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Anomoly Detection

MixMode: Revolutionizing Threat Detection and Prioritization in Cybersecurity

Anomoly Detection, Blog, Identity Threat Detection and Response, MixMode Platform, Network Detection and Response, Real-Time Threat Detection, Threat Detection / By

Enter MixMode. The MixMode Platform is a cutting-edge cybersecurity solution that is changing the game in threat detection and prioritization. The MixMode Platform offers a powerful alternative to traditional security tools by leveraging advanced artificial intelligence and a unique data analysis approach.

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Aligning an Organization’s Attack Surface to Detection Surface is Key to Adversary Defense in Today’s Cloud Era

Anomoly Detection, AWS, Azure, Blog, Cloud Security, Cloudtrail, Detection Surface, Flow Logs, Log Data, multi-cloud, Network Detection and Response, Third-Wave AI, Threat Detection, Uncategorized / By

With the Cloud Era, the IT asset estate has expanded, and there is a diverse set of attack surfaces to defend. The term “attack surface” describes the growing IT asset estate, but there is no parallel term to describe where we can detect or respond to attacker activity.

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Better Anomaly Detection Is Key to Solving the False Positive Problem Once and for All

Anomoly Detection, Blog, False Positives, Self-Supervised AI, Third-Wave AI / By

Keeping up with security alerts can be a Herculean task without the right tools on board. Security teams face more than 11,000 alerts per day on average, according to industry analysts — including thousands of false positives triggered by legacy security solutions.

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What is Anomaly Detection in Cybersecurity?

Anomoly Detection, Blog, Cyber Threats, False Positives, Unsupervised AI / By

Anomaly detection, the “identification of rare occurrences, items, or events of concern due to their differing characteristics from the majority of the processed data,” allows organizations to track “security errors, structural defects and even bank fraud,” according to DeepAI and described in three main forms of anomaly detection as: unsupervised, supervised and semi-supervised. Security Operations Center (SOC) analysts use each of these approaches to varying degrees of effectiveness in Cybersecurity applications.

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