Security & Privacy
How STET handles your data, ensures auditability, and maintains transparency.
Overview
STET is designed for financial reconciliation where security and auditability are non-negotiable. Our architecture prioritizes:
Zero Persistence
Data is processed in memory and never stored on our servers.
Full Transparency
Every decision includes evidence links and reasoning.
Deterministic First
Rule-based matching before any ML enhancement.
No Model Training
Your data is never used to train ML models.
Data Handling
In-Memory Processing
All file processing happens in memory. Uploaded files are read, parsed, matched, and discarded. No data is written to disk or persisted in any database.
No Data Retention
After an audit completes, the only output is the audit certificate and results JSON. The original files are not retained. To re-run an audit, you must upload the files again.
Stateless Architecture
The backend is stateless. Each request is independent. There's no session, no user account, and no persistent storage of user data.
Deterministic Logic
The core matching algorithm is entirely deterministic:
- Same inputs always produce same outputs. No random seeds, no non-deterministic ordering.
- Rule-based matching runs first. Passes 1-3 use exact thresholds (85% similarity, ±3 days, etc.).
- ML enhances, never fabricates. Semantic matching only provides similarity scores; it doesn't invent matches.
Reproducibility Guarantee: Given the same input files and thresholds, STET will produce identical results every time. This is verified via the file hash in the audit certificate.
ML Transparency
STET uses a pre-trained sentence transformer model for semantic matching. Here's what you need to know:
Model Used
sentence-transformers/all-MiniLM-L6-v2A small, efficient model for computing text embeddings. Open-source and widely audited.
No Training on Your Data
The model is pre-trained and frozen. Your transaction descriptions are used only for inference (computing embeddings). They are never stored, logged, or used for training.
No Hallucinations
Unlike generative AI, semantic matching computes similarity between existing texts. It cannot "make up" transactions or create false matches.
Auditability
Every audit produces a certificate that enables third-party verification:
- File Hash: SHA-256 of combined input files
- Threshold Configuration: Similarity %, date tolerance, etc.
- Match Log: Every match with pass used and confidence score
- Discrepancy List: Every flagged item with reasoning
- Timestamp: ISO 8601 completion time
Anyone with the same input files can re-run the audit and verify they get identical results.
Deployment Options
Self-Hosted
Run STET entirely on your infrastructure. Docker Compose makes deployment simple. No external calls.
On-Premise
Air-gapped deployment option available. Contact us for enterprise licensing.