GDPR — Article 15 access + Article 17 cascade

Crewship stores agent-authored content (peer cards, memory snapshots, inbox items) that may include personal data about end users. EU operators have an obligation under GDPR Article 15 (Right of Access) and Article 17 (Right to be Forgotten) to produce that data on request and to delete it on demand. This page is the operator playbook for both flows. It covers the two admin endpoints, the audit table that records every action, the idempotency contract, and the parts of the cascade that intentionally remain manual for the operator’s judgement.

Two endpoints, one audit table

Action	Endpoint	RBAC	Records to `gdpr_actions`
Export (Article 15)	`GET /api/v1/admin/users/{userId}/data`	`manage` (ADMIN+)	`action='export'`, no reason required
Delete cascade (Article 17)	`DELETE /api/v1/admin/users/{userId}/data`	`manage` (ADMIN+)	`action='delete'`, reason required (non-empty after trim)

The gdpr_actions audit table is the canonical record of every Article 15 / 17 attempt — successful or failed — keyed by (workspace_id, data_subject_id) for fast SAR query lookup. Each invocation creates a new row even when repeated against the same subject, so the audit trail captures the full history of how the workspace handled that subject.

Article 15 — exporting a subject’s data

A data subject contacts the operator (typically via support email or a privacy portal) requesting a copy of all personal data held about them. The operator finds the subject’s user_id in the workspace identity store, then:

curl -X GET \
  -H "Authorization: Bearer $ADMIN_TOKEN" \
  -H "X-Workspace-ID: $WORKSPACE_ID" \
  "https://$CREWSHIP_HOST/api/v1/admin/users/$USER_ID/data" \
  > sar-export-$USER_ID-$(date +%Y%m%d).json

The response is a single JSON bundle containing every row from the four tables that carry data_subject_id (the three data tables below plus gdpr_actions, the audit history — see “What’s NOT in the export” for the boundary):

{
  "subject_user_id": "usr_…",
  "exported_at": "2026-05-21T18:42:11Z",
  "scope": {
    "peer_cards": 23,
    "memory_versions": 117,
    "inbox_items": 4,
    "user_models": 1,
    "gdpr_actions": 2
  },
  "peer_cards": [ {...}, {...} ],
  "memory_versions": [ {...}, {...} ],
  "inbox_items": [ {...}, {...} ],
  "user_models": [ {...} ],
  "gdpr_actions": [ {...}, {...} ]
}

The handler returns 500 Internal Server Error on ANY query failure — even if a partial bundle was assembled from successful queries — to prevent an incomplete export from being handed to the subject as if it were authoritative. The audit row is still written with status='failed' so the attempt is recorded; the operator retries after investigating the underlying failure. The export is the canonical artefact the operator hands the subject. The format is JSON because it’s machine-readable for downstream redaction tooling; the operator is responsible for translating it into whatever the subject requested (PDF, CSV, etc.) if a specific format was named.

What’s NOT in the export

The export only covers tables that carry a data_subject_id foreign key:

peer_cards — agent-authored peer cards mentioning the subject
memory_versions — versioned memory blobs the agent wrote about the subject
inbox_items — inbox rows whose payload references the subject (e.g. persona-suggestion proposals about the subject)
user_models — the operator model: the subject’s role, ownership and stated working constraints. Exported with its body, not just the index row — it is the one surface here holding facts the subject stated about themselves, so a row count would not answer the access request
gdpr_actions — the audit history itself (always included)

Content the operator may also need to surface manually:

lessons.md entries that mention the subject by user_slug — agents append to a per-crew lessons file with free-form text; the cascade logs a warning naming the subject when its slug is found in any lessons.md but does not modify the file. Operators are responsible for redacting if the lesson text contains personal data. See Lessons memory tier.
Chat conversation history — the chats table is workspace-scoped not subject-scoped; if the subject’s user_id is user_id on a chat row, the operator filters those rows separately. Covered by Chat sessions retention.
Audit logs from internal/journal — operator-facing operations the subject performed are tracked in the journal; the operator queries by actor_user_id rather than data_subject_id because the journal is for what the operator did, not what was done to a subject.

Article 17 — cascade delete

A data subject requests deletion of all personal data (“right to be forgotten”). The operator runs:

curl -X DELETE \
  -H "Authorization: Bearer $ADMIN_TOKEN" \
  -H "X-Workspace-ID: $WORKSPACE_ID" \
  -H "Content-Type: application/json" \
  -d '{"reason": "GDPR SAR ticket #1234 from data-subject"}' \
  "https://$CREWSHIP_HOST/api/v1/admin/users/$USER_ID/data"

reason is REQUIRED. The handler rejects an empty or whitespace-only reason with 400 Bad Request, and the underlying gdpr_actions CHECK constraint rejects the same at the DB layer (defense-in-depth — even a future admin SQL bypass that skips the handler can’t land an unjustified delete row). Whitespace-only reasons like " " or "\n" are also rejected because they’re functionally blank for audit purposes.

