Newspaper Publisher Gains Control Over Content Theft with Netacea Bot Protection
24%
£15 million

The Challenge
Content theft has emerged as a critical concern for media and news publishers. The rapid rise of generative AI (GenAI) has disrupted traditional revenue models, as automated systems increasingly scrape content from news websites without consent. These systems generate outputs based on stolen articles, editorials, and images, undermining the core revenue streams of publishers – advertising and paid subscriptions.
While this disruption presents challenges, it also opens new opportunities. Media organizations can establish financial agreements with GenAI businesses to license content. This has the potential top open new and highly valuable revenue streams. However, this strategy is only viable if publishers can identify scraper traffic and block unauthorized access effectively.
One of the world’s largest media publishers faced a growing problem with scraper bot activity across their extensive network of news websites. These bots facilitated content theft in multiple ways:
- Bypassing paywalls to access subscriber-only content.
- Extracting trainable data for GenAI models without permission.
- Increasing infrastructure costs by consuming server resources to deliver content to automated systems.
Despite having a rudimentary Web Application Firewall (WAF) in place, the client struggled with:
- Limited visibility into bot traffic.
- Inability to counter sophisticated, evolving bot tactics.
- High utilization of their AWS infrastructure by bot traffic.
The executive leadership team prioritized solving this issue. The organization needed better visibility, control, and protection to combat content theft effectively, without having to manually block bots themselves.
The Solution
To tackle the issue head-on, the publisher partnered with Netacea, a leader in bot protection for a fully managed bot protection service.
Step 1: Understanding the Bot Threat Landscape
Netacea’s Threat Intel Center conducted extensive research across hidden marketplaces and forums to uncover content scraping configurations targeting the client’s websites.
Step 2: Data-Driven Detection
Netacea seamlessly integrated with the client’s CloudFront CDN, ingesting web log data from key news sites. Using server-side analysis and machine learning algorithms, Netacea analyzed each web request’s intent to differentiate between benign visitors and malicious bots.
This highly scalable and adaptive approach made it impossible for attackers to detect and bypass Netacea’s defenses.
Step 3: Automated Identification and Response
The detection models quickly identified that 24% of all website traffic came from malicious bots. Further investigation revealed advanced techniques employed by attackers, including:
- Rotating IP addresses to evade detection.
- Using spoofed user agents to mimic human visitors.
Netacea’s machine learning algorithms automatically adapted to counter these evolving tactics, requiring no manual intervention from the publisher’s internal teams.
The Outcome
Netacea’s Bot Protection is now fully integrated across the publisher’s news websites, delivering tangible results:
- Reduced Infrastructure Costs: The publisher’s infrastructure team reported a 24% drop in AWS usage, saving operational expenses.
- Visibility into Bot Traffic: Through the Netacea portal, the organization can now see and analyze every bot attempting to scrape their content.
- Commercial Licencing of Ethical Bot Traffic: While malicious bots are automatically blocked, ethical scrapers that declare their identity can now be approached with financial agreements to license content access.
With Netacea Bot Protection, the publisher is not only addressing content theft effectively but also establishing new revenue opportunities in the era of generative AI.
By investing in robust bot protection technology, this organization has safeguarded its content assets, protected revenue streams, and positioned itself as a leader in managing the challenges posed by GenAI-driven content scraping.