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The State of Cloud Security: New MixMode Report Finds Enterprises Are Struggling to Keep Pace with Security As Cloud Adoption Accelerates

Blog, Cloud Security, Cloudtrail, eBook, multi-cloud, Whitepaper / By

he new State of Cloud Security Report from MixMode summarizes input from 588 security professionals and reveals significant gaps in organizations’ abilities to secure cloud platforms and workloads. Despite multi-cloud adoption reaching mainstream levels, critical capabilities for cloud security, such as real-time threat detection and response, comprehensive visibility, workload protection, and data security, still need to be improved.

The Fallacy of “One-Click Remediation”

Blog, One-Click Remediation, Whitepaper / By

Let’s take a closer look at the false narrative being propagated in the cybersecurity market and explore some more appropriate alternatives. An example of a legacy cyber tool vendor “feature” being touted as an ideal solution to the problem of automatic remediation is reliant on a standard (though rarely used for reasons we will explore in this article) function known as TCP Reset.

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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Our Top 2020 Cybersecurity Insights

Blog, MixMode News, Network Baselines, Network Detection and Response, Network Security, Predictive AI, Self-Supervised AI, SIEM, SOC, Third-Wave AI, Webinar, Whitepaper / By

The transition from office to remote environments was abrupt and one of the most defining moments that the cybersecurity industry and professionals faced in 2020. We wrote about the top issues CISOs were facing throughout the year but also doubled down on sharing insights about the evolution of next-generation SOCs, the failure of SIEM platforms as organizations are experiencing them today, and how self-supervised AI fits into the equation.

How Vendors Capitalize on SIEM’s Fundamental Flaws

Blog, SIEM, SOC, Whitepaper / By

Because the fundamental nature of SIEM requires infinite amounts of data, security teams are forced to constantly wrangle their network data and faced with an unmanageable number of false positive alerts. This means they have to devise efficient ways to collect, organize and store data, resulting in an incredible investment in human and financial resources.

The Evolution of SIEM

Blog, Network Detection and Response, Network Security, Self-Supervised AI, SIEM, Whitepaper / By

It should be noted that SIEM platforms are exceptionally effective at what they initially were intended for: providing enterprise teams with a central repository of log information that would allow them to conduct search and investigation activities against machine-generated data. If this was all an enterprise cybersecurity team needed in 2020 to thwart attacks and stop bad actors from infiltrating their systems, SIEM would truly be the cybersecurity silver bullet that it claims to be.

Whitepaper: The Failed Promises of SIEM

Blog, Data Overload, False Positives, Regulations & Compliance, Self-Supervised AI, SIEM, SOC, Whitepaper / By

The fundamental SIEM flaws lie in the platform’s need for continual adjustment, endless data stores, and a tendency to create an overwhelming number of false positives. When organizations instead turn to a next-generation cybersecurity solution, which predicts behavior with an unsupervised (zero tuning) system, they are poised to save on both financial and human resources.

How Data Normalization in Cybersecurity Impacts Regulatory Compliance

Blog, Data Overload, Regulations & Compliance, SIEM, SOC, Whitepaper / By

Complying with privacy regulations requires all organizations to have access to data on demand, wherever it lives on a network. With the unfathomable amount of data managed by most organizations operating in the finance space today, it can become a significant challenge to locate specific data across legacy systems and networks with countless connections online and off.

Data Overload Problem: Data Normalization Strategies Are Expensive

Blog, Data Overload, Network Detection and Response, Network Security, Network Traffic Analysis, Self-Supervised AI, Whitepaper / By

Financial institutions spend five to ten million dollars each year managing data. A recent Computer Services Inc (CSI) study reveals that most banks expect to spend up to 40 percent of their budgets on regulatory compliance cybersecurity, often adopting expensive data normalization strategies.