Formal verification: We've validated through pilot deployments and CS/DevOps teams who've stress-tested the pipeline in production.
Positional inference: Good catch. We replace PII with type-consistent tokens (e.g., [NAME], [DATE]) so format is preserved for downstream tasks, but the actual value is gone. For higher security, we offer synthetic replacement (fake but realistic values) so position and format don't leak information.
FedRAMP: Not yet certified, but the architecture supports it — runs inside customer VPC, no data leaves the environment, full audit logging. FedRAMP and StateRAMP are on our compliance roadmap after SOC2 and HIPAA. Yes, public sector is a major target market.
Internships: Not formally open yet, but email me at sukin@safekeylab.com — always interested in students working on AI security.
Few questions I though of, and I apologize if they seem stupid as ML is not my focus of study.
1.Has your team ever considered formal verification of code to show how reliable the process you have is?
2. If data has been removed via your pipeline, is it possible to still infer the type of data based on position or format? (Names of people being located in certain places of a sentence, or say the fact the data is formated a certain way could reveal its a date or timestamp?)
3. You mentioned clients can deploy via VPS, does that mean this is a fedramp ready product? (Do you see this tool being offered to public institutions?)
4. Do you have any internship openings for college students in the summer of 2026?