---
license: apache-2.0
language:
  - fr
  - en
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - tabular-regression
  - text-classification
tags:
  - gdpr
  - privacy
  - data-protection
  - cnil
  - regulatory
  - france
  - enforcement
  - sanctions
  - legal
  - cookies
pretty_name: CNIL Sanctions 2011 – 2025
configs:
  - config_name: default
    data_files:
      - split: train
        path: cnil_sanctions_analysis.parquet
---

# CNIL Sanctions Dataset — 2011 → 2025

> A typed, statistics-ready dataset of **374 sanctions** issued by the French
> data protection authority (CNIL — *Commission Nationale de l'Informatique
> et des Libertés*) between **2011 and 2025**, structured into **34
> analytical variables** for quantitative research on GDPR / French privacy
> enforcement.
>
> Generated end-to-end (scrape → typed schema → per-row extraction → export)
> by **[Gemma Miner](https://github.com/moncifem/gemma-miner)** — an
> autonomous text-to-dataset agent that turns any website into a
> research-grade dataset in minutes.

## TL;DR — the big takeaways

- The CNIL became **roughly 5× more active** between 2014 and 2024 (18 → 86
  decisions per year). 2025 is on the same trajectory (83 decisions
  through Q3).
- The **simplified procedure**, introduced in 2022, now drives **80 % of
  yearly volume** — it has transformed CNIL enforcement from "a few
  high-profile cases per year" into a continuous stream of mid-size
  decisions.
- **Volume and money decoupled in 2024.** That year had the highest
  decision count on record (86) but the *lowest* aggregate disclosed fines
  in seven years (**€55 M**). The simplified procedure trades severity for
  throughput.
- **2025 then exploded to €487 M in disclosed fines** — on just two
  mega-decisions (€325 M and €150 M, both Sept 1, both cookies & consent).
  Total fines across 2011-2025: **€1.14 B**, dominated by a handful of
  adtech/big-tech rulings.
- The **single most common breach theme is security of processing**
  (Art. 32 GDPR — 36 % of all sanctions), followed by **information /
  transparency obligations** (34 %) and **data minimisation** (30 %).
  Cookie/consent appears in only 14 % of decisions but dominates the
  highest-value decisions.
- **Private companies** account for **74 %** of sanctioned entities; the
  public sector and associations together make up only ~14 %.

## Quick start

<details>
<summary><b>📥 Load with 🤗 datasets</b> (click to expand)</summary>

```python
from datasets import load_dataset

ds = load_dataset("moncefem/cnil-sanctions-2011-2025", split="train")
print(ds[0])
print(ds.features)
```
</details>

<details>
<summary><b>🐼 Load with pandas (no `datasets` install needed)</b></summary>

```python
import pandas as pd

df = pd.read_parquet(
    "hf://datasets/moncefem/cnil-sanctions-2011-2025/cnil_sanctions_analysis.parquet"
)
print(df.shape)         # (374, 34)
print(df.dtypes)
```
</details>

<details>
<summary><b>🦆 Load with DuckDB (in-process SQL)</b></summary>

```python
import duckdb

con = duckdb.connect()
con.execute("""
    CREATE VIEW cnil AS
    SELECT * FROM read_parquet(
      'hf://datasets/moncefem/cnil-sanctions-2011-2025/cnil_sanctions_analysis.parquet'
    )
""")
print(con.execute("SELECT n_sanction_year, COUNT(*) AS n, SUM(amount_fine_eur)/1e6 AS fines_meur "
                  "FROM cnil GROUP BY 1 ORDER BY 1").df())
```
</details>

---

## Charts at a glance

### 1. Enforcement volume is growing fast — and the simplified procedure drives it

![Sanctions per year, by procedure type](charts/yearly_volume.png)

Yearly counts were stable at ~10–18 from 2011 to 2021, then exploded after
the **simplified procedure** was introduced (loi du 24 janvier 2022).
2024 saw **86 sanctions**, of which **80 % were simplified** — a regime
change, not just a trend line.

