DevSecCon - AI Security Track
Watch the best of DevSecCon ‘24
DevSecCon 2024 virtual summit was packed with DevSecOps lessons and hands-on experiences from industry trailblazers. Dive into the sessions from the Snyk track below and join the global DevSecCon community to help shape the future of secure development.
Developing AI trust
Explore the critical intersection of AI and application security. Watch the recordings now.
Securing AI systems in real-world deployments
Discuss the unique challenges of deploying AI systems in real-world environments, including ensuring reliable performance and defending against AI-specific threats. As AI becomes more complex and autonomous, its unpredictability grows, making security a critical concern. Drawing on insights from our experience, including operating the world’s largest AI Red Team (Gandalf), explore the evolving threat landscape and the security implications of AI in production, covering issues such as prompt injection attacks, data loss, and the risks posed by the democratization of AI capabilities.
Protect the electric sheep
Explore the new security challenges AI introduces across the software supply chain, SDLC, and for developers and architects. We will examine key attack vectors in the supply chain and map them to the OWASP Top 10 for Large Language Models while assessing their impact on CI/CD pipelines. Viewers will gain insights into the vulnerabilities posed by AI in software development and learn actionable strategies to mitigate these risks, protect their pipelines, and safeguard both software and customers from evolving security threats.
Automating AI failure mode testing in devSecOps pipelines
Explore the motivation for integrating AI into services for productivity and cost reduction. Address the security and trust challenges that arise from embedding AI in applications. Traditional testing often falls short. Discuss how targeted strategies and automated processes for testing AI models can mitigate these risks. Demonstrate how automating the testing of AI failure modes can ensure secure and reliable AI-enhanced applications, allowing organizations to seamlessly integrate AI without exposing their systems to vulnerabilities.
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