Why it matters
- Modern environments contain custom APIs, bespoke auth, business logic and novel combinations.
- Static test libraries cannot anticipate every target-specific weakness.
- Hypotheses make testing intentional and auditable.
Hypothesis-Driven Testing
Use observations and graph context to ask testable security questions instead of only running static checks.
Deterministic evidenceScope-aware executionAdaptive capability
Capability architecture
Hypothesis-Driven Testing: context, validation and evidence.
This capability contributes to the same platform outcome: understanding realistic attacker exposure and proving what matters.
ThreatCanary approach
- Generate hypotheses from exposure, API behaviour, vulnerability intelligence, drift and analyst input.
- Assign priority, validation steps, safety constraints and expected evidence.
- Track lifecycle from proposed to tested, confirmed, rejected or retest required.
What it validates or reveals
- Candidate attack paths.
- Custom validation plans.
- A clear record of why a test was run and what it proved.
Evidence 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.
See ThreatCanary in action
Book a technical demo