![Share of simplified procedure](charts/simplified_share.png)

<details>
<summary><b>🔬 Reproduce these charts</b></summary>

```python
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_parquet("cnil_sanctions_analysis.parquet")
yearly = df["n_sanction_year"].dropna().astype(int).value_counts().sort_index()
simp = df[df["is_simplified_procedure"] == True]["n_sanction_year"] \
        .dropna().astype(int).value_counts().reindex(yearly.index, fill_value=0)
std  = yearly - simp

fig, ax = plt.subplots(figsize=(10, 5))
ax.bar(yearly.index, std,  label="standard")
ax.bar(yearly.index, simp, bottom=std, label="simplified")
ax.set_title("CNIL sanctions per year, by procedure type")
ax.legend(); plt.show()
```
</details>

### 2. Volume and money decoupled — and 2025 broke the trend

![Aggregate disclosed fines per year (€M)](charts/yearly_fines.png)

Two observations the chart makes obvious:

1. **2024 is the inversion year.** 86 decisions (highest ever) but only
   **€55 M** in disclosed fines (lowest since 2019). The simplified
   procedure produces mid-size sanctions; the CNIL went broad rather than
   deep.
2. **2025 is the rebound.** €487 M in nine months — almost all of it
   from two cases on Sept 1 (€325 M + €150 M, both consent / cookies).
   On a per-decision basis, 2025 is by far the most expensive year on
   record.

The €1.14 B aggregate hides a fat-tailed distribution: the median fine
sits at **€10 000**, but the top decile drives essentially all of the
monetary value. Practically every year past 2019 has one or two
decisions ≥ €40 M.

**Top 5 fines in the dataset**:

| Date       | Organisation type                                                   | Fine      | Main breaches                                                       |
|------------|---------------------------------------------------------------------|-----------|---------------------------------------------------------------------|
| 2025-09-01 | Société développant plusieurs services en ligne                     | **€325 M**| Cookies (information & consent) + unsolicited commercial messaging  |
| 2025-09-01 | Société de vente en ligne (vêtements, chaussures, accessoires)      | **€150 M**| Cookies (information & consent)                                     |
| 2021-12-31 | Services internet (moteur de recherche, plateforme de vidéos)       | **€150 M**| Cookie-refusal UX                                                   |
| 2020-12-07 | Société de services technologiques                                  | **€100 M**| Cookies + information + consent + opposition                        |
| 2022-12-19 | Vendeur OS / logiciels / matériels                                  | **€60 M** | Cookies & trackers consent                                          |

Cookies / consent under the ePrivacy directive (Art. 82 LIL) — not GDPR
fines per se — produce the most expensive decisions in the dataset.

<details>
<summary><b>🔬 Query the top fines yourself</b></summary>

```python
import pandas as pd
df = pd.read_parquet("cnil_sanctions_analysis.parquet")
top = (
    df.dropna(subset=["amount_fine_eur"])
      .sort_values("amount_fine_eur", ascending=False)
      .head(10)
      [["dn_sanction", "organism_type_raw", "amount_fine_eur", "main_breaches_raw"]]
)
print(top.to_string(index=False))
```
</details>

### 3. Most sanctioned entities are companies

![Sanctions by sector](charts/sector_breakdown.png)

| Sector                  | Share   |
|-------------------------|---------|
| Private company         | **74 %** |
| Public administration   | 10 %    |
| Professional individual | 7 %     |
| Association             | 5 %     |
| Political party         | 4 %     |
| Other                   | < 1 %   |

<details>
<summary><b>🔬 Compute the sector / breach-mix crosstab</b></summary>

```python
import pandas as pd
df = pd.read_parquet("cnil_sanctions_analysis.parquet")
breach_cols = [c for c in df.columns if c.startswith("is_breach_")]
ct = df.groupby("cat_sector_group")[breach_cols].mean().round(2)
print(ct.to_string())
```
</details>

### 4. Security failures dominate the breach mix

![Breach themes detected](charts/breach_themes.png)

Of the eight breach themes in the codebook, the top of the distribution is:

| Rank | Theme                              | n   | share |
|------|------------------------------------|-----|-------|
| 1    | Security of processing (Art. 32)   | 134 | 36 %  |
| 2    | Information / transparency         | 126 | 34 %  |
| 3    | Data minimisation                  | 111 | 30 %  |
| 4    | Rights of data subjects            | 83  | 22 %  |
| 5    | Lawful basis: consent              | 69  | 18 %  |
| 6    | Cookies / trackers (Art. 82 LIL)   | 51  | 14 %  |
| 7    | Special-category data (Art. 9)     | 38  | 10 %  |
| 8    | Sub-processor obligations          | 25  | 7 %   |

Most decisions involve **more than one** theme — the median
`n_breaches` per row is 2. Note the inversion between **frequency** and
**severity**: cookies/consent appears in only 14 % of decisions but
drives **most of the monetary value** (every fine ≥ €60 M in the dataset
is a cookies/consent case).

(These flags are detected from each decision's public CNIL summary;
`null` means the summary doesn't address that theme — it is **not**
evidence the breach didn't occur.)