What the cascade touches

Layer	Scope	Behaviour
`peer_cards` (DB)	rows WHERE `data_subject_id = $USER_ID`	Hard delete
`peer_cards` (disk files)	`peers/{user_slug}.md` and its `.lock` sentinel in each agent’s `.memory/`	Best-effort — `outputBasePath` must be configured + filesystem writable. Failures logged but do not block DB delete.
`memory_versions` (DB)	rows WHERE `data_subject_id = $USER_ID`	Hard delete
`memory_versions` (blob store)	content-addressed blobs deduplicated across the workspace	NOT auto-deleted in v107 — rows go but blobs left for cross-tenant dedup safety; tracked as PR-F16 for a separate orphan-GC sweep
`inbox_items` (DB)	rows WHERE `data_subject_id = $USER_ID`	Hard delete
`user_models` (DB)	row WHERE `user_id = $USER_ID` in this workspace	Hard delete
`user_models` (disk file)	`/crew/shared/.memory/users/{user_slug}.md` and its `.lock` sentinel in the subject’s most-active crew	Best-effort — `outputBasePath` must be configured. Failures logged but do not block the DB delete.
`keeper_requests`	All rows	Excluded — these record sidecar decisions and don’t carry user-attributable content beyond the agent_id
`lessons.md` content scan	Per-crew lessons files	Manual — the handler logs a warning naming the subject when it finds the user_slug in any lessons.md; operator decides whether to edit the file by hand
`chats`	All rows owned by subject	NOT auto-deleted — operator runs a separate query if the data subject was also a Crewship user; see Chat sessions retention

No per-subject marker survives the erasure. Each peer card and operator model is written under a <user_slug>.md.lock sentinel, and that sentinel is now removed with the file it guards — on the Art. 17 cascade, on crewship privacy user-model delete, and on the opt-out purge alike. It used to stay: a zero-byte file named after the subject, left in a directory whose other artefacts were all gone. user_slug is sha256(user_id ‖ 0x00 ‖ workspace_id)[:16] and carries no personal data, but it is recomputable by anyone holding the workspace id and a list of users, so a listing still answered “did this workspace ever hold a record about this person?” after the record itself was erased. A purge for a subject who never had a card or a model now writes nothing at all, rather than creating the sentinel it was called to remove.

The response confirms what landed:

{
  "action_id": "gdpr_act_…",
  "rows_deleted": {
    "peer_cards": 23,
    "memory_versions": 117,
    "inbox_items": 4,
    "user_models": 1
  },
  "warnings": [
    "lessons.md mentions user_slug=pavel_advine — manual review required"
  ]
}

Idempotency

Running the cascade twice for the same subject is safe:

Already-deleted rows are silently skipped (cascade is row-set based, not snapshot-based)
Each invocation creates a new gdpr_actions row, so the audit trail records BOTH attempts (operators sometimes re-run a cascade weeks after the first attempt to verify completeness — both runs land in the audit)
The action_id returned in the response is the audit row id, not a transaction id — quoting it back to the operator’s ticketing system gives a stable handle

This matters because regulators sometimes ask “did you delete this subject’s data on 2026-03-14 AND again on 2026-04-02 in response to two separate confirmation requests?” — the answer comes from SELECT * FROM gdpr_actions WHERE data_subject_id = ? AND action = 'delete' ORDER BY initiated_at and shows both runs without ambiguity.

Verifying a cascade landed

After running the cascade, the operator verifies completeness by re-running the export:

curl -X GET \
  -H "Authorization: Bearer $ADMIN_TOKEN" \
  -H "X-Workspace-ID: $WORKSPACE_ID" \
  "https://$CREWSHIP_HOST/api/v1/admin/users/$USER_ID/data"

A successful cascade returns:

{
  "subject_user_id": "usr_…",
  "exported_at": "2026-05-21T18:45:30Z",
  "scope": {"peer_cards": 0, "memory_versions": 0, "inbox_items": 0, "user_models": 0},
  "peer_cards": [],
  "memory_versions": [],
  "inbox_items": [],
  "user_models": []
}

…with all scope counts at zero. If any non-zero remains, the cascade either had a partial failure (check the previous gdpr_actions row’s status and error columns) or there’s data without a data_subject_id foreign key that won’t be reached by the cascade (escalate to engineering — likely a column added since the v107 migration).

