Fendr vs Spirion
Fendr
Fendr is a browser-extension based data loss prevention and AI governance product that monitors and controls what employees paste or upload into ChatGPT, Copilot, Claude, Gemini and other web-based AI tools. It is aimed at mid-market organisations and deploys via MDM, with Chrome and Edge named as supported browsers; policies can block a risky action, redirect the user to Copilot or log the action for audit. The vendor positions it as an option for firms without Microsoft Purview E5 licensing and Entra-enrolled Windows devices, and states that the extension does not see native desktop apps such as Claude desktop or Copilot inside Word. Pricing is published: the first 10 licences are free, the Standard plan is GBP 6 per user per month for 11 to 500 employees, and Enterprise pricing is custom. The product is made by Fendr Ltd, incorporated in London on 4 August 2025, with founder Katie Whitlock listed as a director at Companies House. Fendr took part in the LASR Validate programme and was profiled by Plexal in October 2025, when it was running proof-of-concept trials.
Pros
- Published per-user pricing with the first 10 licences free
- Registered UK company (Fendr Ltd, 16625357) with a published extension privacy notice
- Independent coverage from Plexal and the LASR Validate programme
Things to check
- Company incorporated August 2025 and still running proof-of-concept trials as of November 2025 per LASR
- Covers browser-based AI tools only; the vendor states it does not see native desktop apps such as Claude desktop or Copilot in Word
Pricing: Free for 1 to 10 employees; Standard is GBP 6 per user per month for 11 to 500 employees with discounts for annual commitment; Enterprise is custom pricing with volume discounts for 500 or more employees.
Spirion
Spirion is a sensitive data discovery and classification platform that specializes in finding, classifying, and remediating sensitive data across structured and unstructured data stores. Spirion's Sensitive Data Platform uses a combination of pattern matching, machine learning, and contextual analysis to accurately identify PII, PHI, PCI, and other regulated data types with precision. It is particularly strong in healthcare and financial services where accurate sensitive data discovery is critical for compliance.
Pros
- Strong accuracy for sensitive data identification with low false positives
- Deep specialization in regulated data types (PII, PHI, PCI)
- Strong presence in healthcare and financial services verticals
- Covers both structured and unstructured data stores
- Remediation actions to quarantine or protect discovered sensitive data
Things to check
- Narrower scope. Focused on discovery and classification, not full data security
- Lacks insider threat detection and behavioral analytics
- No data access governance or permission mapping capabilities
- Can be resource-intensive during large-scale scanning operations
- User interface less modern than newer competitors
Pricing: Custom pricing based on data volume and endpoints