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Forums it-sa Expo Knowledge Forum C

Clear Focus in the SOC – Using AI to Distinguish Anomalies from Real Attacks!

Learn how Vectra AI uses AI to assess anomalies, accurately detect real threats, and minimize false positives.

calendar_today Thu, 24.10.2024, 10:45 - 11:00

event_available On site

place Forum, Booth 7-641

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south_east

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south_east

Themes

Cloud Security Endpoint Protection Identity and access management Network Security / Patch Management Data Center SIEM / Threat Analytics / SOC

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This action is part of the event Forums it-sa Expo

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Action description

In an increasingly complex IT landscape, traditional SIEM systems are reaching their limits. These systems collect massive amounts of data and analyze them retrospectively to identify potential threats. However, this approach often leads to a flood of alerts that are difficult to prioritize. As a result, IT teams are overwhelmed by false positives, and real threats can be overlooked.

In this presentation, we introduce an alternative approach that focuses on precise and contextual analysis of data using AI. Vectra AI takes the opposite route, concentrating on identifying only relevant threats and assessing them within the context of overall network behavior. Instead of treating every anomaly as a potential attack, our AI solution discerns which deviations are truly dangerous and which are merely harmless outliers.

We discuss how the Vectra AI approach optimizes data flow by filtering out only the most critical information, significantly enhancing the efficiency of your security strategy. By applying AI to analyze behavioral patterns, threats are detected and assessed in real-time, allowing for immediate and targeted security measures.

The presentation also explores how Vectra AI can be integrated into existing security architectures to ensure comprehensive coverage and protection against modern threats. Our goal is to demonstrate that not every anomaly is an attack—and how the right tools can elevate your cybersecurity to a new level.
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Language: German

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