Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In short, the MCP 2026-07-28 release candidate is getting attention for going stateless. The quieter story is a package of six SEPs that harden the protocol’s OAuth layer: issuer validation, credential binding, client type declaration, and cleanups around refresh tokens, scopes, and discovery. All six are worth shipping, and all six fix real failure modes. But they harden how a client authenticates to a server, and that was never the whole problem.

Back in June, I wrote a blog making the case that agentic triage alone isn’t an AI SOC. The way I see it, that’s like saying triage is the only responsibility of a SOC team. But as we know, the SOC’s responsibilities extend far beyond that, and these triage-only solutions don’t investigate threats, contain them, or close cases. That work is still left to the SOC team; the bottleneck is just shifting.

Rail networks sit at the intersection of critical national infrastructure and daily public life, making them a persistent target for protest, industrial action, infrastructure crime and, in parts of Europe, suspected sabotage tied to geopolitical tensions. This analysis from CYJAX sets out the physical threat picture in the UK alongside that of the wider continent.

Unknown block type "undefined", specify a component for it in the `components.types` option AI is creating urgency at the board level and a definitional problem in the market.

In the span of six weeks this summer, the United States government issued three separate security directives that, on the surface, appear to address completely different problems. One tightens how federal agencies patch software vulnerabilities. Another creates a government-industry clearinghouse to triage AI-discovered bugs. The third restructures how defense contractors source the raw materials that go into missiles, aircraft, and military electronics. Different agencies. Different languages.

API discovery has become a foundational capability for modern enterprises as API ecosystems expand across cloud-native applications, microservices, SaaS integrations, partner APIs, and AI-powered workflows. By 2027, 78% of applications are expected to use APIs, and with that growth comes an urgent need for visibility that goes far beyond simply listing endpoints.

Every so often, an industry gets a moment that quietly redraws where the line is — not because anything was said, but because something was proven. AI security had one of those moments last week, and it's worth being direct about that before getting into the details: this wasn't an incremental data point. It was the moment a risk that security and safety researchers had described in theory for years showed up, fully formed, in a disclosed incident report.

Quantifying cyber risk for the board means translating technical exposure into dollar-denominated financial risk that the audit committee, CFO, and directors can act on. Boards care about strategic business impact like operational downtime, regulatory penalties, and reputational damage. ‍ They do not care about patch rates, blocked emails, or firewall logs, which are the metrics cyber teams have historically brought to board meetings and which board members have historically ignored.

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.

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.