Operations | Monitoring | ITSM | DevOps | Cloud

How do you secure data in the age of Agentic AI? In this episode of ShipTalk, Dewan Ahmed sits down with Devan Shah, Chief Architect of Data Security at IBM, to explore the massive shift from traditional DevOps to AI-infused software delivery. Devan shares his journey from being a chef to leading an "army" of 450+ developers at IBM. They dive deep into the technical bedrock of IBM’s "OnePipeline" (built on Tekton and Argo CD), the rise of Data Security Posture Management (DSPM), and the architectural principles required to ship AI features without compromising security or compliance.

Agentic AI isn’t limited to security operations. It’s already improving hospitals, financial systems, and service industries by reducing overload and filling skill gaps. Here’s the problem it was actually built to solve. Additional Resources: About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500.

GitKraken's Product Director Justin Roberts explains why we're focused on managing AI-generated code aftermath, not creating another code generator. Learn about Commit Composer, AI conflict resolution, GitKraken MCP, and our agent-driven workflow vision.

As we move through 2026, the global conversation around AI is shifting from simple adoption to a deeper focus on true openness and sovereignty. In this session from Civo Navigate India 2025, OpenUK CEO Amanda Brock explores the evolving state of AI openness and shares a significant milestone: India is now the world’s number one open-source contributing community.

AI is changing network operations (NetOps) from static automation into adaptive, data-driven systems that can summarize incidents, retrieve knowledge, and guide remediation with human oversight. In this talk, Phil Gervasi breaks down what “AI for NetOps” really means in practice, including the difference between classical ML and large language models (LLMs), why data pipelines matter more than model tuning, and how patterns like RAG (retrieval augmented generation), text-to-SQL, and agentic workflows turn raw telemetry into decisions.