AI company introduces novel signals to combat the escalating influence of autonomous artificial intelligence, termed as Agentic AI.
In the rapidly evolving digital landscape, the distinction between beneficial automation and malicious activity driven by bots and AI agents has become increasingly crucial. To address this challenge, Fingerprint, a leading device intelligence company, has introduced advanced Smart Signals for bot and AI agent detection.
These Smart Signals are designed to help businesses accurately distinguish between legitimate automation, such as AI assistants and verified bots, and malicious activity. Key components of these Smart Signals include:
1. **Bot Detection Smart Signal:** This innovative feature detects dozens of bot detection and browser automation tools. It intelligently classifies each API request to determine if it is from a legitimate or malicious bot. Only verified beneficial bots and AI agents are trusted, ensuring businesses avoid blocking useful automation while stopping harmful bots.
2. **Virtual Machine Detection Smart Signal:** This new feature identifies virtual machines commonly used in sophisticated fraud schemes, providing an extra layer of security against advanced attack vectors by spotting environments often abused by malicious automation.
3. **Residential Proxy Detection:** Malicious actors often use residential proxies to mask their real IP addresses, making detection difficult. This signal confidently detects proxy use, helping to spot agentic-driven fraud that tries to leverage residential IP addresses to appear legitimate.
4. **Request Filtering:** Fingerprint maintains a list of known user agents used by AI companies for web scraping, model training, and repetitive automated tasks like scheduling. This filtering enables clients to exclude such legitimate AI agents and bots from fingerprinting, reducing unnecessary costs without compromising fraud detection capabilities against malicious AI-driven traffic.
Together, these Smart Signals provide real-time risk indicators based on device behaviour, environment, and configuration, empowering businesses to:
- More accurately identify and trust helpful automation. - Effectively block and mitigate malicious bots and AI agents. - Optimize operational efficiency by filtering out benign AI traffic. - Protect against increasingly sophisticated fraud schemes involving AI and automation.
Dan Pinto, CEO of Fingerprint, acknowledged the significance of this development, stating that bots and AI agents pose both a current risk and a rapidly evolving threat landscape. With bots making up over half of all internet traffic, 30% of which is classified as malicious, these Smart Signals are a timely response to the rise of intelligent automation.
These enhancements from Fingerprint provide enterprises with comprehensive visibility into visitor intent, enabling proactive defense against evolving attack patterns. By balancing security with automation benefits and reducing friction for legitimate users, these Smart Signals are set to play a pivotal role in the digital security landscape of the future.
[1] Fingerprint Press Release, [Date] [2] Gartner Predicts, "Gartner Forecasts Worldwide Artificial Intelligence Revenue to Reach $327.5 Billion in 2026", [Link] [3] Statista, "Share of total internet traffic generated by bots worldwide as of 2021", [Link] [4] Fingerprint, "About Fingerprint", [Link] [5] Fingerprint, "Fingerprint's Smart Signals for Bot and AI Agent Detection", [Link]
- Leveraging advanced technologies like artificial-intelligence, the Bot Detection Smart Signal in Fingerprint's system can intelligently classify each API request to distinguish between legitimate and malicious bots, thereby contributing to fraud detection.
- To combat sophisticated fraud schemes involving AI and automation, virtual-machine detection is a technology integrated within Fingerprint's Smart Signals, allowing businesses to identify virtual machines typically used in fraudulent activities.
