Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Many organizations still rely on legacy applications to run core business processes. But that comes with limitations. According to the Workforce Agility Report, 50% of CIOs said legacy applications hold back digital transformations. More importantly, they create major security gaps. Most legacy applications, such as Oracle EBS, PeopleSoft, SAP, and JD Edwards, use authentication methods that do not fit modern identity requirements.

Software that runs inside customer-managed infrastructure creates a particular challenge at the FedRAMP High baseline. It still has to meet the applicable security and compliance requirements, even though the vendor does not control the surrounding operating system, libraries, network configuration, or maintenance practices. For Datadog, that challenge centers on the Datadog Agent, which runs directly on customer-managed hosts to collect logs, metrics, traces, and security signals.

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.