For each security-relevant AI sandbox action or decision, capture a structured, versioned receipt that records what happened, when and where it was observed, which actor and resource were involved, the decision and outcome, and how the record is tied to the run. Bind the receipt to a cryptographic digest and signer identity, preserve relevant model and configuration provenance, and record what the capture process could not see. This is an implementation synthesis—not a universal or mandatory receipt schema.
What belongs in a signed receipt?
A receipt should let an investigator connect an event to its actor, authorization, sandbox run, boundary observer, and outcome without assuming that a signature makes the account of the event true. NIST SP 800-171 Rev. 3 describes useful audit content as event type, time, location, source, outcome, and associated identities or entities. That is a general audit-record baseline, not a sandbox-specific receipt standard. The checklist below applies that baseline to agentic and generative AI workloads. NIST SP 800-171 Rev. 3
- Receipt and schema identity: a unique receipt ID; schema name and version; event type; producer or observer component; and environment identifier.
- Time: event time in UTC with declared precision, receipt creation or signing time, and ingestion time if different. Record clock source or synchronization context when timing matters to an investigation.
- Run and correlation context: sandbox instance, run or session ID, request or correlation ID, parent operation or trace ID, and tenant or project when applicable. Use identifiers that can be matched across application, proxy, tool, and downstream-provider evidence.
- Actor and authorization: the user, service, agent, workload, or process identity; a credential or principal reference if safe; the relevant role or privilege context; and the policy or rule ID plus authorization decision.
- Action and boundary: the attempted or completed operation, tool name, route or resource, relevant source and destination, access or flow-control boundary, and the component that observed it. For denied or failed actions, retain a denial reason or normalized error class.
- Outcome: success, failure, or blocked status; result class; relevant state change; and security-relevant guardrail or anomaly decision. Do not place secrets or full sensitive payloads in this field.
- Integrity and provenance: a digest of the canonicalized receipt, signature, signer or key ID, algorithm and format, and the applicable key or trust-policy reference. Link to digests or version identifiers for the model, tool, policy, prompt or configuration, and generated artifact when needed to reconstruct the decision.
- Capture coverage: capture method, observer boundary, known exclusions, capture health, and references to independent boundary logs or separately protected payload evidence where available.
- Storage and retention: an authoritative, access-controlled store separate from the sandbox workload where feasible; a retention policy; auditing of access and export; and an alert or record for logging-pipeline failure.
Choose retention based on applicable business, contractual, and legal requirements; the cited guidance does not establish one universal retention duration.
How should receipts capture AI-specific context?
For an agent run, event records need to connect requests, model responses, safety decisions, tool calls, and downstream actions. Record the policy decision and outcome, actor and tenant where applicable, route or tool, request ID, and normalized error class. A confidence or score can be useful when it was part of the decision. These fields help explain a sequence of actions rather than merely showing that a process ran.
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Keep important system and prompt-configuration changes in the audit trail. The UK Department for Science, Innovation and Technology’s Code of Practice for the Cyber Security of AI calls for an audit trail of changes to system prompts or other model configuration that affect behavior. Its provenance recommendations also include cryptographic hashes for model components made available to stakeholders, and source plus date/time records for publicly sourced training data. Map these records to assets that matter to the system’s risks instead of collecting irrelevant detail.
For generated artifacts, retain provenance that can identify the producing AI system, generation context, involved humans, and associated audit records. A digest or version reference can connect a receipt to an artifact without copying the artifact into every log store.
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How can teams preserve evidence without over-logging?
A signed receipt should support investigation, not become a duplicate repository of every prompt, response, and tool payload. Raw content may contain personal information, credentials, or other secrets, and duplicating it across telemetry systems increases exposure. Prefer a content reference, digest, or appropriately redacted excerpt when the full payload is not needed to answer the investigative question. If full content is necessary, store it separately with explicit access controls and a documented capture policy; test those controls.
- Decide which event classes require receipts and which content fields are necessary for each investigative purpose.
- Separate operational metadata from sensitive payloads, linking them with stable IDs where access to the payload is justified.
- Record policy and configuration changes that affect model behavior, and preserve the relevant version or digest with decisions that depend on them.
- Monitor capture health and log pipeline failures. An absent receipt may mean capture failed; it is not proof that no action occurred.
- Restrict and audit receipt access, including exports, and apply the organization’s retention requirements.
These practices align with the broader risk-management framing in the NIST AI Risk Management Framework and with OWASP’s guidance on verifying third-party agent execution evidence.
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What does the signature prove—and what does it not?
A valid signature can support the claim that the signed bytes have not changed since signing and that the holder of the corresponding key produced the signature, subject to assumptions about key custody, identity binding, algorithm, and verification policy. A trusted timestamp can support that a record existed by the asserted time. A transparency log can make later changes detectable.
None of those properties alone establishes that the described action truly happened, that the record was captured contemporaneously, or that every relevant event was submitted. As OWASP’s execution-evidence guidance puts it, “Do not treat a valid signature or log inclusion proof as proof that an action occurred.” A log cannot expose an event that was never sent to it.
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When verifying a receipt, state which property is being checked: signer identity, artifact integrity, execution binding, timing, or capture coverage. Keep expected values and the verification policy outside the evidence being reviewed. Where possible, reconcile sandbox-generated receipts with an independent boundary observer, and document what that observer could and could not see.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a team implement the receipt design?
- Define security-relevant events and boundaries. Specify which decisions and actions must be recorded—such as authorization outcomes, tool invocations, denials, and configuration changes—and which component observes each one.
- Version the schema and correlation IDs. Make required fields and identifier formats explicit, and ensure the same run and request can be traced across relevant services.
- Set content and provenance rules. Decide when metadata, a digest, a redacted excerpt, or separately protected payload storage is appropriate. Identify which model, tool, policy, configuration, and artifact references are needed for reconstruction.
- Define signing and verification policy. Document canonicalization, accepted algorithms, signer identity, key custody, trust references, and how verification failures are handled.
- Protect storage and detect gaps. Store authoritative receipts outside the workload where feasible, control and audit access, set retention, and alert on capture or ingestion failures.
- Test the evidence chain. Confirm that investigators can correlate a run, verify receipt integrity and signer, find relevant configuration provenance, and identify coverage limits. Test denial, failure, and logging-pipeline failure paths as well as successful actions.
When assessing a schema or logging architecture, compare event coverage and boundary visibility, actor attribution, integrity and key management, timestamp assumptions, privacy controls, retention and failure detection, cross-service correlation, and opportunities for independent corroboration.
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