Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Ready for AI? Why the Right Infrastructure Is the Secret to Success

Artificial intelligence is transforming the IT landscape, but many organizations still face a major infrastructure gap. In this podcast, 11:11 Systems explores why the right foundation is critical for building secure, reliable, and scalable AI strategies. Watch Justin Giardina, Chief Technology Officer at 11:11 Systems, and Laura Shafer, VP of Product Marketing at 11:11 Systems, as they discuss.

As AI Comes for Your Data, Is Your Recovery Ready? Cyber Resilience in the AI Era

AI is already writing code, connecting SaaS platforms, and interacting with sensitive business data. In this podcast, 11:11 Systems explores what organizations must do to prepare for the risks AI can introduce across data security, recovery, and cyber resilience. Watch Jim Jones, Sr. Product Infrastructure Architect at 11:11 Systems, and Laura Shafer, VP of Product Marketing at 11:11 Systems, as they discuss.

Demo Observe What Your AI Is Actually Doing

Security teams are receiving more alerts tied to AI workloads, but most miss the runtime context needed to understand what happened, why it happened, and whether it violated policy. AI visibility cannot stop at deployment and configuration. Join this live demo session to see how Wallarm AI Hypervisor helps teams understand what AI workloads are actually doing at runtime inside Kubernetes environments. The session focuses on giving security teams clearer operational context around AI behavior, outbound activity, sensitive data exposure, and user-driven actions across AI systems.

AI Pentesting vs Traditional Pentesting: A Comparison, Cost, and Coverage Breakdown

If there’s one thing all of us can agree about modern security, it is that penetration testing is no longer a once-a-year activity. Modern attack surfaces do not stay still. New code ships faster, cloud infrastructure is constantly changing, and APIs are multiplying across product ecosystems. To keep up, engineering teams have moved security earlier in the development lifecycle through shift-left practices.

Move faster than AI-driven risk: Inside Mend.io's latest AI application security update

AI didn’t just change how fast you ship. It changed what your AI application security program has to protect. Two years ago, security teams protected code, open source, and containers. Today they also have to protect AI agents, MCP servers, models, prompts, and runtime interactions, configured or deployed faster than any team can manually review. The attack surface didn’t grow. It exploded.

AI Agents and MCP: Security Implications

The Model Context Protocol has quietly become the connective tissue of enterprise agentic AI. MCP standardizes how AI agents discover, request, and invoke tools, data sources, and external systems, replacing the custom integration code that used to sit between every agent and every backend. ‍ That standardization is what made agents commercially viable at scale. It is also what turned MCP into one of the largest and least-understood attack surfaces in enterprise AI.

Securing the Agentic Enterprise

We're living through the biggest shift in how work gets done in a generation. In every industry, every company is becoming an agentic enterprise, meaning a business where humans and autonomous AI work side by side. What makes an agentic enterprise successful is its workflows: how it combines intelligence, both human and machine, with its proprietary data.