Data subject control support

To meet the data subject control support requirement, you must provide practical mechanisms that let your customer (the controller) fulfill data-subject requests across the PII you process in your cloud service—access, correction, deletion, and related controls—without unsafe workarounds. Operationalize this by building request intake, identity verification, scoped execution workflows, and audit-ready evidence for every request and outcome.

Key takeaways:

• Build repeatable workflows for access, correction, and deletion requests, mapped to your data stores and subprocessors.
• Make the customer-controller’s job easy: clear interfaces, defined SLAs, and predictable outputs (exports, confirmations, exceptions).
• Keep proof: request logs, approvals, execution records, and exception handling tied to specific data locations and systems.

“Data subject control support” is an operational requirement that shows up as soon as your cloud service processes PII for a customer that acts as the controller. Controllers get the legal obligations; processors get the operational work. ISO/IEC 27018 frames this as a need to provide mechanisms that support customer and data-subject control requirements ¹. In practice, auditors and customers interpret that as: can you help your customer reliably execute data-subject rights across your platform, at production scale, without breaking security or losing track of what happened?

This requirement often fails for mundane reasons: unclear ownership between Support, Security, and Engineering; incomplete data maps; exports that omit derived data; deletion that misses backups or queued events; and “manual one-off” scripts with no review trail. If you want to operationalize quickly, focus on three deliverables: (1) a rights-request workflow that works end-to-end, (2) system-level capabilities (APIs, admin tools, and runbooks) that execute those requests safely, and (3) evidence that stands up in customer due diligence and ISO audits.

Regulatory text

Provided excerpt (non-licensed summary): “Baseline implementation-intent summary derived from publicly available framework overviews; licensed standard text is not reproduced in this record.” The requirement summary is: “Provide mechanisms supporting customer and data-subject control requirements.” ¹

Operator interpretation: you must be able to demonstrate—through product features, support processes, and technical controls—that your customer can exercise data-subject controls over the PII you process. That usually includes:

• Access: retrieve/export relevant PII.
• Correction/rectification: update inaccurate PII.
• Deletion/erasure: delete PII (or apply a defined alternative where deletion is constrained, such as legal hold or security logging).
• Scope control: honor the request for the correct identity, tenant, dataset, and time range.
• Proof: produce an audit trail of what you did and when.

ISO/IEC 27018 is a privacy code of practice for cloud PII processors; customers expect these mechanisms to exist even if you are not directly handling requests from data subjects. Your customer is usually the request “front door,” and you are the execution layer.

Plain-English requirement: what “support” means

“Support” is not a policy statement. It means you provide real mechanisms that can be used repeatedly:

• A defined intake path for customer instructions (portal ticket type, API endpoint, or formal support channel).
• A way to validate the request (correct tenant, correct individual, correct data scope).
• A way to execute in systems (export, update, delete) with appropriate approvals and segregation of duties.
• A way to confirm results (completion notice, export file, list of systems touched, exceptions).

If your current approach is “Engineering can run a script if asked,” you are exposed. You may still pass a lightweight questionnaire, but you will struggle with deeper due diligence, ISO audits, and enterprise customer negotiations.

Who it applies to (entity + operational context)

Applies to:

• Cloud PII processors providing services where customer data includes PII ¹. Common examples:
• SaaS platforms storing end-user profiles, identifiers, support tickets, telemetry tied to users, or HR/customer records.
• Managed services or hosting where you administer systems containing customer PII.
• Any environment using subprocessors (cloud infrastructure, support tools, communications tools) that may hold PII relevant to a request.

Operational contexts where this becomes urgent:

• You sell to regulated customers (financial services, healthcare, public sector) with strict due diligence.
• You process PII across multiple regions/tenants and need consistent execution.
• You have distributed storage (production DBs, analytics, logs, backups) and cannot easily prove completeness.

