Organizations now face external digital risks from look-alike domains, leaked credentials, dark web posts, impersonation profiles, and public technical traces that security teams cannot control. The article examines artificial intelligence in Digital Risk Protection and external threat intelligence as an analytical layer for collecting, classifying, prioritizing, and routing external risk signals. The study draws on recent academic publications, systematic reviews, industry guidance, and the MITRE ATT&CK Reconnaissance Framework. Source analysis, comparative analysis, conceptual synthesis, typological classification, and analytical generalization guide the research. The article distinguishes Digital Risk Protection from classical cyber threat intelligence. It explains the use of AI in domain similarity detection, homoglyph analysis, dark web monitoring, language-based extraction, and alert ranking. It replaces a linear workflow figure with a table that connects monitored sources, AI functions, and operational outputs. The proposed model suits enterprise security teams that need defensible prioritization before takedown, identity response, legal review, or SOC escalation.
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