Introduction
UIs are great for exploration, but enterprise workflows often live in scripts, CI, and runbooks. A reliable CLI is the difference between “we can anonymize” and “we can operationalize anonymization.”
DataPrivix ships a CLI designed for file-based, offline-first anonymization.
Problem statement
Teams need to sanitize artifacts repeatedly and predictably:
- same flags, same rule files, same outputs
- batch processing for directories and archives
- integration into ticketing and incident workflows
Manual steps do not scale, and ad-hoc redaction scripts tend to drift over time.
Why this matters in real-world workflows
The CLI is commonly used by:
- platform engineers automating support bundle sanitization
- support teams preparing artifacts for vendor escalation
- security teams enforcing sensitive data protection policies
It must be deterministic, transparent, and easy to run in restricted environments.
Feature explanation (CLI)
The CLI supports:
- input as a directory, single file,
.zip, or.tar.gz - configuration via
rules.jsonand.exclude - predictable output as a sanitized archive
The key design choice is structure preservation: the output remains usable for troubleshooting and correlation.
Walkthrough (based on the demo video)
The CLI demo typically shows a minimal but realistic run:
1) Run anonymization
data-anonymizer --input ./in --output ./out2) Add organization rules and exclusions
data-anonymizer \
--input /path/to/support-bundle.tar.gz \
--output anonymized-out \
--rules examples/rules.json \
--exclude examples/.exclude3) Validate the outcome
Spot-check output samples and, when needed, iterate on rules. For Pro workflows, preview mode can make validation faster.
Practical use cases
- CI job that sanitizes logs before attaching them to artifacts
- A support runbook step: “sanitize bundle before escalation”
- Automation pipelines that process many tickets per day
Key benefits
- Repeatability: same inputs + same rules produce consistent masking
- Operational fit: works in restricted/offline environments
- Audit-friendly: configuration stored as files and reviewed like code
Conclusion
If you want data anonymization to be part of your operational workflow, start with a CLI-first approach: version your rules, keep behavior predictable, and automate the boring parts.