Software engineering constantly evolves, introducing new risks. We've shifted from mainframes to cloud, waterfall to agile. Now AI-generated code, LLMs and AI agents drive generational change in application creation.

Organizations face unprecedented challenges as AI transforms development through AI-accelerated coding assistants, AI-native machine learning integration and natural language software interactions. Traditional application security cannot address novel vulnerabilities like prompt injection, model jailbreaking and non-deterministic behavior in AI-native applications.

Security teams must evolve from reactive vulnerability management to creating policies for AI-accelerated workflows.

This session, led by Clinton Herget, field CTO at Snyk, will explore:

• How to assess AI trust readiness built upon mature DevSecOps foundations;
• Why novel AI-native vulnerabilities require new security approaches;
• Ways to implement governance frameworks addressing AI-accelerated and AI-native development risks.