---
title: Endeavor for Cybersecurity | 33% Faster Vendor Risk Review
description: Endeavor automates third-party risk questionnaires and threat triage - cutting manual review time 33% and raising accuracy 27% in production, with full source attribution behind every finding.
---

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# 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.

[Discuss your assessment workload](https://rotational.ai/calendar?hsLang=en)

## 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

### Third-party risk

Classify and flag questionnaire responses, compare answers against prior submissions, and surface the ones that need a human.

### 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.

[Talk to us about vendor risk](https://rotational.ai/calendar?hsLang=en)

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