Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Kubernetes backup & recovery deep dive: DR and VM protection with Spectro Cloud Palette & CloudCasa

Hands-on deep dive into building rock-solid resilience for modern Kubernetes environments. We’ll start with the must-know foundations of Kubernetes backup, disaster recovery and compliance, then explore real-world pitfalls that simple snapshots or open-source Velero can miss. You’ll learn how deploying CloudCasa through Spectro Cloud Palette delivers enterprise-grade, policy-driven protection across multicluster environments. Next, we’ll tackle an emerging challenge: safeguarding virtual-machine workloads running on Kubernetes through KubeVirt solutions like Palette VMO.

AI Safety Controls #cybersecurity #ai

Eric Capuano, founder of Digital Defense Institute, demoed permission controls using LimaCharlie's MCP server. When Claude recommended isolating compromised systems, Eric explained: "It's very aggressively recommending it's time to isolate these systems, but it is not authorized to fire off the isolate network command.".

What is an Enterprise Risk Retainer?

Organizations today are under pressure to effectively respond to acute risk events that can threaten financial stability, regulatory compliance, executive safety and stakeholder trust. Hear from Managing Director Nickolas Savage on the importance of getting the right support quickly, while maintaining cost efficiency and operational resilience.

The Invisible Trick: How to Fool an AI Agent

The Invisible Trick: How to Fool an AI Agent A10 Networks' security experts, Jamison Utter, Madhav Aggarwal, and Diptanshu Purwar, discuss a classic example of an adversarial attack that tricks an AI agent using the equivalent of invisible watermarks. Madhav explains how researchers used an invisible watermark in a research paper that, when scanned by an AI agent, would automatically trigger a positive review. This watermark was not visible to human reviewers. This clever manipulation highlights a significant vulnerability in AI models: they can be influenced by hidden data in their input.