This is the third article in our AI Agent series. In Part 1, we introduced Red Sift’s AI Agent for lookalike classification – an intelligent solution for handling the ambiguous cases that rule-based automation can’t confidently resolve, offering analyst-grade triage autonomously. In Part 2, we took readers behind the scenes to explore the engineering…
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In our previous blog post, we introduced Red Sift’s AI Agent for lookalike classification – an intelligent system that determines whether a suspicious domain has been deliberately crafted to mimic a legitimate one or if the resemblance is merely coincidental. That post focused on the what and why of the solution: why rule-based automation…
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Executive summary: AI has supercharged brand impersonation, with Q2 2024 seeing nearly half of all processed emails containing spoofing or phishing attempts—40% of which were AI-generated. The scale, speed, and sophistication of these attacks are overwhelming security teams, draining resources on false positives, and leaving critical threats undetected. Consumers are unforgiving when trust is…
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Executive Summary: Accurate logo detection is essential for protecting brands against misuse and fraudulent activities. Red Sift’s hybrid AI approach enhances detection precision, effectively balancing the reduction of false positives with the identification of genuine threats. This article: Introduction Logo detection is crucial for brand protection, helping identify logo misuse in lookalike domains and fraudulent…
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Relevance Detection is an AI-powered feature that automatically classifies the significance of identities found across sources such as DNS, WHOIS info, SSL certificates, and many others...Continue Reading: Red Sift Brand Trust launches first GPT-4 powered feature, Relevance Detection
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