Runtime data governance for AI agents and developer tooling.

DataFog runs a policy-enforced loop on sensitive payloads as they move through commands, files, and tool calls:

• detect sensitive entities
• decide what policy says (allow, transform, allow_with_redaction, deny)
• enforce before the action executes

It is the data firewall for your local developer + agent workflows.

# Drop DataFog into your PATH with a policy gate wrapper
datafog-shim hooks install --target /usr/bin/git git

from datafog import scan
scan("Call Sarah Chen at 415-555-0142, SSN 234-56-7890")
# -> [{"entity_type": "NAME", "value": "Sarah Chen", ...}, ...]

Process-boundary enforcement with scan/decide/transform APIs, receipts, and adapter-aware policy matching.

SDK + CLI bindings for integrating DataFog policies into agent and app pipelines.

Integrate policy-aware interception for MCP tool responses, so sensitive values are governed before they reach context.

• Developer- and AI-tool guardrails — enforce policy before shell/file/API actions execute.
• Runtime DLP for agents — treat policy as code, with decision receipts and auditable logs.
• CI/CD and compliance checks — scan artifacts and enforce data handling controls in workflows.

🌐 datafog.ai · 📦 PyPI · 𝕏 @datafoginc