Agentic triage that never leaves the boundary.
A3CT is Pueo’s deployable agentic analysis layer for security telemetry: multi-agent first-pass triage and semantic detection-gap analysis, built to run on customer-controlled, on-premises models with no API egress.
Agentic AI Analysis of Cybersecurity Telemetry (A3CT)
The problem in a federal SOC is rarely a shortage of telemetry. It is the distance between the telemetry and a decision: queues of unknowns, context spread across consoles, and detection inventories nobody can prove are complete. A3CT is built to take the first pass at that gap.
Multi-agent triage works an alert the way a first-tier analyst does: pull the context, enrich the indicators, recall what the environment has seen before, reason to a verdict, and route it with the evidence attached and cited back to the source events. Semantic detection-gap analysis compares what the detection content actually expresses against adversary behavior described in MITRE ATT&CK, surfacing gaps that a rule-name inventory or a coverage spreadsheet will not show.
A3CT is deployable, and the deployment posture is the point: it is built to run on customer-controlled, on-premises models, so the model, the prompts, the telemetry, and the audit trail all stay inside the boundary with no outside API calls. It is engineered for the SIEM substrate Pueo operates, which is why the integration assumptions are grounded in a real security platform rather than a reference architecture. Pueo GAUGE carries the same discipline into detection truth: an ATT&CK coverage board built from live telemetry, with a confidence truth-score on every detection.
What’s Inside
Multi-agent first-pass triage
Specialized agents intake, enrich, recall, reason, and route: alerts arrive at the analyst as a cited verdict with its evidence, rather than as another unknown in the queue.
Semantic detection-gap analysis
Detection content compared by meaning against ATT&CK technique behavior, so a gap is identified by what the rules actually catch instead of by how they are named.
On-premises models, no API egress
Built for customer-controlled inference on customer hardware: no outside API calls, no mission data or prompts crossing the boundary, and no dependency on a commercial endpoint.
Auditable evidence chain
Every conclusion carries citations back to the source telemetry and the reasoning path that produced it, so an analyst or an assessor can check the work rather than trust it.
Built for an operated SIEM substrate
Engineered for the security platform and telemetry pipelines Pueo operates, so deployment assumptions reflect how a federal SOC is actually instrumented.
Outcomes
- First-pass triage taken on by agents, with analyst attention spent on real threats.
- Detection gaps surfaced by meaning rather than by rule-name inventory.
- Every verdict traceable to the source telemetry that produced it.
- AI capability delivered without mission data, prompts, or telemetry leaving the boundary.
- A deployable path to agentic operations for environments where commercial AI services are not an option.
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