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How to Prevent Voice Phishing Attacks

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A Conversation with GoodLabs + Confluent

Available On-demand

Many financial institutions have implemented voice biometrics systems as a security measure to authenticate and verify the identities of their customers. However, with the rapid advancement of AI, particularly in voice cloning technologies, a pressing question arises: How effective are these systems against AI-generated cloned voices? As voice cloning AI continues to evolve, it is crucial for organizations to rigorously test and evaluate the resilience of their voice biometrics systems.

GoodLabs’ vishing penetration test strategy is designed to help financial institutions periodically assess and enhance their voice biometrics systems and evaluate potential vendors. Their AI vishing detector employs a multi-layered detection mechanism that incorporates acoustic analysis, linguistic pattern recognition, and behavioral anomaly detection to distinguish between genuine and AI-generated voice communications.

Join this webinar to learn how GoodLabs Studio utilizes Confluent and Apache Flink® to create their Vishing Penetration Testing System, and how it can help prevent voice phishing attacks.

Lydia HuangSr. Product Marketing Manager, Confluent

Lydia Huang is a Sr. Product Marketing Manager at Confluent, working with RSI partners to create joint GTM strategies. Prior to Confluent, she worked with product marketing at IBM, managing cloud security.

Hussain JaberDirector of Fraud and Financial Crime - Strategy and Engineering

Hussain Jaber leads efforts to raise awareness about the current landscape of fraud and the importance of using advanced software engineering to detect, prevent, and deter fraud for financial institutions.

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