Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Boards want answers, not technical dashboards. Three AI tools tried to build the perfect AI risk slide for ACME Corp, and all three left out what matters most. ‍

Frontier AI is changing the economics of cybersecurity. Advanced models can accelerate vulnerability research, exploit-path analysis, attack planning, and disclosure workflows, making vulnerability discovery more continuous, automated, and AI-driven. This raises the bar not only for enterprises that need faster protection, but also for cybersecurity vendors that must adapt secure development, production security, runtime validation, incident response, and AI-assisted workflows to keep pace.

The Anthropic Glasswing initiative brings together Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks as launch partners. You can find a lot of posts and reactions on social media as it is definitely a big deal that Anthropic is keeping their Mythos Preview model out of general access.

For two decades, VMware was the default answer for virtualization. It worked, it was well supported, and the commercial terms were predictable enough that infrastructure strategy could largely ignore the underlying platform and focus on workloads. Broadcom’s acquisition ended that. Perpetual licences are gone. Product catalogues have collapsed from 168 offerings into four mandatory bundles. Per-core minimums have created fixed costs for capacity many organisations don’t use.

A recent open letter from the UK government on AI-driven cyber threats highlights a clear shift in the threat landscape. Cyberattacks are no longer constrained in the same way by human expertise, as advanced AI models can now help identify vulnerabilities, generate exploit code, and increase the speed and scale of attacks.

Gartner named Zenity the company to beat in the AI Agent Governance category in its AI Vendor Race: Zenity Is the Company to Beat in AI Agent Governance report as of 17 April 2026. The evaluation covered technical capabilities, customer implementations, business model, and ecosystem strength. That methodology matters because for us, it means the recognition reflects what the platform actually does in production, not just how well a demo lands.

If your first instinct when connecting an LLM to enterprise systems via MCP is to strip out all personally identifiable information, you’re building a system that is useless. The “block all PII” approach sounds responsible. It checks a compliance box. But it fundamentally misunderstands what MCP-based AI systems do and why they need data in the first place. The real engineering challenge is not blocking data.

Security operators often struggle with the escalating friction that naturally occurs in their detection and response (D&R) workflow. Detections fire in one tool. Investigations happen in another. Case tracking lives in a third. For MSSPs managing dozens of client environments, fragmentation compounds quickly. Analyst time bleeds into context-switching. SLAs are hard to track. When something goes wrong, reconstructing what happened across multiple platforms is painful.