AI-assisted development tools can generate code, tests, and even architectural patterns in seconds, dramatically accelerating how software is built. However, this speed also introduces new security challenges that many teams are not yet prepared to address. AI-generated code can include insecure patterns, outdated dependencies, improper authentication logic, or subtle vulnerabilities that may go unnoticed during rapid development cycles. As organizations increasingly rely on AI-assisted coding, ensuring that AI-generated software remains secure becomes a critical responsibility for developers and engineering teams.

In this session, we explore the security implications of AI-assisted development and the risks that emerge when developers rely too heavily on AI-generated code. Through practical examples, we will examine common vulnerabilities introduced by AI tools and demonstrate how insecure patterns can unintentionally make their way into production systems. The session will also walk through practical techniques for reviewing and validating AI-generated code, including dependency and supply chain checks, secret management, and integrating security scanning into CI/CD workflows.

By the end of this talk, attendees will gain a clearer understanding of how to balance the productivity benefits of AI-assisted development with the security practices required for modern applications. Developers will leave with practical strategies for reviewing AI-generated code, identifying potential vulnerabilities early, and implementing guardrails that allow teams to safely integrate AI into their development workflow without compromising application security.