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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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Dependence on Log Data | Twenty-first Century Networks Require Twenty-first Century Security Solutions

Blog, Cyber Threats, Log Data, Network Security, SIEM / By

When Cybersecurity teams assume that log-based platforms are the best available network security solutions, they have made a decision to invest in “bad gas.” These products will cost more and deliver less than systems enhanced by context-aware AI.

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Dependence on Log Data | An Increasing Vulnerability to Threat Actors

Blog, Log Data, Whitepaper, Zero Day Attacks / By

How sure are you that log files represent the best source of information to base your entire Cybersecurity program upon? Log data is the cornerstone of every traditional cybersecurity platform including SIEM (Security Information and Event Management), UEBA (User and Entity Behavior Analytics), and xDR (Detection and Response).

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Cybersecurity Spend for Data Retention and Analysis is Out of Control and Largely Unnecessary

Blog, Data Storage Cost, Log Data, SIEM / By

Managing a relatively small, stable data store is one thing, but dynamic companies face immense challenges when those circumstances change. Data growth can become unwieldy to safeguard when it has to be carefully prepared through a series of time-consuming, manual processes before the security software can evaluate it.

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The Aggregation Model is Falling Short

Blog, Log Data, SIEM, SOC, Third-Wave AI, Whitepaper / By

The following is an excerpt from our recent whitepaper, “Why Traditional Cybersecurity Tools Cannot Defend Against Zero-Day and No Signature Attacks,” in which we dive into how traditional cybersecurity tools work, why this fundamentally limits them from being able to detect zero-day or previously unknown attacks, why the industry standard for breach detection is around

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Maximize ROI with Greater Efficacy Using Unsupervised AI

Blog, Case Study, Data Storage Cost, Log Data, Network Security, SIEM, SOC, Unsupervised AI / By

Within the first 24 hours after deployment, MixMode had enabled the government entity to regain control over the security environment and network data infrastructure. No longer limited to log data analysis, they were able to identify and address real-time threats as well as network and operational configuration challenges.

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How Self-Supervised AI Tackles Ambiguity in Network Security

Blog, Context-Aware AI, Log Data, Network Baselines, Network Detection and Response, Network Security, Predictive AI, Self-Supervised AI, SIEM, Third-Wave AI / By

Cybersecurity vendors promise the moon when it comes to AI. As the recent TechRepublic article, “Why cybersecurity tools fail when it comes to ambiguity,” makes clear, often, these promises fail short in real world network environments.

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