Inside FusionAI: Getting Signal from your Stack with RADBot

Written By:

Jimmy Mesta

January 29, 2026

Security teams deal with an overwhelming number of signals across tools and environments, and are challenged with connecting those signals in a way that provides clear, actionable insight. Without that, triage drags, priorities blur, and response stalls. RAD’s FusionAI Core exists to fix that. It ingests telemetry from across your cloud environment including runtime data, identity activity, configuration state, and turns it into structured, contextual output you can act on without guesswork.

RADBot is the interface to that system.

This is not a wrapper around alerts. RADBot is a front end to your live security graph, trained on your real environment. It runs on top of a structured reasoning engine that continuously correlates CVEs, config drift, behavioral anomalies, and access patterns. Every answer comes with provenance and linked evidence. You ask questions, and FusionAI answers with receipts.

This is what lets teams move past dashboards entirely. RADBot reconstructs attack paths, explains process-level deviations, and surfaces IAM access history across clusters. All of this comes from telemetry already streaming into FusionAI. Nothing is synthetic or manually stitched. 

Cloud Security signals power the system, but they are not the system. Kubernetes activity, workload behavior, vulnerability metadata, and runtime fingerprints are foundational inputs. FusionAI does the work to normalize, correlate, and reason across them. The result is a single system that tracks risk, explains decisions, and aligns every finding to context.

Risk assessments are now fully automated. One of our FinTech customers reduced 30 day review cycles to sub-hour analysis using RADBot. The agent combines FAIR based scoring, internal documentation, and live telemetry to produce defensible, board-ready output. It evaluates threat frequency, potential impact, and relevant compensating controls all mapped to your environment.

GRC outputs use the same reasoning layer. RADBot automatically tags findings to controls, correlates activity to violations, and generates reports that link every conclusion to runtime evidence. Audits become continuous, because FusionAI already has the data teams need.

This is the difference. RAD doesn’t pull findings from different tools and try to layer intelligence on top. FusionAI is the layer. Every signal goes through the same normalization and prioritization process, and every automation is grounded in the same context graph. RADBot simply exposes it in a way humans can use.

Teams that adopt this model stop asking how to prioritize alerts. They start asking better questions and getting precise answers. 

• What changed in this workload?
• What does this CVE actually impact?
• What’s the blast radius if this container is compromised? 
• What should we do next?

The result is automation that doesn’t need babysitting, intelligence that doesn’t break when your environment changes, and a security system that thinks the way your team does.

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