DataFog Core 0.3.0 · Structured record protection ↗

Local PII protection
for AI applications.

Detect sensitive information in text and structured records, then choose how to protect it before passing it to a model, a log, or another system.

DataFog Core runs in your application, with SDKs for Rust, Python, Node.js, and browsers.

Get started ↗ Try the demo ↓ 
python -m pip install datafog-core

No separate service to deploy. Installation guide ↗

Open sourceMIT licensed Built in RustOne shared privacy engine
Local detectionRuns inside your application
Ready out of the boxStart detecting with the built-in engine
Detection is the beginning

Choose what happens
to sensitive data.

Different workflows need different protections. Select the information to transform, apply rules by entity type, and exempt approved values.

Redact · Mask · Remove

Remove what you don’t need.

Replace sensitive values with typed placeholders, mask them with optional partial visibility, or remove them from the text.

Explore transformations ↗

Pseudonymize

Preserve useful relationships.

Use keyed pseudonymization when repeated values need consistent replacements. Your application supplies the key provider.

Understand pseudonymization ↗

Tokenize · Restore

Restore when authorized.

Use provider-backed tokenization for reversible replacements. Your provider owns storage and authorization; Core checks the request scope.

See tokenization and restoration ↗

Redaction, masking, and removal work across all SDKs. Pseudonymization and tokenization use application-supplied providers in Rust, Python, and Node.js.

Build with DataFog

Start in the runtime
you already use.

Scan for findings, transform selected values, or combine both in one operation. The same Core defines the privacy behavior across SDKs.

Installation guide ↗

Already using the datafog Python package? Core has a separate package and API. Follow the migration guide.

# pip install datafog-core
from datafog_core import scan_and_transform

result = scan_and_transform(
    "Contact ada@example.invalid",
    {"transform": {"default": {"strategy": "redact"}}},
)
print(result.text)
# Contact [EMAIL]
Python reference ↗
Beyond the SDK

Connect privacy to your workflow.

Explore DataFog’s existing agent and gateway integrations. Each has its own setup and release path; Core adoption is a separate next step.

Existing integration

OpenClaw

The fogclaw plugin brings PII detection and redaction to OpenClaw.

Explore fogclaw ↗
Where we’re going

One privacy engine.
More places to use it.

We’re working toward consistent detection and transformation across SDKs, agents, and gateways. Core provides the shared engine. Integrations connect it to workflows and decide when to allow, warn, block, or transform.

Available in Core today

Build the protection you need.

  • Text scanning and structured-record protection, including supported person-name fields.
  • SDKs for Rust, Python, Node.js, and browsers.
  • Configurable transformations, with provider-backed operations in server runtimes.

Next direction

Bring the engine to more workflows.

  • Adopt Core in downstream integrations.
  • Carry shared privacy behavior into more agent and gateway workflows.
  • Keep workflow-specific interception and enforcement in the integrations.

Read the Core 0.3.0 release notes ↗ · Follow the Core roadmap ↗

Performance varies by workload. See the 0.3.0 benchmark methodology and results.

Build privacy protection
into your next application.

Start with a scan, choose a transformation, and connect DataFog to your workflow.

Get started ↗Read the documentation ↗