AI-enabled development tools and coding assistants are pushing CI pipelines, fueling innovation, and iterating business-critical software faster than ever before. Security teams are now forced to catch up. The burden to remedy AI tools often weak coding practices and to overcome their ignorance to third-party vulnerabilities and licenses is further complicated by developers' implicit trust in their output and an intrinsic incompatibility between AppSec and Dev teams' primary role: to ship functional code quickly. Enterprises can no longer treat development and AppSec workflows as separate entities. To unify them, however, requires fast, reliable, automated ways to evaluate every line of code that flows through the pipeline and to provide immediate access to fix methods. This only manifests with integrated security gates across CI/CD pipelines, aligned to risk-tolerance standards and optimized to maintain the speed benefits of AI-enabled development. Join us as we analyze DevSecOps in the age of AI and structure a viable strategy that accounts for: - Software security and license compliance issues introduced, at speed, by AI coding assistants - The dissonance between the adoption of AI-enabled dev tools and AppSec's preparedness to secure their output - Business priorities that span development, security, legal, and executive requirements
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