Yes, model choice matters. No, it is not enough.
The 2026 GenAI Code Security Report shows meaningful spread across leading AI models. But even the top performer does not generate securely enough to act as a control on its own. That shifts the conversation from tool preference to verification discipline.
For security leaders, the implication is clear: software trust in the AI-coding era depends on controls outside the model. That includes continuous verification before release, policy enforcement across teams, supply chain protections for dependencies, and faster remediation when flaws are found.
In this live webinar, get a break down of the strategic decisions security leaders should be making now:
- How to evaluate AI coding risk as part of enterprise risk management
- Where release-gate controls should tighten
- How to prioritize the risks that matter most and secure software faster
If AI-generated code is already inside your SDLC, this is the webinar to attend before the next policy review, audit cycle, or executive risk conversation.
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