Webinar Description
Key Takeaways
- Examines security vulnerabilities in AI-generated code, with research indicating an average of 15 vulnerabilities per codebase.
- Features insights from Kawin Boonyapredee (CISO Advisor, KnowBe4) and Pieter Danhieux (CEO, Secure Code Warrior).
- Covers benchmarking of AI coding models using the SCW AI Trust Index.
- Addresses regulatory gaps affecting AI adoption in banking, financial services, and healthcare.
- Designed for security leaders, engineering teams, and developers working with AI coding tools.
About the Event
AI in the SDLC: Separating the Hype from the Security Reality is a webinar presented in partnership with KnowBe4 that examines the security implications of integrating artificial intelligence into software development workflows. The session brings together Kawin Boonyapredee, CISO Advisor at KnowBe4, and Pieter Danhieux, CEO of Secure Code Warrior, to discuss research findings and practical approaches to managing AI-related security risks.
Security Risks in AI-Generated Code
The webinar addresses vulnerability patterns commonly found in code produced by AI coding models. Key technical areas under examination include authentication, input validation, database operations, API integrations, and cryptography. Research from the SCW AI Trust Index, which benchmarks AI coding models from multiple vendors, forms the basis for discussion. A central finding is that no universally secure model exists among current offerings.
Regulatory and Industry Context
Organizations in regulated sectors face particular challenges as AI-assisted development outpaces regulatory frameworks. The session explores the gap between rapid AI adoption and the lack of established oversight mechanisms, with specific relevance to banking, financial services, and healthcare environments where compliance requirements are stringent.
Who Should Attend
This webinar is relevant for CISOs, security advisors, engineering leadership, software developers, and security teams responsible for AI governance. It addresses visibility and validation challenges for AI-influenced code entering production environments, along with risk reduction techniques for organizations deploying AI coding tools at scale.