Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

New GenAI code security research shows a stubborn truth: as AI is generating more of the code entering production, secure output is not improving at the same pace. The result is a GenAI code security challenge defined by scale, model choice, and the growing need for verification. AI coding has moved past experimentation. For many teams, it is now part of how software gets built every day. That’s the opportunity. It’s also the risk.

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.