DLP and redaction rules for agents are execution-time controls that detect sensitive data (like credentials, HR data, and customer identifiers), redact it before it leaves the runtime, and record what was changed. In Claw EA, you express these requirements as policy-as-code and bind them to an OpenClaw-based runtime using a WPC and CST enforcement, not by relying on prompts.
Prompt-only guidance fails when the agent is tool-using, multi-step, or under prompt injection. A permissioned execution layer makes the rules non-optional: the agent can be blocked from sending unredacted content, and every model call can be tied to gateway receipts and a proof bundle for audit.
Step-by-step runbook
This runbook assumes OpenClaw is your baseline agent runtime and that you route model calls through clawproxy to capture gateway receipts. The goal is fail-closed: if a message cannot be verified as redacted per policy, it does not ship.
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Define what “sensitive” means for your environment. Start with concrete detectors: API keys, OAuth tokens, passwords, SSNs, bank data, and tenant-specific identifiers (employee IDs, customer IDs, case numbers).
Also define what the agent is allowed to do with each class: block, redact, or allow with justification and a narrow destination list.
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Write the policy-as-code artifact and publish it as a WPC (Work Policy Contract) in the WPC registry. Treat the WPC as the canonical source of truth for redaction behavior and logging requirements, not as documentation.
Pin the WPC hash in the job configuration so the runtime cannot silently drift to a different policy version.
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Issue a CST (scoped token) for the job from clawscope with scope hash and optional policy hash pinning. Use short TTLs and job-scoped binding so tokens cannot be replayed across runs.
Store the CST only inside the secure execution context and avoid writing it to agent memory or chat transcripts.
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Route all model traffic through clawproxy. This gives you gateway receipts for each model call, which you later bundle into a proof bundle.
If you use OpenRouter via fal, route it through clawproxy so the receipt boundary stays consistent across providers.
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Implement redaction at the execution layer, not in the prompt. Concretely: apply redaction before any network egress tool runs (Teams message, email send, ticket comment, webhook post) and before any model call that includes tool outputs.
Where possible, keep the raw secret out of the LLM context entirely by stripping or tokenizing it at tool output boundaries.
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Turn on OpenClaw sandboxing for tool execution and restrict the tool policy to the minimum needed. This reduces the chance that a prompt injection turns into filesystem scraping or bulk export.
Run OpenClaw’s security audit regularly and treat any relaxation of redaction-related settings as a deployment change that requires review.
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Emit auditable evidence: redaction events (what detector fired, what replacement happened, and where), plus gateway receipts. Package them as a proof bundle and store a Trust Pulse for viewing.
Verify bundles during incident response to confirm the run was actually constrained by the pinned WPC and that the model calls match receipts.
Threat model
DLP and redaction are not about perfect secrecy. They are about preventing the common, high-impact failure modes where an agent accidentally discloses sensitive data through chat output, logs, or tool egress.
| Threat | What happens | Control |
|---|---|---|
| Prompt injection causes exfiltration | The agent is convinced to paste credentials or internal records into a public channel or an external webhook. | Execution-time redaction before egress plus tool policy minimization; enforce via WPC and verify with proof bundles. |
| Secrets leak into model context | Tool output includes tokens or private text; the model call transmits it to the provider. | Redact or strip tool outputs before the model call; route via clawproxy to generate gateway receipts for what was sent. |
| Logging or transcripts retain sensitive fields | Raw values end up in debug logs, chat history, or operator dashboards. | Redaction applied at the runtime logging boundary; OpenClaw security audit flags risky logging settings. |
| Policy drift across environments | Dev uses strict redaction; prod silently runs with weaker rules, or rules change between runs. | WPC hash pinning and CST policy hash pinning (optional) so the job is bound to a specific policy artifact. |
| Token replay across jobs | A CST is reused to run a different job with different data, bypassing intended scoping. | Marketplace anti-replay binding with job-scoped CST binding; verify in the proof bundle metadata. |
Policy-as-code example
This is a simplified, JSON-like example of a WPC that forces redaction before outbound tools and before model calls that include tool output. The key point is that the rule is evaluated by the execution layer, not by the model’s willingness to follow instructions.
