Arkose Labs, the fraud prevention and account security company, today introduced significant updates to its Fraud Deterrence Platform. New platform capabilities deliver enhanced risk detection and attack response, and this is backed by an industry-first credential stuffing warranty.
Speaking at Money 20/20 this morning, Arkose Labs CEO Kevin Gosschalk highlighted credential stuffing as one of the key threats to the digital economy, fueling large-scale, persistent attacks on user accounts, “In a constantly evolving threat landscape, businesses deserve more assurance from their fraud and account security vendors. Technology offerings focus on features and functionality, but fail to offer commercial assurance on outcomes in the event of a critical attack.”
As pioneers in the industry, Arkose Labs is the only provider in the space to offer a warranty on this fast-growing issue. The company’s Fraud Deterrence Platform is powered by artificial intelligence and offers a unique combination of advanced risk classification, anomaly detection through machine learning, and dynamic attack response that deters malicious activity long-term. This comprehensive approach protects across user touchpoints. It is the only platform to cover the full responsibility for credential stuffing attacks, with a warranty of up to $1 million in response expenses in the event of a successful attack.
Arkose Labs’ latest product release delivers a defense-in-depth approach to addressing evolving threats, such as attackers spoofing devices and leveraging machine learning to bypass user challenges. It also provides a new approach to intelligence sharing with customers, for full transparency and easier access to data for internal models.
“Arkose Labs is assembling extensive platform capabilities across both attack detection and attack response. We are committed to delivering a flexible and highly effective solution to help our customers stay ahead of evolving threats,” said Ashish Jain, Chief Product Officer of Arkose Labs.
He added, “The latest product enhancements include detection capabilities which are adapted to a world where attackers are spoofing devices and other identifying information. Customers also have easier access to the multi-layered risk insights that we use in our machine learning powered decision engine.”
Key Arkose Labs platform enhancements include:
ARKOSE DETECT
Arkose Labs’ improved risk engine has enhanced IP intelligence, and an innovative new approach to device intelligence that evolves with attackers’ evasion techniques. The system dynamically analyzes global device data and uses self-learning models to identify outlier signatures. These are purpose-built for identifying attackers that spoof device characteristics.
ARKOSE INSIGHTS
The Arkose Platform provides real-time visibility into each event in a session and easy-to-consume reason codes. This enables businesses to use Arkose Labs in internal models and gain holistic visibility into their traffic and throughput rates.
ARKOSE ENFORCE
New developments to Arkose Labs’ market leading challenge-response solution have delivered superior resilience to sophisticated, machine learning powered attacks. Also, key accessibility enhancements within the enforcement challenges now can accommodate a broad scope of disabilities across visual, auditory, motor, and cognitive categories.
For more information about Arkose Labs’ Fraud Deterrence Platform, please visit https://www.arkoselabs.com/technology-solutions/.
About Arkose Labs
Arkose Labs bankrupts the business model of fraud. Recognized as a 2021 Cyber Defense Magazine “Hot Company in Fraud Prevention”, its innovative approach determines true user intent and remediates attacks in real time. Risk assessments combined with interactive authentication challenges undermine the ROI behind attacks, providing long-term protection while improving good customer throughput. Arkose Labs is based in San Francisco, Calif., with offices in Brisbane, Australia and London, UK. For more information, visit www.arkoselabs.com or on Twitter @ArkoseLabs.
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Contacts:
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