Audit table reference

gdpr_actions schema (added in migration v107):

Column	Type	Notes
`id`	`TEXT PRIMARY KEY`	`gdpr_act_` + cuid suffix
`workspace_id`	`TEXT NOT NULL REFERENCES workspaces(id)`	Cascade to workspace delete
`data_subject_id`	`TEXT NOT NULL`	The user being acted on
`actor_user_id`	`TEXT NOT NULL`	The admin who initiated
`action`	`TEXT NOT NULL` CHECK IN `('export','delete','view')`
`scope_json`	`TEXT`	Per-table counts for delete; per-table row count for export
`initiated_at`	`TEXT NOT NULL`	RFC3339
`completed_at`	`TEXT`	NULL while in progress
`status`	`TEXT NOT NULL` CHECK IN `('in_progress','completed','failed')`	Defaults to `in_progress`
`error`	`TEXT`	Populated on `status='failed'`
`reason`	`TEXT`	Required for `action='delete'` via a table-level CHECK (`action <> 'delete' OR length(trim(reason)) > 0`)

Indexes:

idx_gdpr_actions_subject (workspace_id, data_subject_id) — fast SAR lookup
idx_gdpr_actions_initiated (initiated_at) — chronological browse

Querying the audit

Every Article 15 + Article 17 attempt for a subject:

SELECT id, action, status, initiated_at, completed_at, actor_user_id, reason
FROM gdpr_actions
WHERE workspace_id = ? AND data_subject_id = ?
ORDER BY initiated_at DESC;

All cascade deletes performed by an admin in the last 90 days:

SELECT g.id, g.data_subject_id, g.initiated_at, g.completed_at, g.reason
FROM gdpr_actions g
WHERE g.workspace_id = ?
  AND g.action = 'delete'
  AND g.actor_user_id = ?
  AND g.initiated_at > datetime('now','-90 days')
ORDER BY g.initiated_at DESC;

Failed exports the operator should retry:

SELECT id, data_subject_id, initiated_at, error
FROM gdpr_actions
WHERE workspace_id = ?
  AND action = 'export'
  AND status = 'failed'
ORDER BY initiated_at DESC;

UI surface

Both endpoints are reachable from Admin → Users: search for the person, open their row, and the two actions are there — Export data and Erase data. They used to live behind their own “GDPR actions” nav row, which carried its own user picker: a second roster of the same people the Users list already showed, kept in step by hand. The actions belong to a person, so they live where people live.

The roster searches by name, email or user id; one row opens at a time, because one of these actions is irreversible and two open panels invite the wrong one being used
Export data downloads the JSON snapshot; Erase data opens a confirmation dialog with a required reason field and an “I understand this is irreversible” checkbox
The dialog stays open during the async DELETE (no auto-close) — a failed call surfaces the error toast without wiping the operator’s input, so they can retry without re-typing

From a terminal, the same two operations are crewship admin gdpr export and crewship admin gdpr delete --reason … --yes, which is the better surface for a queue of requests. The reason field is the same one that lands in gdpr_actions.reason — operators are encouraged to paste their ticketing system’s identifier (“SAR ticket #1234”) rather than free-form text, so the audit query reads naturally.

What’s NOT exposed

Crewship intentionally does NOT provide:

A self-service deletion endpoint for end users — Article 17 requests are mediated by the operator because the cascade affects content the operator owns (agent decisions, audit records that have to survive operator’s own retention obligations)
A bulk delete endpoint (“delete every user matching this filter”) — every cascade is one subject at a time, by design, because each gdpr_actions row needs a justification specific to that subject
Auto-deletion on user account close — closing a Crewship user account doesn’t trigger Article 17 (the user might have other obligations to a different workspace tenant within the same Crewship instance); the explicit cascade endpoint is the only path

Cross-references

Threat model — Tenant isolation on internal-auth handlers — how the GDPR cascade endpoints fit into Crewship’s tenant-isolation surface area
Audit logging — broader audit log shape; gdpr_actions is a specialized table for the GDPR cascade subset
Encryption at rest — operator-side encryption that protects gdpr_actions rows in backups + filesystem snapshots
Agent memory — peer cards — how peer cards get created and what data_subject_id they end up with
Admin API — schemas for the two endpoints

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Configuration

Manifest Reference

GDPR — Article 15 access + Article 17 cascade

Two endpoints, one audit table

Article 15 — exporting a subject’s data

What’s NOT in the export

Article 17 — cascade delete

What the cascade touches

Idempotency

Verifying a cascade landed

Audit table reference

Querying the audit

UI surface

What’s NOT exposed

Cross-references

​GDPR — Article 15 access + Article 17 cascade

​Two endpoints, one audit table

​Article 15 — exporting a subject’s data

​What’s NOT in the export

​Article 17 — cascade delete

​What the cascade touches

​Idempotency

​Verifying a cascade landed

​Audit table reference

​Querying the audit

​UI surface

​What’s NOT exposed

​Cross-references

GDPR — Article 15 access + Article 17 cascade

Two endpoints, one audit table

Article 15 — exporting a subject’s data

What’s NOT in the export

Article 17 — cascade delete

What the cascade touches

Idempotency

Verifying a cascade landed

Audit table reference

Querying the audit

UI surface

What’s NOT exposed

Cross-references