies, and retrain models instead of one-off patching.

INSIGHT

The Asset Is A Learning Model

The asset has shifted from static software to learning systems like neural networks and models.
Teams must assess assets that change behavior over time and contain billions of parameters.

INSIGHT

Three Layers Of AI Risk

Think about AI vulnerabilities across three layers: model, data, and behavior.
Each layer needs distinct testing and controls like red‑teaming, data governance, and behavior monitoring.

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