DSAR Request Intake and Fulfillment
A data subject access request requires locating every piece of personal data your organization holds on that individual across every system that might contain it — the CRM, support tickets, marketing platforms, transaction records, log files, backups — within a statutory deadline that in the EU is one month, extendable in limited circumstances. Locating this data manually means someone querying system after system, and the scope of what counts as personal data is broader than the obvious fields: it includes data the individual didn't directly provide, like inferred risk scores, behavioral segments or derived profiling attributes, which are easy to miss because they don't live in a field labeled with the person's name.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 6-10 hrs per request for organizations handling regular DSAR volume across multiple systems.
How the automation works
We build a DSAR workflow that captures the request, verifies the requester's identity before any data is compiled, and runs an automated discovery sweep across your connected systems to locate personal data associated with that individual — including data linked by indirect identifiers, not just direct name or email matches, and including derived or inferred data such as scoring or segmentation outputs that count as personal data under GDPR even though the individual never directly provided them. The compiled results, source system for each item and any data the sweep couldn't confidently resolve are presented to a privacy officer for review before anything is sent to the requester — the sweep finds and compiles, it never sends a response autonomously, since a completeness error or an incorrect inclusion in a DSAR response carries real legal consequence.
Process flow
- 01
DSAR request received trigger
A subject access request, from any intake channel, opens a tracked case with the statutory response deadline calculated and displayed immediately.
- 02
Verify requester identity ai
Identity verification runs before any data compilation begins, since responding to an unverified requester risks disclosing personal data to the wrong person entirely.
- 03
Sweep connected systems for personal data integration
Connected systems are queried for data associated with the verified individual, matching on direct identifiers and cross-referencing indirect identifiers that link records without an obvious name match.
- 04
Classify data including inferred fields ai
Discovered data is classified against what actually counts as personal data under GDPR, explicitly including derived and inferred data — risk scores, behavioral segments, profiling outputs — not just directly provided fields.
- 05
Privacy officer review and approval output
A privacy officer reviews the compiled data set, checks for scope issues (over-inclusion or under-inclusion, third-party data mixed in) and approves the final response — no response is sent without this review.
- 06
Deliver response and log output
The approved response goes to the requester through a secure channel, with the full compilation, review and approval trail logged for regulatory accountability.
Inputs
- DSAR intake requests
- Requester identity verification data
- Connected system records (CRM, support, transactional, logs)
- Data classification and inference-field mapping
Outputs
- Verified and compiled personal data response package
- Privacy officer review and approval record
- Statutory deadline tracking per request
- DSAR fulfillment audit log
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Personal data under GDPR extends well beyond directly provided fields to include inferred and derived data — a behavioral segment, a churn-risk score, a fraud-risk flag — generated from someone's activity, and a discovery sweep scoped only to fields containing a name or email address will systematically under-report what a DSAR response is legally required to include.
- Responding to an unverified requester is itself a data breach risk — verification has to happen before compilation and disclosure, not as an afterthought, since a sufficiently motivated bad actor requesting someone else's data through a weak intake process can turn a DSAR fulfillment workflow into the disclosure vector.
- A discovery sweep can pull in data that belongs to or references a different individual — a support ticket mentioning the data subject by name within a conversation primarily about someone else — and including that third party's data in the response without redaction is its own compliance failure; the review step needs to specifically check for this kind of scope over-inclusion, not just completeness.
- This workflow must never auto-send a compiled response without human review and sign-off — a privacy officer needs to confirm the data set is complete, correctly scoped and doesn't inadvertently disclose another person's data, because an inaccurate or incomplete DSAR response carries direct regulatory exposure, and the statutory deadline pressure is exactly the condition under which skipping that review looks tempting and is most likely to produce a mistake.
Frequently asked questions
Does this send the DSAR response automatically once data is compiled?
No. A privacy officer always reviews the compiled data set for completeness and scope before any response is sent — the automation handles discovery and compilation, not the decision to disclose.
Does this catch inferred data like risk scores, not just fields with the person's name?
Yes, classification is built to include derived and inferred data — scoring outputs, behavioral segments, profiling attributes — since these count as personal data under GDPR even though the individual never directly provided them, and they're the category manual DSAR processes most commonly miss.
How does it prevent sending someone else's data by mistake?
The review step specifically checks for third-party data that may have been pulled in incidentally, such as a support ticket mentioning the data subject within a conversation about someone else, and that data is redacted or excluded before the response goes out.
What happens if identity verification fails?
The workflow does not proceed to data compilation until identity is verified, since disclosing personal data to an unverified requester is itself a potential breach — an unverified request is held and the requester is asked for further verification through the normal process.