{
"wpc_version": "v1",
"policy_name": "dlp-redaction-default",
"policy_intent": {
"mode": "fail_closed",
"redaction": {
"apply_to": ["model_inputs", "tool_outputs", "egress_payloads", "logs"],
"detectors": [
{ "id": "msft_graph_token_like", "type": "pattern", "severity": "high" },
{ "id": "password_like", "type": "pattern", "severity": "high" },
{ "id": "pii_ssn_like", "type": "pattern", "severity": "high" },
{ "id": "customer_id_prefix", "type": "pattern", "severity": "medium" }
],
"actions": [
{ "on": "high", "do": "redact", "replacement": "[REDACTED:HIGH]" },
{ "on": "medium", "do": "redact", "replacement": "[REDACTED]" }
]
},
"egress_rules": [
{
"tool": "teams_send_message",
"require": ["redaction_applied", "wpc_hash_pinned"],
"block_if": ["unredacted_sensitive_detected"]
},
{
"tool": "http_post",
"require": ["redaction_applied"],
"block_if": ["high_severity_detected"]
}
],
"evidence": {
"emit_redaction_events": true,
"require_gateway_receipts": true,
"bundle_format": "proof_bundle"
}
}
}
If you are integrating with Microsoft 365, keep the rule language about scopes concrete. For example, if your agent uses Microsoft Graph via official API, require that only the necessary Graph permissions/scopes are used and treat token-like strings as high-severity redaction targets.
What proof do you get?
You get machine-verifiable artifacts that link “what policy was supposed to happen” to “what actually happened” during the run. This is critical when an incident review asks whether a disclosure was caused by policy drift, tool misuse, or operator configuration.
Claw EA uses WPC (served by clawcontrols) as the signed, hash-addressed policy artifact. A job uses a CST (issued by clawscope) with scope hash and optional policy hash pinning so the run is cryptographically tied to the intended policy.
Model calls routed through clawproxy emit gateway receipts. Those receipts, plus redaction events and relevant metadata, are packaged into a proof bundle, and you can store/view the result as a Trust Pulse for audit workflows.
Rollback posture
DLP failures are often discovered after rollout, under real traffic. Your rollback plan should preserve evidence while you reduce blast radius quickly, and it should not require re-prompting or retraining the agent.
| Action | Safe rollback | Evidence |
|---|---|---|
| Detector too aggressive (false positives) | Pin the prior WPC hash and redeploy; add a narrow allow rule only for the specific field format that was misclassified. | Proof bundle shows the WPC hash used per run plus redaction events indicating which detector fired. |
| Detector missed a secret format (false negatives) | Update the WPC with a new detector and require the new hash for all new jobs; keep old runs verifiable. | Gateway receipts plus run metadata distinguish older runs from the updated policy version. |
| Unexpected tool egress path | Remove or deny the tool via OpenClaw tool policy, then reintroduce with an explicit pre-egress redaction gate. | OpenClaw configuration changes plus proof bundle evidence for model calls; tool restrictions reduce future exposure. |
| Suspected token misuse | Revoke CSTs and re-issue job-scoped tokens; rotate any impacted upstream credentials separately. | CST issuance and job-scoped binding help show whether the token was replayed across jobs. |
Planned: if you need network egress allowlists enforced outside clawproxy, that can be implemented as an additional boundary. Do not treat it as a substitute for redaction because sensitive data can leak through any allowed destination.
FAQ
Why is prompt-only “do not leak secrets” insufficient?
Because the model does not control the real boundary where data leaves: tools do. A permissioned execution layer can block unredacted egress and can prove which policy was active via the WPC hash and proof bundle.
Where should redaction happen in an OpenClaw-style runtime?
Apply it at tool boundaries and before network egress, and avoid placing raw secrets into the model input whenever possible. OpenClaw’s sandbox and tool policy reduce blast radius, but redaction is still needed to prevent disclosure through allowed channels.
How does this relate to Microsoft Purview DLP or Copilot Studio data policies?
Purview DLP and Copilot Studio policies are tenant-level governance controls for Microsoft-managed surfaces. Claw EA focuses on agent execution controls: the agent’s own tool calls, model calls, and logs, including when interacting with Microsoft 365 via official API.
What can auditors verify after the fact?
They can verify the WPC hash that defined the rules, the CST binding used to authorize the run, and the gateway receipts for model calls. They can also review the proof bundle redaction events that show where sensitive strings were detected and replaced.
Can I prove that the model provider never saw the secret?
You can prove what was sent through clawproxy using gateway receipts. If you strip or redact before the model call, the receipts plus your redaction events support a concrete claim about what left your runtime.