How is “high-fidelity” triage measured?
“Triage fidelity” is a term that deserves a precise definition. Across the industry, triage fidelity is measured by how often the AI gets the verdict right: does the malicious verdict reflect a real threat, and does a benign verdict actually mean nothing to act on?
Today, Radiant’s accuracy stands at 97% for clients with a fully deployed instance. What makes Radiant more accurate than other platforms is a combination of architecture and human influence over the AI.
The four architectural factors that drive Radiant’s accuracy
No single factor explains a 97% accuracy rate. It’s the combination of four architectural decisions that competitors haven’t made together.
Generative Triage Technology. Other AI SOC platforms construct playbooks. Radiant curates knowledge. Pre-built playbooks create coverage ceilings: you can only triage what someone explicitly built triage logic for. Radiant builds and executes triage plans on the fly for any alert type, including ones it’s never encountered before.
Injecting organizational context throughout. Users can influence AI logic and inject their own processes and environment-specific knowledge at any point in the product, with no advanced skills or tuning effort required. The AI’s verdict reflects what normal looks like in your specific environment.
Dynamic Data Orchestration. Radiant eliminates the data engineering bottleneck by executing investigations directly against raw telemetry. The AI dynamically selects and stitches queries across SIEMs, APIs, and unstructured logs, without the overhead of normalization.
Log Management. Accurate triage requires access to data at scale. Traditional SIEMs force teams to choose what to ingest and retain based on pricing, creating blind spots that persist across every investigation. Radiant’s Log Manager enables unlimited data retention on the customer’s S3 bucket, at a fraction of SIEM costs, so every investigation runs high-concurrency, long-range queries against complete telemetry.
Radiant has embedded decades of frontline security expertise into its underlying triage logic. This moves well beyond generic LLM capabilities; every investigation reflects veteran-level judgment. Individually, any of these decisions would improve accuracy. Together, they eliminate the gaps where other platforms lose fidelity.
Triage fidelity: day 1 vs day 90
Day 1 accuracy is higher than expected.
AI tools are already accurate on day one for common alert types, the ones that hit your SOC every day, without any tuning required. The reason is that AI is better at understanding data than any rule-based tool. A SIEM fires an impossible travel alert when it sees two logins from geographically distant locations within minutes. An AI looks at the same event, sees that the second login came through your organization’s VPN, and immediately reaches the correct verdict by reasoning across context.
That said, AI is like a new hire; to perform at their full potential, they need context, feedback, and integration into the environment around them.
Day 90 is where accuracy depends on you, too.
Day 90 is where a critical dependency surfaces: accuracy at scale requires context, and context requires documentation. The platforms that make it easy for teams to inject their knowledge and act on AI feedback loops, like Radiant, are the ones that deliver increasingly precise verdicts the longer you use them.
Everything your team knows about your environment – which IPs belong to authorized scanners, what your test lab network looks like, needs to be documented somewhere. Most teams discover during deployment that almost none of this is actually written down; it lives in people’s heads.
AI SOC solutions are better at reasoning through large volumes of data than humans, SIEMs, or SOAR tools from day one. But that accuracy compounds with context and feedback over time.
What this looks like in production
Most POCs test platforms on familiar alert categories: endpoint, identity, phishing. Those are the alerts everyone has playbooks for. The real test is what happens when an alert is new or sophisticated: will the fidelity hold?
Across our customer base, Radiant delivers up to 98% false-positive reduction and consistently escalates 1–3 high-fidelity alerts per analyst per day, whether those are known alerts or new ones.
Speak to us to see what high-fidelity triage could look like in your environment.