---

## Suggested research questions

This dataset is sized for fast iteration on questions like:

1. **Has the simplified procedure changed the WHO of enforcement?** Compare
   sector mix in pre-2022 vs post-2022 decisions.
2. **Does sanction severity predict the breach mix?** Cookie/consent
   appears to over-index in the €40 M+ tail — is that statistically
   robust?
3. **Are public-sector and private-sector breach profiles different?**
   `is_breach_security` looks higher in the public set; quantify it.
4. **What share of fines are GDPR vs ePrivacy/cookies?** Many of the
   biggest "fines" in the dataset are L.82 cookies cases, not strict
   GDPR Art. 83. The `decision_raw` field lets you split.
5. **Year-on-year severity:** has the median (not just sum) of fines
   moved? Compute on `amount_fine_eur` excluding nulls.

<details>
<summary><b>🔬 Quick answer sketch for Q1 (pre-2022 vs post-2022 sector mix)</b></summary>

```python
import pandas as pd
df = pd.read_parquet("cnil_sanctions_analysis.parquet")
df["era"] = df["n_sanction_year"].apply(lambda y: "pre_2022" if y < 2022 else "from_2022")
print(
    df.groupby(["era", "cat_sector_group"]).size()
      .unstack(fill_value=0)
      .apply(lambda r: (r / r.sum() * 100).round(1), axis=1)
)
```
</details>

<details>
<summary><b>🔬 Quick answer sketch for Q5 (year-on-year severity)</b></summary>

```python
import pandas as pd
df = pd.read_parquet("cnil_sanctions_analysis.parquet")
agg = (
    df.dropna(subset=["amount_fine_eur"])
      .groupby(df["n_sanction_year"].astype(int))["amount_fine_eur"]
      .agg(["count", "median", "mean", "sum"])
)
agg[["median", "mean", "sum"]] = agg[["median", "mean", "sum"]] / 1e3  # €k
print(agg.round(1).to_string())
```
</details>

---

## Codebook (34 columns)

All columns are explicitly typed. Where a row lacks evidence in the public
summary, the value is **`null`** rather than a fabricated default (this is
deliberate — see "Limitations" below).

### Identification & metadata

| Column                | Type    | Description |
|-----------------------|---------|-------------|
| `id`                  | string  | Deterministic content-hash id (stable across runs) |
| `dn_sanction`         | date    | Decision date, ISO `YYYY-MM-DD` |
| `n_sanction_year`     | integer | Calendar year of the decision |
| `sanction_date`       | string  | Raw date string as scraped (`DD/MM/YYYY`) |
| `sanction_year`       | integer | Same year, kept for cross-checks |
| `organism_name`       | string  | Name of the entity (often anonymised) |
| `organism_type_raw`   | string  | Raw CNIL category label (French) |
| `main_breaches_raw`   | string  | CNIL "principal failings / themes" text (French) |
| `decision_raw`        | string  | CNIL "decision adopted" text (French) |
| `decision_title`      | string  | Full title incl. délibération id where present |
| `decision_url`        | string  | Légifrance URL of the published decision (when published) |

### Sanction type & severity

| Column                       | Type    | Description |
|------------------------------|---------|-------------|
| `has_fine`                   | boolean | Administrative fine present |
| `has_injunction`             | boolean | Injunction (mise en demeure) accompanies the decision |
| `has_astreinte`              | boolean | Periodic penalty payment (astreinte) attached |
| `has_warning`                | boolean | Public warning (avertissement public) issued |
| `amount_fine_eur`            | float   | Fine amount in euros (NaN when none / undisclosed) |
| `cat_fine_bucket`            | enum    | `none`, `under_10k`, `under_100k`, `under_1m`, `over_1m` |
| `n_decision_severity_score`  | integer | 1–5 severity proxy synthesised from sanction type + amount |

### Procedure & sector

| Column                       | Type    | Description |
|------------------------------|---------|-------------|
| `cat_procedure_type`         | enum    | `simplified`, `standard` (or null) |
| `is_simplified_procedure`    | boolean | True if the decision used the simplified procedure |
| `cat_sector_group`           | enum    | `private_company`, `public`, `association`, `professional_individual`, `political`, `other` |
| `is_public_sector`           | boolean | Sanctioned entity is a public body |
| `is_health_related`          | boolean | Health context (hospital, médecin, mutual, …) |
| `is_digital_platform`        | boolean | Online / platform context |
| `has_external_decision_link` | boolean | Decision links to a published Légifrance text |

