Secure and Compliant Enterprise AI

Woodrow protects your most sensitive business data using enterprise-grade security protocols and best-in-class privacy measures.
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MANAGE PERMISSIONS

Access control and audit logs

Woodrow is designed for controlled access and clear operational oversight. You can scope permissions, restrict what workflows are allowed to do, and review a complete record of activity.
Role-based access controls
Assign roles and permissions so access matches responsibilities.
Pre-approved tool whitelisting
Define which tools and systems each workflow is allowed to use.
Event logs
Support internal reviews and audit requests with a consistent activity history.
PROTECT BUSINESS DATA

Data encryption

Woodrow applies layered controls to reduce exposure of sensitive data while workflows run—from what users see in the UI to what’s transmitted through integrations.
TLS 1.3+ encryption in transit
Data is encrypted in transit between yoursystems and Woodrow using TLS 1.2.
AES 256-bit encryption at rest
Data is encrypted at rest using AES 256-bit encryption.
Automatic PII masking
Sensitive fields are masked by default toreduce accidental exposure.
MAINTAIN CONTROL

AI governance

Woodrow is built to minimize exposure of sensitive information when AI is involved. Data handling is scoped, controlled, and designed to avoid unnecessary retention.
Zero day retention
By default, Woodrow does not retain model inputs or outputs beyond what’s required to complete requests and maintain audit logs.
No model training
Your business data is not used to train 
underlying AI models.
Scoped model access
Your business data is not used to train 
underlying AI models.

Woodrow is SOC 2 Type II compliant

We undergo independent assessments to verify that our controls are designed appropriately and operate effectively over time. Security documentation and supporting materials are available in the Trust Center.

Security FAQs

What data does Woodrow store?
What subprocessors does Woodrow use?
Is my business data used to train AI models?
What steps are taken to address potential biases in the AI model?
What measures are in place to protect against prompt injection and other vulnerabilities?
What audit logging capabilities are available in Woodrow?
Additional questions?