The report discusses an automated approach using graph neural networks to proactively detect malicious infrastructure employed by threat actors in cyber attacks based on known indicators. It examines the relationships between different types of indicators, such as co-hosted domains, malware delivery URLs, and SSL certificates, which can reveal connections between seemingly unrelated infrastructure. The approach involves training a graph neural network classifier on these relationships to identify new malicious domains and infrastructure. Three case studies are presented, highlighting the effectiveness of this approach in uncovering large-scale phishing campaigns targeting postal services, financial institutions, and web skimmer operations.