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Most security teams are not suffering from tool failure.
Endpoint platforms generate accurate telemetry, identity systems enforce access correctly, cloud scanners surface real configuration issues, and workflow tools move tickets and messages where they need to go.
The failure mode shows up when teams try to reason across those systems.
Answering basic operational questions requires manual correlation across dashboards, exports, and tribal knowledge. Risk evaluation becomes slow because context is distributed, and resolution stalls because ownership and evidence are unclear.
RAD was built for this exact problem. The goal is not replacement, but correlation, continuity, and operational clarity across an existing stack.
RAD functions as the front page of security operations. It is the place where signals converge and where investigation starts.
FusionAI Connects Signals to Outcomes
FusionAI is the intelligence layer inside RAD. It operates above integrated systems and focuses on one job. Convert fragmented telemetry into usable security outcomes.
This involves identifying an issue, retrieving the relevant data across systems, correlating signals that originate from different control planes, and supporting resolution with evidence intact.
FusionAI is not a generic reasoning layer. It is built around security-specific workflows, data models, and failure modes that practitioners deal with every day.
RAD exposes this capability in two primary ways.
Ad Hoc Investigation Through RADBot
Security work frequently starts with a question rather than a ticket.
RADBot provides a conversational interface backed by FusionAI and live access to integrated data sources. Queries trigger real API calls into connected systems and return results with full context preserved.
Practitioners can ask about workload behavior, identity access paths, historical exposure, or data handling patterns without manually pivoting between tools.
Responses are are structured explanations with supporting evidence attached. Investigation state remains persistent and traceable rather than disappearing into chat logs or screenshots.
This approach reduces cognitive overhead during analysis and removes the need for manual correlation during time-sensitive investigations.
Automations for Repeated Security Work
A significant portion of security operations consists of repeated reasoning patterns.
Weekly exposure reviews, standard investigation flows, continuous posture validation, and periodic reporting for stakeholders.
RAD Automations formalize this work.
Automations pull data from integrated systems on a defined schedule, apply consistent reasoning logic using security-focused AI, and generate outputs that remain explainable and auditable.
- The value is not limited to speed.
- The primary benefit is consistency.
- The same logic runs every time.
- The same evidence is captured.
- The same outputs can be trusted across teams.
Integration Model Designed for Operations
RAD integrations fall into two functional classes that map to how security teams actually work.
Operational Integrations
These integrations ingest telemetry from systems that observe or enforce security controls. Examples include runtime platforms, identity providers, vulnerability scanners, and cloud infrastructure services.
The data collected from these systems describes what is happening in the environment. FusionAI uses this data to reason about behavior, exposure, and impact.
Organizational Integrations
These integrations connect RAD to systems used for coordination and execution. Examples include ticketing platforms and messaging tools.
Findings and investigation results are pushed into existing workflows with context preserved. Teams are not forced to adopt a parallel process or user interface to act on results.
Normalized Data With a Unified Query Surface
Tool integration alone does not solve the correlation problem.
RAD continuously synchronizes findings and alerts from operational integrations into a normalized data model. This data is stored with full source metadata intact and remains queryable as a single logical dataset.
Practitioners can interrogate this data through RADBot or directly using RADQL. Dashboards are built on top of the same underlying model.
This removes the need to mentally reconcile differences between tools and enables consistent reasoning across the entire environment.
Runtime Analysis Provides Immediate Context
RAD does not rely exclusively on third party telemetry.
The platform performs proprietary runtime analysis to observe behavioral patterns in real time. This data provides insight into what workloads actually do rather than what they are configured to do.
When abnormal behavior is detected, RAD enriches the finding with context from integrated systems and automatically correlates:
- Identity activity
- Configuration state
- Historical behavior
- Data movement
Triage begins with context already assembled. Investigation time drops because the platform supplies the information that teams usually have to collect manually.
Designed to Augment Existing Practice
RAD is not positioned as a monolithic replacement platform.
It is designed to sit at the center of a heterogeneous security stack and provide a coherent operational view across it.
Security teams operate under time pressure and staffing constraints. Clarity matters more than volume, evidence matters more than alerts, and resolution matters more than dashboards.
RAD FusionAI exists to support those priorities.
