Note: This article discusses legal and regulatory considerations for web scraping. This is not legal advice. Consult with your legal counsel before implementing any of these recommendations.
Since 2022, a series of landmark court rulings and regulatory crackdowns - from high-profile settlements to the European Union (EU) AI Act - has drawn a hard line in the sand. Compliance missteps carry real consequences: legal liability, financial penalties in the millions, and reputational damage that can reach the boardroom.
But, while the compulsion to compliance is clear, achieving it can still be tricky - especially when you are an engineer or data developer more used to binary concerns like commits and tests than gnarly matters of legal interpretation and balance-of-risk estimation.
So, what does legal and regulatory compliance look like on the ground, in your system? Below is a practical guide to four crucial new compulsions, with concrete steps for technical implementation.
1. Respect rightsholders’ wish for opt-out
The EU AI Act obliges providers of General-Purpose AI Models (GPAI) to respect a website owner’s choice to opt out of being scraped for AI training. This is a legal requirement under Article 53(1)(c).
What to know
While the primary obligation rests with GPAI providers, the compliance burden is shared any web scraper supplying them with data is part of the value chain. Violations can trigger fines of up to €15 million or 3% of annual global revenue.
What to do
For technical teams, compliance means building the checks into the scraping workflow.
- Check for emerging standards like ai.txt or trust.txt hosted at the root of domains to parse datatrainingallowed directives.
- When scraping image assets, it's good practice to extract embedded metadata (EXIF, IPTC) to find "noai" tags.
- Cache these rules to avoid repeated fetches and log all compliance decisions for auditability.
2. Establish lawful basis for scraping personal data
Personal data such as names, emails, IP addresses, and any information identifying an individual, is governed by a web of data protection regulations. The EU’s GDPR set the global precedent with six lawful grounds for processing data, with "legitimate interest" being the de facto global standard for scraping data.
What to know
The financial stakes are enormous. In the EU, GDPR fines can reach €20 million or 4% of global revenue. In California, USA, the CCPA creates significant class-action risk with fines of $2,663–$7,988 per violation. Other jurisdictions, from Brazil (LGPD) to China (PIPL), have their own multi-million dollar penalties.
What to do
To avoid inadvertently collecting Personally Identifiable Information (PII), technical teams could adopt a two-pronged strategy: data minimization by design and automated detection.
- Design your schema upfront to include only essential business fields, use regex patterns to exclude common PII patterns, and exclude high-risk sources like social media profiles and community forums.
- Then integrate open-source tools like Microsoft Presidio into your data processing pipeline to identify and remove PII before it's stored.
PII exclusion should be an inherent part of your system that runs without manual intervention or legal review.
3. Stay on the right side of copyright
Copyright law, established in the 18th Century and actively litigated today, protects creative works from unauthorized reproduction. The law makes a critical distinction for web scrapers: facts are not copyrightable, but creative works are.
What to know
While you can typically freely scrape factual data like prices and product specifications, you cannot republish articles, reviews, or images without permission or, in the US, a valid fair use exception.
Determining fair use is complex. A four-factor fair use test examines the purpose and character of use, the nature of the work, the amount taken, and the market effect. However, fair use is case-specific, and courts have significant discretion in applying these factors. This unpredictability makes it risky to rely on fair use as a defense.
In the United States, the penalties for infringement are severe, with statutory damages ranging from $750 to $150,000 per work. The recent wave of AI-related litigation highlights the risk; in September 2025, Anthropic agreed to a $1.5 billion settlement for using copyrighted works in its training data.
What to do
To mitigate this risk, technical teams could implement a multi-layered filtering strategy.
- This begins with content-based filtering, using keywords to exclude creative works like articles and reviews.
- The next step is schema-based filtering, where the scraping schema is defined upfront to include only factual fields.
- When working with image assets, teams could check for copyright metadata using Python libraries like Pillow and consider using computer vision to classify and exclude high-risk image types like artwork.
Finally, documenting these filtering rules and maintaining audit logs demonstrate a good-faith compliance effort.
4. Honor clickwrap agreements
Terms of service (ToS) agreements are legally binding contracts. There are two main types of website terms of service:
- Browsewrap terms are linked at the bottom of a page and do not require explicit agreement.
- Clickwrap terms are those that are expressly agreed to - typically, logging into a site, checking a popup box or where a user clicks "I agree". They are highly enforceable, with a 70% court success rate.
What to know
If a ToS explicitly prohibits scraping, you must respect that prohibition. Breach of ToS can result in civil lawsuits with damages ranging from $10,000 to over $100,000.
What to do
To manage this, technical teams should implement a structured decision-making workflow where it is required to explicitly accept ToS in order to access the data.
- Use LLM-based parsing, like the approach described in the "Terminators" research paper, or regex-based keyword detection to find scraping prohibitions in the ToS.
- Based on the findings, the workflow could assign a risk level – for example, “high for explicit prohibitions in a clickwrap agreement, and lower for browsewrap – and log the final compliance decision.
This automated workflow doesn't replace legal review, but it provides a scalable, auditable process for making defensible compliance decisions at scale.
The path forward
Compliance doesn’t have to be hard.
Start with the strategy that most directly affects your business, review your processes regularly as regulations evolve, and build a culture where compliance is integrated in your web data collection operation.
By understanding and proactively implementing these four strategies, organizations can systematically tread the complexities of the modern web scraping’s legal landscape with confidence.
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