From 30 Days to 30 Minutes: How AI Agents Rewrite the Rules of GRC
RAD Security
Compliance work takes up more time than it should.
Security and GRC teams spend their days chasing documentation—pulling evidence for SOC 2 audits, responding to vendor risk questionnaires, reviewing controls for ISO 27001, aligning with evolving guidance from frameworks like NIST and FAIR. Then there are internal requests from legal, questions from procurement, policy updates from leadership, and last-minute escalations from sales trying to close a deal. It adds up quickly, and most of it lands on the same small group of people.
The work is difficult to move quickly through because key information lives in too many different places. Evidence might be buried in a cloud config, an internal doc, or a Jira ticket. Risk summaries are often written from scratch. Reports get duplicated and reshaped for each audience. The tools supporting this work—shared folders, outdated templates, customized ticketing systems—aren’t designed for responsiveness or reuse.
This creates a slow bleed on productivity. Teams spend hours formatting evidence, rewriting findings, and trying to keep control mappings current. By the time a report is finished, the environment may already have changed. Small tasks pile up and block bigger ones. Delays in documentation make it harder to make timely decisions. Reactive work leaves little space for strategic improvements.
The longer it takes to verify posture or respond to a control failure, the greater the risk of exposure. Manual, fragmented workflows drain attention away from actual risk management and make audit prep feel like a separate job—rather than part of the operational rhythm of security.
Smarter Risk Scoring, Automated from the Start
RAD gives GRC teams the context and coverage they need to move faster.
The system connects directly to live sources of truth—cloud configurations, runtime data, risk registers, internal policies, and external frameworks. It pulls the right information without requiring teams to dig through dashboards or request screenshots from engineering. Each data point is linked to a control, a system, a business owner, and a timestamp. That context stays up to date and traceable.
RAD’s GRC agent uses Retrieval-Augmented Generation (RAG) to source and assemble the most relevant content from across your environment. This includes internal documentation, policy references, risk models, and telemetry from cloud workloads. The agent maps this information to frameworks like NIST RMF and FAIR, then produces reports with full citations and linked evidence. Outputs follow a clear reasoning structure and show where each input came from.
