AgentFence Feature
Data-loss prevention built for AI
AgentFence, DLP Inspect
Example figures- INSPECTED
- 12,940
- prompts + calls
- SENSITIVE HITS
- 318
- classified
- GATED
- 77
- held or stepped up
- Prompt with customer PII
- gated
- Tool-call exporting secrets
- blocked
- Benign prompt, no sensitive data
- allow
- PHI across data boundary
- step-up
- POST /guardrails/inspect
- per-event
Why AI needs its own DLP
The riskiest data path in most companies is now a prompt box. Sensitive data flows into models and out through tool-calls in ways legacy DLP, tuned for email and file shares, never sees. AgentFence inspects the AI paths directly: POST /guardrails/inspect evaluates a prompt or tool-call, deterministic data classification identifies what is sensitive, and data-boundary policy decides whether it may proceed.
How AI DLP works
Inspect, classify, gate
Inspect
A prompt or tool-call is submitted to /guardrails/inspect for evaluation.
Classify
The deterministic data classification engine identifies sensitive data classes: PII, secrets, PHI.
Apply boundary policy
Data-boundary rules decide whether that data may cross to the model or target.
Gate
Sensitive flows are stepped up, held, or blocked rather than passed automatically.
Record
The decision and the data classes involved are recorded as evidence.
| Path | Risk | Control |
|---|---|---|
| Prompt into a model | Sensitive data leaving governance | Classify + gate |
| Tool-call moving data | Exfiltration via a tool | Data-boundary policy |
| Agent-to-agent handoff | Cross-context leakage | Boundary + memory limits |
Deterministic classification
The classification behind AI DLP is deterministic (data_classification.py), not a probabilistic guess. The same content is always labeled the same way, so gating decisions are consistent and defensible. That determinism is what lets you set a policy once and trust it applies the same way every time.
How it connects
- Shares the classification engine with data classification
- Enforces the same data boundaries as the MCP gateway on tool-calls
- Pairs with the prompt injection firewall on the inbound prompt path
- Records gated events to the tamper-evident ledger as evidence
Questions people ask
What is DLP for AI?
DLP for AI is data-loss prevention focused on AI paths (prompts, tool-calls, and agent data flows) rather than email and file shares. AgentFence inspects those paths, classifies sensitive data deterministically, and applies data-boundary policy to gate sensitive data before it reaches a model or crosses a boundary.
How is AI DLP different from traditional DLP?
Traditional DLP is tuned for channels like email, endpoints, and file shares. AI DLP watches the channels AI actually uses, the prompt box and tool-calls, which legacy DLP typically does not inspect. The prompt box is a real data egress, and AI DLP treats it as one.
Does it block prompts with sensitive data?
It gates them according to policy: a prompt or tool-call carrying sensitive data can be stepped up, held for approval, or blocked based on data-boundary rules, rather than passed automatically. The response depends on the data class and the boundary being crossed.
How does it decide what is sensitive?
It uses a deterministic data classification engine, so the same content is always labeled the same way: PII, secrets, PHI, and so on. Because classification is deterministic rather than a probabilistic guess, the gating decisions are consistent and defensible in an audit.
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Stop sensitive data at the prompt
Inspect, classify, and gate data before it reaches a model or crosses a boundary.