Balancing Security and Privacy: Web Bot Detection, Privacy Challenges, and Regulatory Compliance under the GDPR and...

Area of Science:

• Cybersecurity
• Web Infrastructure Analysis
• Privacy Engineering

Background:

• Web bots exhibit dual roles, enabling malicious activities (e.g., credential stuffing, scraping) and benign automation.
• Distinguishing between humans, good bots, and bad bots is crucial for modern web security.
• Large volumes of benign traffic complicate bot detection, posing privacy risks.

Purpose of the Study:

• To comprehensively analyze web bot activity from offensive and defensive viewpoints.
• To investigate technical challenges in bot detection and associated privacy risks.
• To explore the role of Privacy Enhancing Technologies (PETs) in balancing bot detection and user privacy.
• To examine legal and ethical considerations, aligning technical solutions with regulatory frameworks (GDPR, AI Act).

Main Methods:

• Analysis of web bot activity and detection challenges.
• Exploration of Privacy Enhancing Technologies (PETs) for data minimization.
• Examination of legal and ethical frameworks, including GDPR and the EU AI Act.
• Mapping technical bot detection solutions to regulatory compliance requirements.

Main Results:

• Identified critical challenges in distinguishing bots from human users in high-traffic environments.
• Highlighted privacy risks inherent in large-scale user monitoring for bot detection.
• Demonstrated the potential of PETs to mitigate privacy concerns while maintaining detection efficacy.
• Provided insights into aligning bot defense strategies with GDPR and AI Act compliance.

Conclusions:

• Effective bot detection requires balancing security needs with user privacy rights.
• PETs offer a viable pathway to enhance cybersecurity without compromising individual privacy.
• Adherence to legal and ethical standards, such as GDPR and the AI Act, is essential for responsible bot management.