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Put a durable intent record and policy decision between a retirement request and the API call that deletes or disables the domain. Execute only after the record is saved and the decision permits the action; otherwise stop or require approval. This article uses “domain retirement guard” as a name for that design pattern, not as an established industry standard.
How do I log intent before a destructive automation operation?
Make the guard a required stage in the workflow—not a log entry written after the operation. The sequence is: record the request, evaluate policy, persist the decision, execute if permitted, and attach the outcome to the original request. Intended documents a related runtime approach using structured intent and policy outcomes, including the operating rule “No Token, No Action.” That is a vendor-specific example, not a universal standard. Intended’s Authority Runtime Pipeline
- Capture the request. Create a unique intent record with the actor or service identity, target domain or resource, proposed action, environment, risk context, request time, and a correlation or idempotency key.
- Evaluate policy. Check the request against the applicable rules. Record the matched policy and rationale, and choose an explicit result: allow, deny, or require approval or escalation.
- Persist the decision. Ensure the request and its decision are durably stored before calling the destructive API. If approval is required, save its reference and wait for approval before proceeding.
- Gate execution. Call the API only when the durable record exists and the decision permits execution. If logging or policy evaluation fails, do not bypass the guard for a destructive action.
- Record the outcome. Attach the completion status, platform operation ID, and any error to the intent’s correlation key. Keep this outcome distinct from the pre-action decision.
The key property is ordering: the system can later show what was proposed and authorized before it shows what the platform did. A post-action event alone cannot establish that the action was approved in advance.
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What should a destructive action audit record contain?
There is no single mandatory schema in the cited guidance. The following fields combine the structured-intent example from Intended with the details needed to investigate a later platform event. Intended’s Authority Runtime Pipeline
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| Field | What it establishes |
|---|---|
| Intent ID and correlation or idempotency key | Connects the request, decision, approval, and execution outcome. |
| Actor or service identity | Shows who or what initiated the request. |
| Target and environment | Identifies the domain or resource and whether it is, for example, production or a test environment. |
| Proposed action | Describes the intended operation precisely enough to distinguish retirement from a reversible change. |
| Request time and risk context | Places the proposal in context for review and incident reconstruction. |
| Policy decision and rationale | Records the result—allow, deny, or approval required—and why it was reached. |
| Policy and approval references | Identifies the policy applied and, when applicable, the approval that authorized execution. |
| Execution outcome | Records status, platform operation ID, and any error against the original intent. |
Use access controls and retention appropriate to the sensitivity of these records. The sources cited here do not specify a universal retention period, so set one according to your organization’s audit, security, and operational requirements.
Why isn’t a platform activity log enough?
Intent and activity records answer different questions. The guard’s record captures what was requested and whether policy authorized it; the platform log corroborates which API activity occurred. AWS describes CloudTrail events as records of activity and cautions that log files are not ordered API stack traces. A platform event therefore should not be treated as proof that a request passed a pre-action authorization check. AWS: Understanding CloudTrail events
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Check event coverage for the exact operation you need to observe. In AWS CloudTrail, S3 object operations such as DeleteObject are data events. Data events are not enabled by default for trails and event data stores; event selectors let you choose coverage, and additional charges may apply. AWS: Logging data events
For centralized records of automated changes and manual actions, HashiCorp also describes audit trails as a way to bring audit data together. That guidance is about audit collection, not a substitute for placing a durable policy gate before your destructive call. HashiCorp: Audit trails
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What should happen if the guard cannot decide?
Choose the failure behavior explicitly. For irreversible operations, a prudent default is to stop when the intent cannot be saved or policy cannot be evaluated, and provide a clear retry or escalation path. Intended documents fail-closed behavior when its runtime loop fails; that is evidence of one vendor implementation, not a feature guaranteed by every automation platform. Intended’s Authority Runtime Pipeline
- Logging unavailable: Do not execute; preserve the request for retry or route it to an operator.
- Policy service unavailable: Do not infer approval. Stop and alert the responsible team.
- Policy denies the request: Record the denial and rationale, then block the call.
- Approval required: Hold execution until the approval is recorded and linked to the intent.
- Execution fails after approval: Store the error and operation ID, if available, against the same correlation key; do not silently create a second unlinked request.
How do I test a guard before enforcing it?
Start by evaluating proposed policy without blocking the underlying operation, where the platform supports that mode. Google Cloud Organization Policy’s dry-run mode logs policy violations without blocking actions; this behavior is specific to that service. Google Cloud: Test organization policies
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- Exercise representative requests. Include expected approvals, denials, legitimate exceptions, and ambiguous cases.
- Inspect recorded decisions. Confirm the actor, target, action, context, policy rationale, and correlation key are present and searchable.
- Compare against platform activity. Verify that the relevant API events are actually captured; do not assume default logging includes every operation.
- Review false positives and gaps. Adjust policy and routing before enforcement, rather than allowing operators to bypass the guard.
- Enable enforcement deliberately. Define an owner, alert path, and rollback procedure that disables or relaxes enforcement without deleting the audit trail.
Policy-as-code validation before deployment is another related control discussed in HashiCorp’s automation guidance. Its treatment of manual approval gates highlights that approvals can add control while also adding friction; use them for the actions and risk levels that warrant human review. HashiCorp: Implement fully-automated deployments
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Implementation checks before production
- Confirm the intent record is durable before the destructive API call can run.
- Make allow, deny, and approval-required outcomes explicit, with a documented failure path.
- Use one correlation key across intent, decision, approval, and execution outcome.
- Verify event coverage for the specific API operation and account for event volume, retention needs, and any logging charges.
- Limit who can alter policy, approve a request, or edit audit records, and preserve enough history for review.
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