The internet is a vast data repository, with much of its content collected and processed by automated systems. Techniques like data scraping are widely used today in business, marketing, and research to gather information from online sources on a large scale and at high speed.

However, these technologies can also be abused, especially when used to mass-copy personal data or exploit information without user consent. When exceeding permissible limits, data collection can violate website terms of use as well as legal regulations on privacy and data protection.
This article will analyze how unauthorized data collection works in practice, situations that can lead to legal or ethical risks, and measures to minimize the risk of your website or data being illegally exploited.
What is data collection?

Data scraping is a general term referring to methods of extracting information from online sources such as websites, databases, or electronic documents. Its goal is to retrieve specific data and convert it into a format that can be stored, analyzed, or reused. In many cases, scraping is a step within a broader data mining process, but the term emphasizes the extraction action itself.
Comparison between Manual and Automated Scraping
Data scraping can be performed manually or using automated tools. Manual scraping is when an individual accesses a website and copies information into a document or spreadsheet. Conversely, automated methods use bots, specialized software, or scripts to perform the same task at a significantly faster speed and scale.
Scraping tools can analyze page structure, extract displayed content, mine data via APIs, or automate browser loading and repeated page reading. Automation is the key factor that makes collecting large amounts of data in a short time easy — but it also leads to problems such as service term violations, server overload, or privacy breaches.
Distinguishing between data scraping, web crawling, and hacking
Automated data collection is often confused with web crawling and hacking, but they are fundamentally different.
Web crawling
Web crawling is primarily associated with search engines. Data crawlers (or bots) systematically browse the internet, tracking links to discover and index new or updated websites. Their goal is to build a search index that allows users to quickly access information. This activity usually adheres to the robots.txt file and is a symbiotic relationship between the website and the search engine.
Meanwhile, scraping goes beyond simply indexing; it extracts specific data points (such as prices, emails, and contact information) and stores them elsewhere. This “separation and reuse” step—especially when applied on a large scale—can give rise to legal issues.
Hacking
Cyberattacks (hacking) are distinctly different because they typically involve unauthorized access to protected systems. Unlike scraping, which primarily targets public data, hacking seeks to bypass security measures to steal confidential information, disrupt services, or cause damage.

However, even without “breaking” the system, the widespread collection of personal data can still make users feel compromised. For example, in 2025, researchers at the University of Vienna discovered a vulnerability in WhatsApp's contact search mechanism, allowing them to identify billions of accounts and collect public profile data. Although no encrypted messages were compromised, the incident raised privacy concerns and forced Meta to take corrective action.

This shows that regulators are increasingly concerned not only about whether data is public, but also how and for what purpose that data is used.
Is automated data collection legal?
The legality of scraping depends on the specific country and context. There is no single rule that applies globally.

Note: This content is for informational purposes only and is not legal advice.
When is scraping acceptable?

Collecting non-personal, public data that complies with terms of service is generally considered less risky. Many researchers, journalists, and businesses use this method to compare prices, track markets, or analyze trends.

Several organizations, such as the Ethical Web Data Collection Initiative and the Alliance for Responsible Data Collection, also promote transparent and responsible standards in data collection.
Common Legal Risks
Even when data is public, improper use can still violate:
Data protection and privacy laws (such as the General Data Protection Regulation – GDPR)

Content copyright

Website terms of service

Database rights (especially in the EU)

Computer abuse or cybercrime laws

If scraping bypasses technical barriers, exploits vulnerabilities, or accesses data requiring login, such behavior may be considered illegal.
How do businesses use scraping?

When done legally and ethically, scraping offers many benefits:
Price comparison: Price aggregator platforms collect publicly available data for users to compare.

Market research: Analyze consumer trends and behavior from publicly available data.

Brand tracking: Analyze sentiment from reviews and public posts using AI.

However, risks arise when data is linked to specific individuals or combined from multiple sources to build detailed profiles without the user's knowledge.
Impact on website owners
Large-scale scraping can cause:
Server overload, reduced performance

Increased bandwidth costs

Intellectual property infringement

Risks to users' personal data

If information is copied and misused, users can hold the website accountable even if the collection is done by a third party.
How to Protect Your Website from Unauthorized Scraping
There is no foolproof method, but a multi-layered strategy will significantly increase your defenses.
CAPTCHA and Rate Limiting
CAPTCHA helps distinguish between real users and bots, especially when activated during unusual traffic situations. Rate limiting controls the number of requests from an IP address or account within a given timeframe.
Bot Detection Tools and WAF
Services like Cloudflare offer bot management and web application firewall (WAF) solutions, helping to detect suspicious automated behavior and block unauthorized access.
Complexing Data Structures
Load content using JavaScript instead of static HTML

Blur or lightly encrypt transmitted data

Require login to access sensitive content

Additionally, when blocking bots, limit the display of error details to avoid providing information that allows the scraper to adjust their tools.

Tools for Protecting Individual Users
Individuals can also be affected when publicly available data is collected and aggregated. Ad-blocking, tracking-blocking, or anti-fingerprint recognition tools help reduce the risk of covert tracking.
Some services, such as ExpressVPN's Threat Manager, can block malicious scripts, while Identity Defender (in the US) provides alerts if data appears on the dark web. However, these tools only help reduce the risk — they cannot completely prevent data collection if you have already made it public.