Third-party risk
Classify and flag questionnaire responses, compare answers against prior submissions, and surface the ones that need a human.
Endeavor for cybersecurity
Endeavor automates the document-heavy assessment work that consumes security teams. Third-party questionnaires, threat triage, and evidence review — with source attribution behind every finding so an analyst can defend it.
Proven in a security deployment
33%
Reduction in manual review time
27%
Increase in assessment accuracy
Evaluating third-party cyber risk meant reading long vendor questionnaires line by line. We deployed two domain-specific models working in tandem to classify, evaluate, and flag responses — then made the result accountable, so findings could be trusted rather than re-checked by hand.
Where it fits in a security programme
Threat and advisory triage
Rank incoming advisories and reporting by actual exposure rather than reading everything in arrival order.
Evidence and control review
Extract control statements from policies and evidence packs and check them against the framework you are assessed on.
Built for work that has to stand up
- Every finding carries the source records that produced it.
- PII and sensitive values are redacted before any external model call.
- Analyst scoring is the promotion gate, so accuracy is measured not asserted.
- Runs inside your own environment with egress you control.
- Instruction changes are versioned, so a finding can be reproduced later.
Frequently asked questions
What security work has Endeavor already automated?
Third-party vendor risk questionnaires, using two domain-specific models working in tandem to classify, evaluate, and flag responses. That deployment cut manual review time by 33% and increased accuracy by 27%.
Can an analyst tell why something was flagged?
Yes. Every finding carries source attribution and a reasoning log, so an analyst can see which response and which instruction produced the flag.
Does our data leave our environment?
Only if you choose. Endeavor runs in your own Kubernetes namespace, and models can be hosted locally so vendor submissions never reach a third-party provider.
How does this differ from a GRC tool's built-in AI?
A GRC assistant sees only what is inside the GRC platform. Most real assessment work requires joining that to external threat signal, past assessments, and contract terms held elsewhere.