Fortra Data Classification vs. Protegrity
A side-by-side comparison based on G2 review data.
| Metric | Fortra Data Classification | Protegrity |
|---|---|---|
| Overall rating | 4.3 | 4.5 |
| Review count | 25 | 14 |
| Ease of use | 8.0 | 9.0 |
| Ease of setup | 7.9 | 9.3 |
| Quality of support | 8.6 | 9.3 |
| Meets requirements | 8.8 | 9.3 |
| NPS | 56 | 57.142857 |
| Small Business | 44.0% | 23.1% |
| Mid-Market | 20.0% | 46.2% |
| Enterprise | 36.0% | 30.8% |
| Total page views | 345 | 307 |
| Total LLM citations | 1,327 | 666 |
Fortra Data Classification
For organizations struggling with data visibility, a robust data classification solution is often the answer. Not all solutions are built the same, however, as many classification tools struggle to meet organizational requirements, whether because of a lack of schema or metadata, improper policy enforcement, excessive operational overhead, and more. As a result, organizations may find that the benefits of their classification solutions aren’t worth the cost and added complexity for their employees.
- Integration Support
- Security
- Flexibility
- Data Protection
Protegrity
Protegrity secures the data and knowledge layer AI runs on, binding protection directly to the data itself rather than locking access around it. Its platform tokenizes, encrypts, and masks sensitive data at the field and knowledge-graph level, enforcing policy in real time at the point of a prompt, query, or answer — across AWS, Azure, GCP, and OpenShift, with FIPS-validated, post-quantum-ready cryptography.