CamoTextCLI: Automated Anonymization

July 11, 2025

CamoTextCLI takes the zero-egress CamoText anonymization engine and drops it into your terminal or onto your server. No GUI, no internet, uniquely suited for headless tasks: CI/CD pipelines, cron jobs, containerized data processors, and AI agents. If your workflow can launch a shell command, it can privatize text with CamoTextCLI.

Automated Anonymization with CamoTextCLI

How to Automate Redaction and Anonymization

Apps with graphical interfaces are excellent when a human is steering, but organizations and their privacy officers increasingly need automation-first redaction:

  • Continuous integration: sanitize docs pushed to a repository before they ever reach cloud storage.
  • Serverless workloads: auto-strip PII from incoming documents.
  • ChatOps: bots that auto-redact snippets users paste into a channel.
  • RAG pipelines: anonymize knowledge-base documents before vectorizing them for retrieval-augmented generation.

A GUI can’t sit in those tight loops that precede human eyes—but a powerful NLP-powered CLI executable can.

Feature Highlights

  • Headless, cross-platform. No Python runtime required, fully bundled executable. Just drop camo.exe (Windows) or camo (macOS/Linux) on disk and go.
  • Batch & recursive directory processing with multi-threaded workers for large archives.
  • Flexible inputs. Feed a single file, raw text string, or an entire folder tree of mixed formats (TXT, PDF, DOCX, CSV…).
  • Config settings for prioritized terms, hash length (set to 1 for simple masking/redaction, all using SHA-256 under the hood), and any data categories to ignore.
  • Entity introspection. List detected categories without altering the file—useful for audits.
  • JSON hash key export for reversible re-linking or auditing.
  • Zero network calls. Like its GUI sibling, CamoTextCLI never touches an API endpoint or telemetry server —ideal for air-gapped hosts.

Quick Start


# Get comprehensive help
camo --help                      # Windows
./camo --help                    # macOS/Linux

# One-liner redaction (STDOUT)
camo --i "User last accessed from IP 192.168.0.1, username janedoe@gmail.com"
camo --i log.txt

# Save output + key file
camo --input contract.docx --output sanitized.docx --dump-key audit_key.json

Deeper Usage Patterns

Batch-sanitize an entire client folder

# Recursively process sub-folders
camo --input-dir ./ClientFiles --output-dir ./ClientFilesRedacted --recursive --dump-key batch_key.json

Configure a client- or usecase-specific workflow

Stop known sensitive terms from reaching the remote origin by prioritizing them.

# config.json
{
    "priority": ["AcmeCorp", "Jane Doe", "Project Varia"],
    "hash_length": 10
}

# Run CamoTextCLI with custom config
camo --config config.json -i sensitive.txt -o sanitized.txt 

RAG pre-processor for AI agents

Feed every document through CamoTextCLI before embedding:

# inside ingestion.sh
for f in $(find ./docs -name '*.pdf'); do
    camo -i "$f" -o "./redacted/${f##*/}"
done
python embed.py ./redacted

Sample Command & Flag Reference

Flag Purpose Example
--config Specify a JSON config file for priorities, hash length, and ignored categories --config config.json
-i / --input Single file path or raw string -i notes.txt
-o / --output Write redacted file; omit for STDOUT --output clean.txt
--input-dir Input folder for batch mode --input-dir ./reports
--output-dir Destination folder (batch) --output-dir ./reports_redacted
--recursive Descend into sub-folders --recursive
--priority Anonymize specific words first --priority "Project Zephyr"
--ignore-category Leave enumerated categories visible --ignore-category "LOCATION"
--dump-key Write JSON mapping of hashes→original --dump-key key.json
--list-entities Scan & report detected categories, then exit --list-entities

Full argument matrix: camo --help.

Example Use Cases

Law Firms & E-Discovery

Run CamoTextCLI as an intake filter so first-pass reviewers never see raw PII; export anonymization keys for later re-linking when privilege checks are complete.

Data-Privacy Officers

Schedule a nightly cron job to strip employee or customer identifiers from log archives before they roll into SIEM or SEC compliance storage.

Private AI Agent Startups

Bundle the CamoTextCLI executable into a Docker image; your agent sanitizes context snippets locally, mitigating privacy headaches when you lean on a public LLM API downstream.

Public-Records Teams

Automate redaction of FOIA packets by pointing --input-dir at a document dump and giving interns only the anonymized output plus the audit key.

Enterprise Customization & Accelerated Pipelines

CamoText's out‑of‑the‑box recognizers cover a broad swath of PII and other sensitive information, but enterprises often track domain‑specific tokens, like case IDs, policy numbers, proprietary SKUs, or research subject IDs. Many of these IDs may still be caught by the default recognizers (such as the UUID, CONTACT_NUMBER, or ACCOUNT recognizers), but larger organizations might get a quote from CamoText for custom recognizers.

Enterprises should also use the --config JSON spec for large-scale, high-volume pipelines and long lists of priorities.

Consistency: Maintain the same anonymization rules across multiple runs
Reusability: Share configuration files across team members
Version Control: Track anonymization settings in source control
Reduced Command Length: Avoid typing long command lines repeatedly

Best Practices

Human-in-the-loop is still required. NLP is probabilistic; always perform output checks and use the priority flag for ensured recognition.
Leverage the user config. CamoTextCLI's user config file options are a powerful way to customize and expedite the anonymization process—prioritized terms are deterministically matched.
Guard your key files. Sharing the hash map broadly defeats the purpose of anonymization—only save it if you need it.

For bespoke recognizers and features, integration consulting, or bulk licensing, email contact@camotext.ai.

Endnotes

  1. CamoTextCLI Documentation v0.1.1
  2. CamoTextCLI User Guide