Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In March 2018, the SamSam ransomware attack on the city of Atlanta became one of the most expensive ransomware incidents ever to hit a US local government. It remains a useful case study in what happens when an organization has no way to detect, stop or recover from ransomware in real time.

Learn how to measure time to revoke for exposed credentials using validation and invalidation timestamps, remediation SLAs, and CISO reporting metrics.

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.

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.

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.

Midmarket security teams face the same adversaries as the largest enterprises, often with a fraction of the staff and budget. Alert volumes keep climbing, AI-driven threats are accelerating, and lean teams are expected to do more with fewer resources. What these organizations need is world-class AI-led security operations that are actually within reach.

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.

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.

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.

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