Testing & Security

The disclosure of high-severity flaws in model-serving layers indicates a concerning trend of immature security implementations in the AI agent landscape. As developers increasingly rely on standardized protocols like the Model Context Protocol (MCP) to bridge the gap between large language models

Rivian successfully reduced its security infrastructure costs by 60% while scaling its daily data processing from 150 gigabytes to 10 terabytes. The global cybersecurity landscape has reached a critical tipping point characterized by a "perfect storm" of escalating threats and structural

Unlike traditional automation that follows a rigid script, agentic AI systems possess the capacity to analyze defensive environments and pivot their strategies dynamically based on the specific resistance they encounter. This fundamental shift has transformed the digital threat landscape of 2026,

Security teams are increasingly adopting virtual patching at the network layer to shield applications while permanent software fixes undergo rigorous testing. This shift reflects a broader trend where traditional, manual intervention is no longer viable against the velocity of automated exploits.

A resilient identity ecosystem must ensure that the person initially interviewed is the same individual accessing critical assets months later. In a modern security climate where perimeter-based defenses have crumbled, the verification of a human user represents the most volatile link in the

Agentic AI distinguishes itself from basic assistants by its ability to explore thousands of lines of code and autonomously identify and repair software vulnerabilities. This evolution marks a departure from simple predictive text toward a paradigm where machines function as digital collaborators