Capability architecture
A.R.T. decides what is worth testing on this target.
Autonomous Reasoning & Testing turns graph context into hypotheses, selects controlled methods and records every execution decision.
01Why it matters
- Autonomous offensive security requires grounded context, not just prompt-driven execution.
- AI needs a controlled operating model, evidence boundaries and methodology.
- Security teams need to know why an agent acted and what evidence supports the outcome.
02ThreatCanary approach
- Assemble context from exposure, APIs, identity, vulnerability intelligence and previous observations.
- Generate candidate attack hypotheses and choose validation steps.
- Execute or recommend tests within scope, safety and approval constraints.
03What it validates or reveals
- Attack hypotheses.
- Validation outcomes.
- Reasoning traces tied to evidence and graph context.
04Evidence produced
- Hypothesis, prerequisite and expected outcome.
- Agent action trace with approval and safety decisions.
- Confirmed, rejected or unresolved verdict with evidence.
