Web Scraping Legality: Understanding the Issues and Best Practices

Explore the legality of web scraping, its challenges, and the best practices to follow. Read the article to safely navigate this area.

Key Takeaways

Web Scraping Legality: In France and Europe, scraping is not "universally allowed" or "universally forbidden." It's a technique (neutral), and it's mainly the targeted data, the way it's collected, and its reuse that determine whether it's legal... or RISKY.

If you remember one thing: you can sometimes scrape public data, but you can't do "whatever you want" with personal data, ignore the Terms of Use, or "harvest" a protected database.

Is web scraping legal or illegal in France/Europe?

Web scraping is often legal when it respects three layers at once:

Lawful access: You don't bypass barriers (unauthorized login, technical protections, etc.).
Lawful data: Little to no personal data, or with a legal basis under GDPR + informing individuals. Any company must inform the concerned individuals about how their data was collected and the purpose of this collection.
Lawful reuse: You don't violate intellectual property (copyright/sui generis rights on databases) and you respect the Terms of Use if they clearly prohibit it.

After this step, it's important to remember that companies must obtain consent from individuals before any marketing using data obtained through scraping.

👉 So the real answer: it depends (and that's normal). The CNIL also treats "harvesting (web scraping)" as a collection that can be based on legitimate interest, but only with measures to protect individuals.

Finally, it is essential for any company to minimize data collection to what is strictly necessary during web scraping.

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What laws/rules govern scraping?

Here are the "pillars" that almost always come up.

Legal Compliance Guide for Web Scraping in 2025
Legal Pillar ⚖️	Protection 🛡️	Point of Caution 😬	Concrete Example 🧩
GDPR (European Union) 👤	Personal data of European residents	– Respect the legal basis (Art. 6) – Mandatory information (Art. 14) – Principle of minimization	Extracting profiles with name and email requires strict GDPR management
Data Protection Act 🇫🇷	Specific French framework and CNIL's powers	– Increased control powers – Administrative sanctions – Local compliance	This law complements the GDPR for all activities on French soil
Sui generis rights on databases 🗃️	Financial and human investment of the producer	– Prohibition of substantial extraction – Protection under Art. L342-1 of the IPC – Risk related to reuse	Scraping 80% of a professional directory poses a major legal risk
Criminal sanctions (IPC) 🚨	Repression of attacks on databases	– Fines up to €300,000 – Sentences up to 3 years in prison – Article L343-4 of the Intellectual Property Code	Used in cases of characterized and malicious attacks on a data structure
Terms of Use and Contract Law 📜	Specific terms of use for each website	– Violation of a contractual prohibition – Risk of damages – Technical and legal blockages	Scraping a site that explicitly prohibits the use of bots in its legal notices
Penal Code (STAD) 🧠	Integrity of automated data processing systems	– Fraudulent access or maintenance (Art. 323-1) – Hindrance to system operation – Risk of server overload	Bypassing a technical protection or causing a denial of service by overly aggressive scraping
Commercial Prospecting ✉️	Protection against spam and respect for privacy	– Opt-in obligations for individuals – Right to object (Opt-out) in B2B – Clear information on the origin of data	Turning a scraped file into an emailing list without securing prior consent
Text & Data Mining (TDM) 🤖	Exception for text and data mining	– EU Directive 2019/790 – Right of reservation (machine-readable) – Use for research or AI purposes	The robots.txt file can serve as a basis for an explicit rights reservation

Note: The sui generis right protects databases against unauthorized scraping, notably through Article L. 342-3 of the Intellectual Property Code, which regulates the extraction and reuse of data.

Non-compliance with the Terms of Use can lead to legal action, particularly before the court of appeal, which has already condemned companies for fraudulent data extraction (e.g., Weezevent case). The Court of Justice of the European Union has also issued important decisions on the protection of digital media and the interpretation of copyright and sui generis rights.

For example, Company X was condemned for using data collected by scraping without the database producer's authorization. Additionally, companies have been sanctioned for collecting personal data without respecting GDPR principles.

Companies that violate web scraping rules face significant financial penalties.

When does scraping become illegal or (very) risky?

In the case of scraping personal data, legality depends on the type of data collected and its use. For example, collecting email addresses or phone numbers from websites or social networks is strictly regulated by the GDPR. Scraping contacts is a common practice in commercial prospecting campaigns, but it must comply with current regulations.