What you actually need to do (step-by-step)

Step 1: Define the supported “control actions” and your role

Create a one-page statement that answers:

• Which actions you support: access/export, correction, deletion, and any additional actions your customers contractually require.
• Whether requests come from customers only (typical processor model) or you also accept requests from data subjects directly (less common; higher operational risk).
• Which datasets are in scope (production records, attachments, derived fields, analytics, support tooling).

Deliverable: “Data Subject Controls Support Statement” owned by Compliance/Privacy with Engineering sign-off.

Step 2: Map PII locations and identifiers used to target records

You cannot execute requests reliably without a targeting model.

• Identify primary keys: user ID, email, phone, external subject ID, device ID, customer’s internal ID.
• List systems storing PII: app DBs, object storage, search indexes, analytics warehouses, logging platforms, support ticketing, CRM, email delivery tooling.
• Mark which systems are authoritative, replicas, or derived.

Deliverable: a data map that links subject identifiers → systems → deletion/export method → owner.

Step 3: Implement request intake and authentication of customer instructions

Set up one controlled path:

• Portal form or ticket type specifically for rights requests.
• Required fields: tenant, subject identifier(s), request type, scope notes, requester identity/role at customer.
• Authentication: require the customer to submit via authenticated admin account or pre-authorized support contacts.

Control point: deny or hold requests that arrive through untrusted channels or lack tenant authority.

Step 4: Build execution runbooks (and automate where possible)

For each request type:

Access/export

• Define export format(s): CSV/JSON, attachments packaging, and encryption method for delivery.
• Include both “provided by subject” data and “observed/derived” fields where applicable to your service’s data model.
• Add a review step to prevent cross-tenant leakage.

Correction

• Define what is editable vs system-generated.
• Log before/after values with actor and timestamp.
• Ensure corrections propagate to replicas/search indexes.

Deletion

• Define “deletion” per system: hard delete, cryptographic erasure, tombstoning, or irreversible anonymization.
• Handle queues and retries (events, async workers).
• Address backups: document how backups are managed and what restoration procedures do to prevent “resurrecting” deleted data.
• Manage exceptions: legal hold, security logs, fraud prevention records, or contractual retention. Document the rationale and customer communication template.

Deliverables: runbooks per system plus an execution checklist that operators must complete.

Step 5: Add guardrails (security, approvals, segregation of duties)

Auditors and customers focus on whether the mechanism can be abused.

• Restrict who can run deletion/correction jobs.
• Use peer review for manual scripts.
• Require ticket linkage for any production data changes tied to rights requests.
• Log all admin actions used to fulfill requests.

Deliverable: an access control matrix for request execution functions and evidence of logging.

Step 6: Create customer-facing operating terms

Customers need predictable handling.

• Published instructions for submitting requests.
• Clear definitions of what you can/cannot do.
• Commitments for status updates and completion confirmation.

This is where Daydream typically fits: teams use it to standardize third-party due diligence responses and ensure each claim about rights support is backed by current evidence, not tribal knowledge.

Required evidence and artifacts to retain

Keep artifacts that prove design and operation:

Design evidence

• Data map and system inventory for PII locations.
• Runbooks/workflows for access, correction, deletion.
• Role-based access control documentation for execution functions.
• Customer instructions or DPA addendum language describing request handling.

Operational evidence

• Request register: intake record, requester identity, tenant, request type, timestamps, status.
• Identity/authority checks (customer admin verification or approved contact list reference).
• Execution logs: job IDs, systems touched, before/after values (for corrections), deletion confirmations.
• Exception records: reason for partial denial, legal hold notes, scoped retention justifications, customer communications.
• Subprocessor coordination records when a subprocessor is involved in fulfillment.

Common exam/audit questions and hangups

Expect these questions in ISO audits and customer assessments:

• “Show me the last few rights requests and your evidence trail.”
• “How do you ensure you do not disclose data across tenants during export?”
• “Which systems are in scope for deletion, including analytics and support tools?”
• “What happens to deleted data in backups, logs, and archives?”
• “How do you handle derived data and identifiers shared across services?”
• “Who can execute deletion and how is that activity logged and reviewed?”

