Capability architecture
A.R.T. Engine: context, validation and evidence.
This capability contributes to the same platform outcome: understanding realistic attacker exposure and proving what matters.
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 and outputs
- A clear explanation of the exposure, affected assets and likely attack path.
- Reproducible evidence suitable for analysts, developers and risk owners.
- Prioritisation based on exploitability, business impact, sensitive data and chainability.
- Owner, remediation and workflow context that can move into Jira, Slack, SIEM or reporting.
