DLP
Data loss prevention — screening data in and out of tool calls to catch leakage and injection.
category ▸ Data & Provenance
DLP, in plain language.
Data loss prevention is the practice of inspecting data as it crosses a boundary to stop sensitive information from leaking and to catch malicious content from entering. For AI agents, the boundary that matters most is the tool call: what an agent sends to an external tool, and what it gets back and then surfaces.
Agentic DLP guards two directions at once. Outbound, it prevents an agent from exfiltrating PII or confidential fields. Inbound, it screens for prompt-injection — text crafted to hijack the agent's instructions — which has become one of the defining attack vectors against tool-using models.
How Cortex implements it.
This term isn't abstract here — it maps to a real capability in the runtime. Here is exactly how Cortex enforces or relates to it.
Cortex's MCP Gateway runs a DLP scan on every governed tool call: it screens input for injection and PII and blocks high-injection content before the call is made, then redacts PII from the output before the agent sees it. Every governed invocation — allowed or blocked — is recorded with redacted I/O and a reason.
Output guardrails extend the same idea to generated content, screening or redacting sensitive material before it leaves the agent.
Keep building the vocabulary.
These terms sit next to this one in the governed-AI model — follow the thread to see how the controls connect.
MCP Gateway
A governance layer over every Model Context Protocol tool an agent can call.
Kill switch
An instant control that stops an agent or tool server from acting, immediately.
Tenant isolation
Strict separation so one customer's data and agents can never reach another's.
Fail-closed
When a control can't confirm an action is safe, it denies rather than allows.
See DLP enforced, not just defined.
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