Hangups that trigger deeper testing:

• You cannot name all data stores that contain PII.
• You rely on ad hoc engineering intervention with no standardized approvals.
• Your deletion story ignores replicas, caches, and downstream pipelines.

Frequent implementation mistakes (and how to avoid them)

• Mistake: treating rights support as “Support handles it.” Fix: require Engineering-owned runbooks and system ownership, with Support as intake and coordination.
• Mistake: incomplete scoping (only the primary DB). Fix: maintain the identifier-to-system map and update it as part of change management for new data stores.
• Mistake: exports that leak other users’ data. Fix: enforce tenant scoping and add a second-person review for manual exports.
• Mistake: “delete” that is really “disable.” Fix: define deletion semantics by system and document what remains (e.g., security logs) with reasons.
• Mistake: no evidence. Fix: make the request register and execution checklist mandatory; no closure without attached logs/output.

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement. Practically, the risk shows up as:

• Failed enterprise security/privacy reviews and delayed deals.
• Increased breach exposure if request tooling can be abused for data exfiltration.
• Contractual noncompliance if DPAs require timely, provable assistance with rights handling.

Practical 30/60/90-day execution plan

First 30 days: establish control ownership and a minimum viable workflow

• Assign owners: Privacy/Compliance for policy, Security for guardrails, Engineering for system actions, Support for intake.
• Stand up a single intake path and a request register.
• Draft runbooks for the core systems that store the majority of PII.
• Define approval gates and who can execute each action.
• Publish customer instructions for submitting requests.

Days 31–60: expand system coverage and harden evidence

• Complete the PII location map across production, analytics, support tooling, and logs.
• Implement export templates and deletion/correction checklists.
• Add logging requirements and ticket linkage for all production changes tied to requests.
• Test with tabletop exercises using realistic scenarios (cross-tenant risk, partial deletion exceptions, subprocessor involvement).

Days 61–90: automate repeatable actions and operationalize continuous change

• Add self-serve/admin APIs for common actions where safe (export, deletion initiation, status).
• Integrate subprocessors into the workflow (handoff templates, confirmation capture).
• Add change-management hooks: any new data store must update the data map and rights runbooks.
• Package evidence for audits and customer due diligence; store it centrally so responses stay consistent (a common reason teams adopt Daydream).

Frequently Asked Questions

Do we have to accept requests directly from data subjects?

Often, as a cloud PII processor, you support the customer-controller rather than taking requests from data subjects. Define your intake model explicitly and make sure your mechanism supports the customer’s instructions end-to-end.

What systems are usually expected to be in scope for deletion?

Start with production data stores, then include replicas, search indexes, analytics stores, and support tooling where PII exists. If any system is excluded (for example, immutable security logs), document the reason and how you limit access and retention.

How do we handle backups when a customer requests deletion?

Document your backup approach and restoration controls so deleted records are not reintroduced into production during a restore. Keep an exception rationale if backups cannot be selectively purged, and explain your compensating controls and retention behavior to customers.

Can we fulfill access requests by sending screenshots or ad hoc exports?

You can, but ad hoc methods raise cross-tenant leakage and evidence problems. Standardize export formats, enforce approvals, and retain the export artifact and completion record in the request register.

What evidence do auditors ask for most often?

They typically want a sample of completed requests with timestamps, requester authority checks, execution logs, outputs (export file or deletion confirmation), and documented exceptions. If you cannot produce this quickly, your control will read as informal.

How does this connect to third-party risk management?

Customers will ask for proof that you can support their data-subject obligations as part of due diligence. Treat this as a third-party control: clear commitments, repeatable workflows, and evidence packaged for security reviews.

Related compliance topics

• 2025 SEC Marketing Rule Examination Focus Areas
• Access and identity controls
• Access Control (AC)
• Access control and identity discipline
• Access control management

Footnotes

1. ISO/IEC 27018 overview