Why it matters
- Security environments and attacker techniques change continuously.
- Static tools decay unless methodology and coverage evolve.
- Learning must be controlled, measured and reviewable.
Continuous Learning
Improve detection, methodology, prompts and test coverage from evidence, logs and validation outcomes.
Deterministic evidenceScope-aware executionAdaptive capability
Capability architecture
Continuous Learning: context, validation and evidence.
This capability contributes to the same platform outcome: understanding realistic attacker exposure and proving what matters.
ThreatCanary approach
- Analyse raw outputs, missed patterns, false positives, tool performance and validation results.
- Generate extractor improvements, methodology updates or new test requests.
- Promote improvements through validation and human review where required.
What it validates or reveals
- Detection gaps.
- Improved extractors and methodologies.
- Measured reduction in noise and increased validation quality.
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
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