The current wave of AI coding assistants and agentic orchestration platforms is giving the age-old build vs buy decision a new twist. Prototyping agentic workflows for detection engineering is more accessible than ever, but sustaining
Security teams are facing an exciting new opportunity. Functionality that once required specialized engineering teams and substantial development resources can now be prototyped in days using frontier AI models, coding copilots, orchestration frameworks, and agentic
Detection engineering is getting a very well-deserved moment in the limelight. The 2026 Gartner Hype Cycle for Security Operations introduced a new Detection Engineering Automation Solutions (DEAS) category for “products and services that facilitate developing,
Detection engineering functionality can be vibe coded in days (or less) with AI models, copilots, and agent orchestration frameworks, raising an important strategic question: should you build your own agentic detection capabilities, or choose vendor
Todays environments are multi-cloud, SaaS-heavy, containerized, API-driven, and identity-centric, which creates far more data and complexity. Teams that move to modern data pipelines and data lakes get better scale, flexibility, and cost control. But they
Security has become a data challenge. Identifying threats in the AI era depends on complex data engineering processes, but legacy tools and infrastructure force a painful tradeoff: either collect and store everything and sacrifice your
The CardinalOps Agentic Fleet equips your team to succeed in the AI era. Purpose-built agents continuously improve signal quality and expand coverage, transforming detection engineering into a scalable, adaptive system. Built on decades of practitioner
TAG Infosphere, a professional research and advisory firm serving the cybersecurity community, provides a return on investment (ROI) analysis for the CardinalOps AI-Powered Detection Posture Management platform. Their in-depth analysis shows how the platform delivers
Detection engineers are at a breaking point, managing countless detections, endlessly tuning rules, monitoring telemetry for drift, and researching new adversary behaviors. It’s time for a new model to take flight, with agentic AI pointing
For years, security teams have rallied around a clear idea: shift detection engineering left. Treat detections as code, map them to adversary behaviors, and continuously refine them into high-fidelity signals. On paper, it’s a powerful
For years, security operations leaders have been pushing a simple but powerful idea: shift detection engineering left. Treat detections as code, manage them through lifecycle processes, map to adversary behaviors, then continuously tune, validate, and
CardinalOps closes gaps identified by your BAS platforms and proactively reduces risk with AI-powered detection engineering. By incorporating evidence from attack simulations into automated rule development and tuning workflows, CardinalOps helps accelerate red and purple