Implementing Ingress-Based Service Discovery
Deep dive into implementing ingress-based service discovery for seamless communication between microservices
Topics Covered
Office Hours Summary
Deep dive into implementing ingress-based service discovery for seamless communication between microservices in your platform.
What You'll Learn
• In-depth insights from industry experts
• Practical strategies you can implement today
• Real-world examples and case studies
• Interactive Q&A and community discussion
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Hosts
Ishaan Kalra
Krishna Mundra
Adi Unni
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Discover how Facets' new Kubernetes Agent revolutionizes cluster management by enabling natural language debugging and secure troubleshooting. This episode showcases our AI-powered orchestrator that maintains proper guardrails and permissions while making Kubernetes operations conversational and intuitive. ### Live Demonstrations & Key Features Watch real-time troubleshooting as we diagnose a pod restart issue caused by missing sidecar files, identify and fix Redis deployment memory configuration problems, and demonstrate CPU usage analysis with Prometheus integration. See how the agent maintains security through user-scoped access controls while providing powerful debugging capabilities. ### Technical Deep Dive Explore the architecture behind Facets' Kubernetes Agent and how it orchestrates AI agents with secure infrastructure access. Learn about multi-tool integration supporting kubectl, Helm, and pod exec operations, plus natural language debugging that works with your existing permissions and kubeconfig setup. ### Audience Q&A Highlights Get answers to key questions about historical log analysis capabilities, chat history persistence and session management, integration possibilities with tools like Cursor and MCP, and comparisons with existing tools like ChatGPT and K9s. Plus, discover future plans for custom tool integration and blueprint generation. ### Perfect For DevOps Engineers looking to streamline Kubernetes troubleshooting workflows, Platform Engineers interested in AI-powered infrastructure management, Site Reliability Engineers seeking efficient debugging solutions, and Development Teams wanting to reduce time spent on cluster-related issues.
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Nathan Hamiel, Head of Research at Kudelski Security, joins Rohit Raveendran for an essential reality check on AI security in DevOps environments. This candid conversation cuts through the hype to address real-world threats, vulnerabilities, and practical defense strategies that every team integrating AI into their infrastructure should understand. ### Real-World AI Security Threats Explore the actual security landscape facing organizations adopting AI, from model poisoning and prompt injection attacks to data exfiltration risks. Nathan shares insights from Kudelski Security's research into emerging threat vectors and how attackers are targeting AI-powered systems in production environments. ### DevOps-Specific Vulnerabilities Understand the unique security challenges that arise when AI meets DevOps workflows, including supply chain risks, model integrity issues, and the security implications of AI-generated infrastructure code. Learn how traditional security practices need to evolve for AI-augmented development pipelines. ### Practical Defense Strategies Get actionable guidance on implementing robust security measures for AI in DevOps, including model validation techniques, secure prompt engineering practices, and monitoring strategies for AI-powered infrastructure operations. Discover how to balance innovation with security requirements. ### Industry Insights and Trends Benefit from Nathan's perspective on the evolving threat landscape, emerging security standards for AI systems, and what organizations should prioritize when building security into their AI-driven DevOps practices. ### Key Takeaways for Teams Learn how to assess AI security risks in your current environment, implement baseline security controls for AI systems, and build a security-first culture around AI adoption without stifling innovation. Essential listening for security professionals, DevOps engineers, platform teams, and anyone responsible for safely integrating AI into production infrastructure and development workflows.
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