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AEPD Spain: AI and Employee DPA Rules

AEPD issued 847 sanctioning resolutions in 2023 — the highest in the EU by number — and requires DPIAs for all AI systems processing personal data.

May 29, 20267 minute read
AEPD SpainSpanish GDPRAI DPIA Spainemployee monitoringSpanish data protection

AEPD Spain: AI and Employee DPA Rules

Updated for 2026

AEPD: EU's Top Enforcer by Volume

AEPD (Agencia Española de Protección de Datos) is Spain's privacy watchdog. It issued 847 fines in 2023. No other EU body came close. Total penalties that year topped €12M.

The agency works differently from most EU peers. It does not focus only on big fines. It also targets small firms, town halls, and mid-size groups. This spreads pressure across Spain's economy.

Top areas enforced in 2024:

  • Camera and biometric checks (29% of cases)
  • Marketing and unsolicited contacts (24% of cases)
  • Staff monitoring and HR files (18% of cases)
  • AI systems and auto decisions (15% of cases — rising)
  • Health and special-class records (14% of cases)

AEPD's AI DPIA Rule

The regulator's 2024 Guía de adecuación al RGPD de tratamientos con IA sets one clear rule. Any AI tool that handles personal records needs a DPIA (Data Impact Assessment).

GDPR Article 35 asks for DPIAs when processing poses a high risk. That is a context test. The Spanish body takes a stricter view. Its guide says any ML tool that touches personal records triggers the DPIA rule. No case-by-case risk check is needed first.

Spanish groups must run and file DPIAs for:

  • Customer-service chatbots
  • Hiring screening tools
  • Marketing tools
  • Text-processing models (including anonymization tools)
  • Any AI tool that handles staff or customer records

Each tool used in Spain needs its own DPIA file. This applies even if the tool seems low-risk.

AEPD Anonymization Standards

The agency's anonymization guide builds on CNIL's work. It adds Spain-specific rules for national IDs:

Spanish ID types:

  • DNI (Documento Nacional de Identidad): 8-digit number plus a check letter
  • NIE (Número de Identificación de Extranjero): Letter + 7 digits + letter, for foreign nationals
  • NIF (Número de Identificación Fiscal): Same format as DNI, used for tax
  • Número de Seguridad Social: Spanish Social Security number

The body notes that NER models often miss NIE numbers. Spain has a large immigrant population. Check your tools can find NIEs when you process files from non-Spanish nationals.

Spanish name patterns:

Spanish naming uses two surnames (apellidos compuestos). NER models trained on single-surname sets can fail here. The name "García López, Juan Carlos" has two surnames, not one. Spanish NER models must handle this.

AEPD Employee Monitoring Cases

Eighteen percent of cases involve staff monitoring. Spain limits employer control under the Estatuto de los Trabajadores (Workers' Statute). The regulator enforces these limits alongside GDPR.

Key positions from the authority:

  • Keyloggers: Covert keylogger use is a GDPR breach in most cases. Screenshot tools need written proof and a fair-use check.
  • GPS tracking: Allowed on work vehicles with clear notice to staff. Not allowed on personal vehicles.
  • Email checks: Allowed with prior written notice and a policy. Content review needs extra proof.
  • AI tracking tools: Any model that tracks staff behavior needs a DPIA. EDPB rules also apply.

Automated monitoring draws the most scrutiny from Spain's DPA.

AEPD-Compliant AI Documentation

Four document sets are required for Spanish groups using AI tools.

1. AI system inventory

List each tool that handles Spanish personal records. Note: system name, vendor, purpose, record types, keep period, and DPA status.

2. DPIA per system

Use the agency's published DPIA template. Cover:

  • Purpose, legal basis, record types, and recipients
  • A fair-use check
  • A risk review for people affected
  • Risk controls: both tech and process
  • DPO notes (where a DPO is required)

3. Technical controls record

For each tool, note the controls that block unauthorized access:

  • Pre-send filtering (PII removal before the model runs)
  • Access controls on outputs
  • Retention limits and their enforcement
  • Breach detection and response steps

4. Staff monitoring policy

If any tool monitors staff, add a written policy. State the scope, give notice to staff, name the legal basis, and show a fair-use check.

AEPD audits start with the inventory and DPIAs. Groups with these files ready resolve audits much faster. Our GDPR compliance guide covers document scope. Our security compliance overview explains tech controls. For Spanish PII detection, see our multilingual PII detection guide.

Sources

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About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

Related reading

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

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How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
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Plans in plain words

We sell credits, not seats.

One credit covers one short job.

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Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.