AI agents are transforming how users interact with digital platforms—but they’re also creating unprecedented fraud and abuse vectors. Arkose Labs provides the only platform purpose-built to classify, manage, and mitigate agentic AI threats while enabling legitimate automation to flow seamlessly.
Agentic AI Security Platform
Intelligent Classification and Economic Disruption for the AI Agent Era
The Agentic AI Challenge
Traditional bot detection assumes automation equals malicious intent. But the emergence of AI agents has made this binary thinking obsolete. Today's reality is more complex:
Legitimate agents from payment processors, customer service tools, and authorized integrations perform valuable business functions
Malicious agents execute sophisticated fraud operations—account takeovers, promotional credit abuse, payment fraud—with human-like behavior that evades legacy detection
Gray area agents like zero-click search bots operate without transparency, creating difficult-to-quantify business impact
The fundamental problem: these agents are technologically indistinguishable. They look exactly the same technically, requiring a complete rethinking of fraud prevention strategies.
Why Legacy Detection Fails Against AI Agents
Traditional bot management relied on assumptions that no longer hold in the agentic AI era:
Behavioral patterns no longer distinguish bots from humans
Agentic AI mimics human randomness with disturbing accuracy, rendering behavioral analysis ineffective
Rigid scripts are obsolete
AI agents adapt strategies in real time based on the defenses they encounter, learning and evolving continuously
Friction doesn't exhaust AI agents
Unlike human attackers, AI agents have infinite patience and willpower, making traditional high-friction defenses obsolete
CAPTCHAs are broken
Standard large language models now achieve 80%+ solve rates on traditional CAPTCHAs—the cornerstone of bot defense for decades
The question has fundamentally changed from ”Is this a bot?” to “Is this agent authorized to do what it’s trying to do?”
Beyond Detection: The Classification Imperative
The agentic AI era demands a fundamental shift in approach. This isn't about detecting automation anymore—it's about classification, management, and governance at scale:
- Your good agents need to work seamlessly without friction
- Bad agents need to be stopped economically through progressive challenge escalation
- Unknown agents need to be classified fast to determine authorization
We combine economic disruption with intelligent classification, forcing attackers to burn through costly API calls while your legitimate automation flows seamlessly. As agentic AI continues evolving, the companies that will reap the business benefits while staying protected aren’t those with the highest walls—they’re the ones who know exactly who’s at their door and can make that determination confidently and quickly.
Arkose Labs Dual-Track Defense Strategy
We approach the agentic AI challenge through two parallel innovation tracks that work together to classify agents and make attacks economically unviable:
AI Indicators of Fraud: Know Your Adversary
Rather than treating all traffic the same, our platform analyzes multiple dimensions to detect agentic AI even when they don't self-identify:
API Call Pattern Analysis
Reveals LLM agents making characteristic sequences (vision → reasoning → action) that expose their underlying architecture
Framework Detection
Network timing analysis and WebDriver properties expose AI wrappers and automation frameworks attempting to masquerade as legitimate browsers
Proof of Work Validation
Asses if agents are masquerading and claiming to be human users on devices. Agents run on cloud infrastructure and fail device consistency checks
Behavioral Consistency Analysis
Statistical models identify patterns that are “too perfect” or synthetically generated, including:
- Movement density showing sparse “teleportation” behavior versus continuous human trajectories
- Click timing exhibiting superhuman precision
Self-Disclosure Correlation
Examines user agent taxonomy, IP reputation, device intelligence, and other signals to correlate self-disclosure with indicators of fraud
AI-Resistant Mitigations: Raising the Economic Bar
Our mitigation strategy focuses on making attacks economically unviable through progressively applied challenges:
API Call Pattern Analysis
Reveals LLM agents making characteristic sequences (vision → reasoning → action) that expose their underlying architecture
Framework Detection
Network timing analysis and WebDriver properties expose AI wrappers and automation frameworks attempting to masquerade as legitimate browsers
Proof of Work Validation
Asses if agents are masquerading and claiming to be human users on devices. Agents run on cloud infrastructure and fail device consistency checks
Behavioral Consistency Analysis
Statistical models identify patterns that are “too perfect” or synthetically generated, including:
- Movement density showing sparse “teleportation” behavior versus continuous human trajectories
- Click timing exhibiting superhuman precision
Self-Disclosure Correlation
Examines user agent taxonomy, IP reputation, device intelligence, and other signals to correlate self-disclosure with indicators of fraud
The Arkose Labs Advantage
Economic disruption philosophy
Makes attacks unprofitable through progressive friction, not just detection that creates endless arms races
Comprehensive coverage
Combines agentic AI defense with device identification, behavioral analysis, phishing protection, email intelligence, scraping prevention, API defense, and bot management
Purpose-built for the AI agent era
Our platform is specifically engineered to address the unique challenges of agentic AI through our ongoing focus on detecting and mitigating bots.
24/7 Security Operations Center
Actionable insights from an extensive cross-industry intelligence network monitoring legitimate traffic and attack patterns across global enterprises
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