### Breach / theme flags (extracted from each decision's summary)

| Column                       | Theme |
|------------------------------|-------|
| `is_breach_security`         | Art. 32 GDPR — security of processing |
| `is_breach_transparency`     | Information / transparency obligations |
| `is_breach_consent`          | Lawful basis: consent |
| `is_breach_data_rights`      | Rights of data subjects (access, erasure, opposition) |
| `is_breach_cookies`          | Cookies / trackers (Art. 82 LIL) |
| `is_breach_minimization`     | Data minimisation |
| `is_breach_processor`        | Sub-processor / contractual obligations |
| `is_involves_sensitive_data` | Special-category data (Art. 9 GDPR) |
| `n_breaches`                 | Integer count of distinct breach themes in the decision |

---

## How this dataset was built

This file was produced by **[Gemma Miner](https://github.com/moncifem/gemma-miner)**
in a single agent run — about **9 minutes** end-to-end on
`openrouter/google/gemini-3.1-flash-lite`:

1. **Harvest** — agent fetched the CNIL listing page, auto-generated a regex
   extractor over the HTML table, and queued 374 rows (one per sanction).
2. **Codebook design** — an LLM proposed ~30 typed variables matching the
   analytical brief (sector, procedure, breach themes, fine bucket,
   severity score).
3. **Per-row extraction** — for each sanction, an LLM read the French
   text and emitted a JSON object conforming to the codebook; the system
   then deterministically coerced values to the declared types (dates →
   ISO, numbers → float, enums snapped to nearest valid value, ambiguous
   booleans → null).
4. **Export** — parquet + CSV + this card + charts.

No fine-tuning. No labelled training data. Reproducible from the upstream
URL on any day.

<details>
<summary><b>🔬 Rebuild this dataset from scratch</b></summary>

```bash
pip install gemma-miner          # from https://github.com/moncifem/gemma-miner
export OPENROUTER_API_KEY=...    # any OpenAI-compatible provider works
gemma42                          # drops into the REPL

# in the REPL:
> Build me a statistics-ready dataset of CNIL sanctions from
  https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil with sector,
  procedure type, fine amount in EUR, and breach-theme flags.
```
</details>

---

## Limitations & honest caveats

- **CNIL anonymises most entities** by category ("OPÉRATEUR DE TÉLÉPHONIE
  MOBILE") rather than name. `organism_name` is therefore often generic.
  For the named, headline-grade decisions, follow `decision_url` to
  Légifrance for the authoritative text.
- **LLM-derived columns** were extracted from the *public CNIL summary*
  (≤ 1 KB of French text per row), not from the full deliberation. A
  `true` for `is_breach_security` is high-precision; a `null` / `false`
  means the summary doesn't discuss it — **not** that no security breach
  occurred. This is deliberate: the dataset preserves the null-vs-false
  distinction throughout.
- **Fine parsing** is robust on the majority but can misattribute on rows
  combining a fine + a liquidation d'astreinte; the raw `decision_raw`
  field is preserved so you can re-parse if needed.
- **Decision URLs** are present on ~75 % of rows. Simplified-procedure
  decisions are usually unlinked.
- **Sample size for some breach themes is small** (n < 20). Use Wilson
  or Jeffreys CIs rather than normal-approximation intervals when
  comparing rates across years or sectors.
- **The CNIL publishes only its own sanctions** — judicial fines and
  EDPB cross-border decisions where CNIL was supporting authority are
  not included.

---

## Citation

```bibtex
@misc{elmouden_cnil_sanctions_2025,
  title  = {CNIL Sanctions 2011-2025},
  author = {EL-Mouden, Moncif},
  year   = {2025},
  note   = {Generated by Gemma Miner from https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil},
  url    = {https://huggingface.co/datasets/moncefem/cnil-sanctions-2011-2025},
}

@software{elmouden_gemma_miner_2025,
  title  = {Gemma Miner: an autonomous text-to-dataset agent},
  author = {EL-Mouden, Moncif and contributors},
  year   = {2025},
  url    = {https://github.com/moncifem/gemma-miner},
}
```

Underlying decisions are published by the CNIL on
<https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil> and on
Légifrance; consult those sources for the authoritative text.

## Author & links

- 👤 **Moncif EL-Mouden** — [🤗 huggingface.co/moncefem](https://huggingface.co/moncefem)
- 🤖 **Gemma Miner** (the generator) — <https://github.com/moncifem/gemma-miner>
- 🇫🇷 **Source** — <https://www.cnil.fr/fr/les-sanctions-prononcees-par-la-cnil>

## License

[**Apache License 2.0**](https://www.apache.org/licenses/LICENSE-2.0).

Please attribute:

- the **CNIL** as the source of the underlying decisions, and
- **Gemma Miner** (<https://github.com/moncifem/gemma-miner>) as the
  dataset generator.