Real-Time Threat Detection and Incident Response with Graph Analytics & ML

Presented by

Michel Schaler, VP Business Development, TigerGraph

About this talk

2021 has given rise to sophisticated ransomware-style attacks and increased cyber threats. Organizations are fighting the onslaught of threats from inside, from known threat actors, nation-states, etc. This sessions focuses on how machine learning and graph analytics work together to increase threat detection accuracy, decrease alert fatigue and false positives. With automation, security analysts can focus on the events that really matter. In addition, enhanced threat response management satisfies compliance demanded by regulators. Automation of threat analysis is key to your SOC teams’ success. This webinar covers: - New reporting rules for cyber security and other impacts on compliance - How to utilize watch list data and breach data in real-time with new technologies like master data matching, graph analytics, and machine learning. - Capabilities in visualization and human intelligence technologies that will increase throughput and provide valuable data analytics. - How to implement practical methods of cyber identification, complex dependency and case management using "human-in-the-loop" technology for higher accuracy and process streamlining

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TigerGraph is a platform for advanced analytics and machine learning on connected data. Based on the industry’s first and only distributed native graph database, TigerGraph’s proven technology supports advanced analytics and machine learning applications such as fraud detection, anti-money laundering (AML), entity resolution, customer 360, data operations, digital twin, recommendations, knowledge graph, cybersecurity, supply chain, IoT, and network analysis. This channel showcases TigerGraph's technology and how it helps organisations tap into their data to gain key insights into the business decisions and processes driving innovation, growth and cost optimisation. For more information visit