It's important to note that any violation of a site's terms of use during web scraping operations can lead to legal action or lawsuits.

collecting personal data from web pages or social networks using web scraping

1️⃣ You're handling personal data without a safety net

Even if it's "public," it's often still personal (name, photo, ID, email, number...).

You need a legal basis (often legitimate interest), inform individuals when it's indirect collection, and allow them to exercise their rights.

Mini-story: A startup scrapes "contact" pages to create a B2B list. On paper, it's "public." In practice, if you don't manage the info (Art. 14) + opt-out + minimization, it can become a CNIL case.

2️⃣ You're bypassing protections (or forcing entry)

CAPTCHA, blocks, areas behind login, technical restrictions... here you quickly shift to unauthorized access and potential criminal risks depending on the scenario.

3️⃣ You're "pillaging" a database (sui generis rights)

The sui generis right allows the producer to prohibit the extraction and reuse of a substantial part (quantity or quality). Even repeated collection of small parts can be problematic if systematic.

4️⃣ You're reusing as if it were "yours"

Copying descriptions, reviews, profiles, images, articles... may involve copyright or database protection, especially if you republish. (Scraping "collects" is not the same as "publishing.")

5️⃣ You're doing prospecting (marketing)

Scraping emails and then sending messages: you must comply with CNIL/CPCE rules (B2C vs. B2B, information, objection, etc.).

Best Practices: The "Legal Scraping" Kit (and Defensible)

Web Scraping Compliance Checklist: Reflexes and Evidence in 2025
Reflex ✅	Why it's IMPORTANT 🔎	Useful Evidence 🧾	Clear Purpose 🎯
Purpose (GDPR)	Precisely justify the use of collected data	– Internal intention note – Registration in the processing register	Answer the question: "Why am I collecting?"
Minimization ✂️	Reduce legal risks by limiting volume	– Mapping of extracted fields – Justification of each data's usefulness	Collect only what's strictly necessary for the project
Avoid sensitive data 🚫	Health data, opinions, or origins increase penalties	– Automated cleaning filters – Exclusion rules by keywords	Avoid high-risk data categories
Legal basis ⚖️	Legitimate interest is not automatic in scraping	– Balancing test (LIA) – Simplified impact analysis	Move away from "default" to secure legality
Information Art. 14 📣	Obligation to inform individuals in case of indirect collection	– Dedicated information page – Notification message template	Ensure transparency with concerned individuals
Respect for objection 🛑	Guarantee the right to withdraw or forget individuals	– Exclusion list (Robinson) – Logs of processed refusal signals	Manage opt-out and prohibited sources
Security & Duration 🔐⏳	Limit data exposure time and access	– Data retention policy – Access control register	Keep for less time to reduce impact
Reasonable load 🧯	Do not hinder the technical functioning of the target site	– Rate-limit configuration – Monitoring response times	Avoid overload and risk of complaint for STAD
Traceability 🕒	Demonstrate good faith and rigor during an audit	– Timestamped extraction logs – Versioning of used scripts	Strengthen trust in case of CNIL control
Terms of Use + Rights 📜	Contracts and intellectual property are friction points	– Screenshots of Terms of Use at date – Legal analysis Go/No-Go	Prevent major contractual disputes

Bonus (AI/training): if you're doing mining (text & data mining), check EU rules 2019/790: the exception exists, but holders can reserve their rights "appropriately" (often machine-readable).

What are the penalties for non-compliance?

penalties for non-compliance with the legal framework for web scraping

GDPR: potentially very high administrative fines (depending on severity), + injunctions, + reputation.
Database (IPC): infringement of producer's rights = up to 3 years and €300,000 (and more if organized crime).
Computer crime: fraudulent access/hindrance of an STAD = criminal risks depending on facts.
Civil/contractual: damages, injunctions, blocking, access termination (Terms of Use).

Collection vs. Use: The Difference That Changes EVERYTHING

Data Lifecycle: Stages, Key Questions, and Risks 2025
Stage 🔁	Simple Question ❓	Main Risk ⚠️	Concrete Example 🧩
Collect 📥	"Do I have the right to take?"	– Unauthorized access (STAD) – Server overload – GDPR non-compliance	Automated extraction of public prices on a merchant site
Store 🗄️	"Do I have the right to keep?"	– Security breaches – Excessive retention duration – Purpose diversion	Keeping nominative data for 3 years without valid reason
Enrich 🧠	"Do I have the right to cross-reference?"	– Unconsented profiling – High risk (AIPD required) – Algorithmic biases	Cross-referencing social profiles to establish a prospecting score
Reuse 🚀	"Do I have the right to exploit?"	– Intellectual Property (IP) – Unfair competition – Spam (prospecting)	Republishing the entirety of a competitor's database on your own site
Resell/Share 🤝	"Do I have the right to distribute?"	– Unsecured transfer outside the EU – Violation of contractual clauses – Joint liability	Providing a scraped contact file to a third-party partner without a legal basis

You can have a collection that's "mostly OK" but a reuse that's forbidden (e.g., republishing a database, non-compliant marketing, training a model with reserved content, etc.).

