Radiant Logic has expanded its
RadiantOne Platform with AI-driven remediation and real-time access signaling aimed at reducing identity-based attack surfaces. The update focuses on a familiar problem for most security teams: identity risks are detected quickly, but fixing them still takes too long. As identity environments sprawl across cloud services, SaaS apps, and non-human accounts, manual remediation creates backlogs and leaves exposure open longer than teams can afford.
Turning Identity Insight Into Action
Most IAM and IGA platforms stop at detection. They identify misaligned entitlements, duplicate identities, or dormant accounts, then rely on tickets, periodic reviews, or manual cleanup to resolve them.
Sebastien Faivre, CPO at Radiant Logic, told MSSP Alert that the gap between insight and action is where risk compounds. “Most IAM and IGA tools stop at detection. They surface misaligned entitlements, conflicting attributes, or abandoned accounts, but rely on manual processes, tickets, or periodic reviews to resolve them,” Faivre says. “This creates large backlogs, long exposure windows, and high operational cost.”
RadiantOne addresses that gap by treating identity data itself as the control point. By unifying identity data across IAM systems, HR sources, SaaS platforms, and service accounts, the platform gives remediation logic the context it needs to act safely and consistently.
“AI-driven remediation solves the gap between ‘knowing’ and ‘fixing,’” Faivre explains. “RadiantOne unifies identity data across all IAM systems, domains, HR sources, SaaS platforms, and service accounts. This single data foundation gives AI the context required to take reliable, policy-aligned action.”
With authoritative identity data in place, remediation becomes straightforward rather than probabilistic. Inconsistent attributes can be corrected, duplicates reconciled, stale access removed, and orphaned accounts disabled with clear ownership and traceability. Where judgment is required, the platform shifts the work into collaborative workflows instead of creating more alerts.
“When human judgment is needed, the system opens a collaborative workspace and presents options with full context,” Faivre says. “The result is risk reduced at the source rather than simply reported.”
Bounded Automation and Continuous Enforcement
Radiant Logic is careful about where automation applies. The goal is not full autonomy, but safe execution of well-defined actions while keeping high-impact decisions under human control.
“Automation is safest when it enforces objective policy, corrects hygiene issues, or removes known-bad conditions,” Faivre says.
In practice, this means actions such as disabling orphaned or stale accounts, cleaning attributes that violate authoritative sources, reconciling duplicate identities, removing long-unused entitlements, and enforcing baseline hygiene for non-human identities can run automatically. Higher-risk scenarios involving privileged access, regulatory sensitivity, or business exceptions remain human-in-the-loop.
To keep agentic AI governed, Radiant Logic has built remediation around the Model Context Protocol. “MCP creates a governed boundary for agentic AI,” Faivre explains. “It exposes specific, auditable remediation actions and enforces policy constraints around them. Every step is visible, reversible, and signed.”
Access enforcement also becomes more responsive through support for Shared Signals Framework with Continuous Access Evaluation Profile. Instead of waiting for sync jobs or scheduled reviews, risk signals move immediately to relying systems.
“SSF CAEP shortens the distance between ‘risk identified’ and ‘access updated,’” Faivre says. “Access adapts as posture changes, misconfigurations don’t linger for days or weeks, and there’s less manual triage after the fact.”
This shift enables continuous access evaluation, where users maintain the right level of access throughout the day, rather than having corrections applied only during review cycles.
For MSSPs, these changes address scale and consistency across tenants. Identity sprawl, fragmented data sources, and alert fatigue make identity security hard to manage at volume. RadiantOne provides a unified identity baseline per customer, consistent risk signals, and remediation that is governed and reversible.
As Faivre puts it, “RadiantOne becomes the identity data foundation MSSPs rely on to deliver consistent, defensible identity risk management at scale.”
By anchoring remediation, automation, and access enforcement in unified identity data, Radiant Logic is pushing identity security toward a model that reduces risk continuously, rather than documenting it and hoping it gets fixed later.
