Arkose Detect

While new attack signatures arise everyday, they all have one thing in common — they’re motivated by profit. By analyzing attacks for telltale signs of fraudulent intent, companies are empowered to undermine attacks that are caught in the web of early and accurate detection. With a defense-in-depth approach that turns intent data into actionable decisions, fraud & security teams can fight proactively against fraud and shift from mitigation to long-term deterrence.

Arkose Labs believes digital businesses need to be armed with a combination of real-time risk decisioning, machine learning & human analysis to uncover high-risk activity in real-time, while optimizing rapidly with attack patterns.

The right defence-in-depth solution provides long term firepower for fraud and security teams.

Arkose Detect empowers security and fraud teams to root out large-scale persistent attacks with real-time traffic risk classifications powered by multi-faceted machine learning and 24/7 analysis from our Security Operations Center. The detection engine uses a combination of real time aggregation, IP and device intelligence, traffic shaping, and behavioral analysis to identify good vs. malicious users and ensure attackers don’t go unnoticed.

Unlike black box solutions, our ‘clear box’ approach focuses on delivering actionable insights with clear explanations for risk classifications and a clear path to remediation. This goes beyond solutions that provide probabilistic risk scores, which often require a great deal of resources to integrate, fine-tune and define downstream authentication workflows. Our unique risk classification model uses insights from across our global network of telltales to distinguish between good and bad traffic with maximum accuracy, allowing companies to keep the occurrence of false positives and negatives to a minimum.

Arkose Detect carries out deep analysis of behavior and device heuristics to uncover subtle signs of fraud and allow companies to develop a response best-suited for their business and fraud-fighting strategy. This combination of real-time assessment works behind the scenes with shared intelligence from previous attacks to ensure accurate detection across a wide range of growing threats. Machine learning models carry out advanced anomaly detection to uncover subtle signs of malicious behavior.

Multi-Layered Detection

Arkose Labs detects high-risk activity using network data and analysis adapted to a business’ specific ecosystem. Our forecasting method is based on historical trends and powered by machine learning to help us achieve the greatest accuracy in detecting fraud.

AI-Powered Platform Intelligence

Arkose Labs empowers companies with an enriched set of global rules we call telltales which accurately detects ever-evolving attacks. Our decision engine uses globally shared intelligence that is rapidly calibrated with historical and network data for accurate decisioning about risky traffic.

Transparent Risk Insights

Understanding increasingly sophisticated attacks depends on more than risk scores from a blackbox approach. Arkose Labs provides transparent access to its insights through two-way data exchange and dashboards for more informed decisions.