Conclusion (Critical Opinion, Useful on the Ground)

Web scraping remains a legitimate practice in many contexts, particularly for market analysis, academic research, or competitive intelligence on truly public and unprotected data.

However, digital professionals must be aware that the legality of web scraping rests on a fragile balance between several legal frameworks.

The key takeaway is:

Always check the Terms of Use before starting a scraping project
Adopt a GDPR compliance stance from the planning stage
Distinguish between legal collection and legitimate use of data
Document every decision made regarding collection and use
Consult a legal expert in digital law for sensitive projects

European case law, notably the Ryanair v PR Aviation ruling, confirms that even in the absence of sui generis or copyright protection, site owners can contractually impose restrictions on access and use of their data.

In 2026, facing the risk of massive sanctions (notably historically significant GDPR fines), a cautious and documented approach becomes a strategic necessity, not just a recommendation.

To delve a bit deeper into Web Scraping, read the following articles:

Ethical Practices of Web Scraping

Scraping is not just about legality. It's also about respect: respect for people (data), the site (infrastructure), and the editorial work (content).

Anti-scraping techniques — Common Techniques to Block Crawlers

A good rule of thumb: if your extraction feels like a "silent copy" that disrupts the site or surprises users, you're already in a RISK zone.

How to scrape ethically?

✅ Use an official API when available (or an export/feed).
✅ Respect robots.txt (it's not "the law," but it's a clear signal).
✅ Read the Terms of Service and avoid explicitly prohibited uses.
✅ Maintain a reasonable pace (rate limit + pauses).
✅ Adopt an "audit-friendly" stance: purpose, minimization, traceability.

Why is it IMPORTANT?

✅ You reduce the risk of legal issues (contract/Terms of Service, personal data).
✅ You avoid technical blockages and "anti-bot" escalations.
✅ You protect your reputation (and that of your company).
✅ You keep cleaner and more stable data over time.

Ethics and Responsibility Guide in Web Scraping (2025 Edition)
Ethical Checklist 🧭	OK ✅	Caution ⚠️	Red flag 🚫
Page Access 🔓	Access to public pages – Without technical bypass	Gray areas (light paywalls) – Unclear usage restrictions	Security bypass – Forced or unauthorized access
Collected Data 👤	Non-personal data – Strictly necessary data	Public personal data – Requires GDPR framework	Sensitive or massive data – Profiling individuals
Server Load 🖥️	Gentle call pace – Use of backoff	Frequent request spikes – High volumes	Obvious site overload – Hindrance to operation
Transparency 📣	Displayed identity (User-Agent) – Clear contact + Logs	Vague or generic identity – Difficult to trace origin	Total IP disguise – Lack of traceability
Reuse ♻️	Internal statistical analysis – Global aggregation	Partial republication – Citations without agreement	Complete database copy – Theft of protected content

Common Techniques to Block Crawlers

Sites don't "hate" scraping by principle. They protect themselves because automation can quickly become an issue of load, fraud, competition, or intellectual property.

Today, robots (good + bad) weigh heavily in traffic. An Imperva report indicates that automated traffic has surpassed human traffic and that "bad bots" represent a major part of internet traffic.

Anti-Bot Defense Mechanisms and Strategic Impacts in 2025
Defense 🛡️	Objective 🎯	What it means for you 🧩
Rate limiting / quotas ⏱️	Prevent server resource saturation	– Need to slow down requests – Mandatory local caching – Drastic reduction of volume per session
CAPTCHA 🧩	Block aggressive automation attempts	– Immediate stop signal (STOP) – Requalification of need via official API – Request for license or commercial agreement
IP / ASN Blocking 🌐	Interrupt waves of suspicious requests	– Your behavior resembles an attack – Risk of being blacklisted – Need to review request sources
Behavioral Detection 🤖	Identify non-human navigation patterns	– Need for a more sober crawl approach – Prioritize quality over technical stealth – Avoid predictable repetitive patterns
Honeypots (Traps) 🪤	Identify and trap malicious robots	– Risk of collecting corrupted data – Negative impact on your technical reputation – Evidence of unauthorized scraping for the target site

Example: LinkedIn vs hiQ Labs (what it says… and what it DOESN'T say)

Web scraping LinkedIn HIQ — LinkedIn VS HiQ

This case is often cited, but it concerns American law (CFAA) and doesn't apply "as is" to France/EU.

