Why verification outside the model matters more as AI-generated code scales

GenAI performs well on some vulnerabilities and horrendously on others. That matters for verification, release gating and secure delivery.

Webinar   AI Code Risk Is Not Uniform   The 2026 findings show where AI performs better, where it struggles, and where stronger verification matters most.   Register Now     The headline number matters. The blind spots matter even more. In the 2026 GenAI Code Security Report, AI-generated code shows major variation by vulnerability class. Some issues are handled relatively well. Others remain persistent weak points. That means the real control question is no longer “Do we allow AI- generated code?” It is “Where do we require independent verification, and how do we enforce it consistently?” This is especially relevant for security leaders balancing secure delivery with regulatory accountability. If your development workflows assume the model is the control, your risk posture is built on weak assumptions. Join this live webinar to learn how the findings should influence: Control design for AI-assisted development Release-gate policies for high-risk code paths Prioritization for the classes most likely to evade model reasoning Alignment across security, engineering, risk, and compliance We will also discuss how stronger visibility, faster remediation, and software supply chain protections help organizations manage AI-driven development with more confidence and less friction.     Register Now       36 Queen Street London, EC4R 1BN United Kingdom     emea@veracode.com      +44 (0)20 3761 5501 © 2026 Veracode Inc. All rights reserved. Unsubscribe  |  Manage preference