What we learn in practice:

The court considered that scraping publicly accessible data didn't necessarily fall under the CFAA, even after a cease-and-desist, within the "authorization" debate.
However, the risk doesn't disappear: contract/Terms of Service, copyright, and other grounds can still be relevant.
The dispute ended with a confidential agreement (so no "total victory" usable as a universal rule).

The right takeaway: this type of case law mainly shows that "public" ≠ "free of all," and that platforms also defend an economic stake (data, services, competition).

Why Prevent Web Scraping on Your Website?

From the publisher's side, there are very concrete reasons (often combined).

✅ Performance: too many requests can degrade the site, even causing incidents (some attacks rely on saturation).
✅ Security: automation is also used to test vulnerabilities, perform credential stuffing, or scrape endpoints.
✅ Competition: tracking prices, customers, product pages, features…
✅ "Semi-public" data: information accessible but not intended to be extracted on a large scale.
✅ Content: risk of copying without attribution, loss of value for the creator.

Publishers' Motivations and Protection Strategies (Updated 2025)
Publisher Motivation 🏢	Perceived Risk 😬	Common Response 🔧
Infrastructure Stability	– Risk of downtime – Explosion of infrastructure costs – Degradation of response times	Implementation of Quotas – Deployment of WAF (Web Application Firewall) – Strict rate limiting
User Protection 👤	– Exposure of personal profiles – Massive harvesting for spam – Violation of customer privacy	Advanced CAPTCHA systems – Geographic or IP blocks – Update of Terms of Service rules
Business Protection 💼	– Simple cloning of the service – Loss of competitive advantage – Decrease in advertising revenue	Targeted contractual actions – Specific access restrictions – Database monitoring
Data Quality 🧼	– Data scraped then distorted – Loss of control over the source – Obsolescence of disseminated information	Opening of paid APIs – Licensing systems – Insertion of "trap" data (canaries)
Intellectual Property (IP) 🧠	– Unauthorized copying of protected content – Reuse without attribution – Economic parasitism	Automated duplicate detection – Legal actions – Technical content protection

Common Challenges of Web Scraping

Scraping rarely "works" once and for all. The obstacles mainly come from the reality of the web: it changes, it loads, it protects itself.

1. Unstable HTML Structures

A site can change a selector and break your extraction overnight.

➡️ Solution: automated tests + monitoring + alert thresholds.

2. Continuous Maintenance

A scraper without maintenance quickly becomes a machine producing false data.

➡️ Solution: versioning, sample validation, error logs.

3. Anti-Bot Measures

CAPTCHA, honeypots, blockages: often a signal that you're exceeding a limit (technical or contractual).

➡️ Solution: slow down, reduce, or switch to API/license — not "harden" the bypass.

4. Authentication & Sessions

As soon as a login is required, you enter a more sensitive contractual and technical zone.

➡️ Solution: check authorizations, Terms of Service, and legal framework first.

5. Quality & Latency

Slow sites = partial content, timeouts, inconsistencies.

➡️ Solution: error recovery, quality control, progressive collection.

Web Scraping Tools: Overview and Precautions

Web scraping relies on a wide range of tools, from "ready-to-use" solutions to advanced programming libraries.

Depending on your technical level and goals, you can choose visual scraping software (like ParseHub, Octoparse), browser extensions, Python frameworks like BeautifulSoup or Scrapy, or specialized SaaS platforms for online data extraction:

Web Scraping 2026: 6 Tools + 1 Checklist to Avoid 80% of Hassles
The Real Test 🎯	What You Get 💎	To Watch Out For ⚠️	Real Fit 🧭
ParseHub 🧲	Visual scraping “click → extract” 🖱️ Lists, records, quick exports (CSV/JSON) → You see the data drop… right away	Dynamic sites = sometimes fragile 🧩 Page changes → selectors to redo → Plan a verification routine	Beginner, occasional need 🚀 “I want to test an idea in 1 hour” → Without coding, without pipeline
Octoparse 🧰	Templates + scheduling ⏱️ Cloud possible for continuous running → Scraping becomes a small “job”	Steeper learning curve 🧠 Quotas, limits, options according to plan → Read the grid before scaling	SME, recurring extraction 📅 “I want the same data every week” → Without setting up a dev team
Chrome Extension 🧩	Ultra-fast capture of tables/lists ⚡ Perfect for simple pages, immediate exports → You go from page → Excel in 2 minutes	Pagination + infinite scroll = trap 🪤 Little control over retries/logs → OK for “one-shot,” not for production	Research, audit, validation 🔍 “I want a list now” → No need for architecture
BeautifulSoup (Python) 🍲	Clean and flexible HTML parsing 🐍 You control fields, cleaning, export → Perfect for “static” sites	Must manage requests + throttling 🚦 Respect TOS/robots, GDPR minimization → Otherwise, you block yourself	Light dev, custom need 🛠️ “I want to extract EXACTLY these elements” → And normalize them
Scrapy (Python) 🕸️	Scale crawl + pipelines + retries 📦 Logs, queues, scheduling… everything is planned → You switch to “serious collection” mode	Longer setup, mandatory rigor 🧱 Storage, security, data governance → Otherwise, it quickly becomes uncontrollable	Large volumes, multi-site collection 🏗️ “I want a reliable dataset” → With history and traceability
Playwright (Headless) 🎭	Modern JS sites, login, complex journeys 🧠 You “play” the page like a human → Where simple HTML fails	Anti-bot, captchas, blockages 🧱 Higher machine cost, scripts to maintain → Plan a plan B	SPA, dashboards, interactive pages 🖥️ “Content only exists after loading” → So browser automation
Compliance Checklist 🛡️	Data minimization ✂️ + clear purpose Logs & traceability 🧾 + limited retention → You can explain “why” and “how long”	TOS/Terms of Service + intellectual property 📜 Personal data = GDPR (rights, security, access) 🔒 → “Tech” scraping without framework = risk	Before the 1st run ✅ “I validate the framework, then automate” → Otherwise, you redo everything… later

Before launching a data collection project, take the time to assess the risks related to data protection and intellectual property.

Ensure that the chosen tool offers filtering, log management, and data security features. Finally, keep in mind that web scraping can raise technical issues (blockages, captchas, changes in page structure) and legal ones: compliance must remain at the heart of your approach, both in the choice of tool and in the implementation of the collection.

Use Cases of Web Scraping

Web scraping can transform how businesses and professionals leverage data from the internet. Among the most common use cases are competitive intelligence, price monitoring, collecting customer reviews, analyzing trends on social media, or aggregating content for market studies. Public websites, e-commerce platforms, and social networks are full of valuable information that, once extracted, can fuel strategic analyses or optimize marketing campaigns.

In the field of big data and artificial intelligence, web scraping can be used to build massive databases to train models, detect weak signals, or automate decision-making. However, every data collection must be framed by respecting the terms of service of the targeted sites, and the protection of users' personal data must remain a top priority. Failure to comply with these rules can lead to legal issues, sanctions, or damage to reputation.

In summary, web scraping can offer a significant competitive advantage when used responsibly and in compliance with GDPR principles. Before launching a project, it is essential to clearly identify the objective, verify the legality of the collection, and implement measures to ensure the security and confidentiality of the extracted information.

FAQ

Is web scraping legal in France and Europe?

Yes, sometimes: if access is lawful, if you comply with GDPR when personal data is involved, and if you do not violate rights/terms of service. The CNIL clearly regulates data harvesting with a focus on protecting individuals.

What rules govern data scraping?

The most common ones: GDPR (Art. 6, 14, etc.), Intellectual Property Code (databases), terms of service, and sometimes the Penal Code if there is bypassing/interference.

When does scraping become illegal or risky for data protection?

When you collect personal data without a legal basis/information, when you bypass protections, when you extract a substantial part of a database, or when you reuse data for non-compliant spam/solicitation.

Robots.txt: law or just a signal?

It is not "the law" itself, but it is an important signal (and, for some data mining uses, the reservation of rights can be expressed "appropriately," including machine-readable). Moral of the story: don't ignore it.

Scraping "public" data = free use?

No. "Public" does not mean "reusable without limits." GDPR + database rights + copyright + terms of service may apply.

What penalties could be faced?

They range from formal notices and injunctions to GDPR fines, and penalties provided by the Intellectual Property Code for infringing the rights of the database producer (up to criminal law).