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SIEM

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.

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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.

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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.

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Webinar: The Failed Promises of SIEM – What’s Next For Cybersecurity

Blog, Self-Supervised AI, SIEM, SOC, Uncategorized, Webinar / By

The Security Operations Center (SOC) of today is fundamentally flawed. Currently enterprise cybersecurity spend is higher than ever, but despite multi-million dollar cybersecurity investments, organizations remain vulnerable to attacks. One of the major reasons for this is legacy SIEM deployments. More spend does not equal more security.

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3 Reasons Why a Rule-Based Cybersecurity Platform Will Always Fail

Blog, False Positives, Network Baselines, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, SIEM, Zero Day Attacks / By

When it comes to advancements in cybersecurity, rule-based systems are holding the industry back. Relying on humans to constantly input and label rules in order to detect and stay ahead of threats is a bottleneck process that is setting security teams up for failure, especially with tools like SIEM, NDR, and NTA.

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NTA and NDR: The Missing Piece

Blog, Network Detection and Response, Network Traffic Analysis, SIEM, Whitepaper / By

Most SIEM vendors acknowledge the value of network traffic data for leading indicators of attacks, anomaly detection, and user behavior analysis as being far more useful than log data. Ironically, network traffic data is often expressly excluded from SIEM deployments, because the data ingest significantly increases the required data aggregation and storage costs typically 3-5x.

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Guide: The Next Generation SOC Tool Stack – The Convergence of SIEM, NDR and NTA

Blog, Network Detection and Response, Network Traffic Analysis, SIEM, SOC / By

Traditional security vendors offering solutions like SIEM (Security Information and Event Management) are overpromising on analytics while also requiring massive spend on basic log storage, incremental analytics, maintenance costs, and supporting resources.

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New Video: How is MixMode Different From Today’s Network Security Tools?

Blog, Self-Supervised AI, SIEM, Video / By

With MixMode in the center of a program, we will make all the other security investments that you’ve made, better. So when you send data to your SIEM, when you send data to your SOAR, you don’t want those products to be overwhelmed with false positive alerts, with data you don’t need.

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How the Role of the Modern Security Analyst is Changing

Blog, False Positives, Threat Detection, Whitepaper / By

As organizations began to rely more heavily on networking to carry out their operations over the past decade, IT teams added security analyst positions. These professionals focused on network security and providing regulatory compliance oversight.  Over time, the role of the security analyst has expanded to include threat hunting tasks. That is, evaluating security platform

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The Cybersecurity Processes Most Vulnerable to Human Error

Alert Reduction, Blog, False Positives, Predictive AI, SIEM, Whitepaper / By

The world’s reliance on fast, reliable, secure networks has likely never been as apparent as it became in early 2020, when the world responded to the Coronavirus pandemic. Suddenly, vast swaths of the global workforce needed to access and send enormous stores of data from home. In some ways, it couldn’t have happened at a worse time.

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Yesterday’s SIEM Solutions Can’t Combat Today’s Cyberthreats

Blog, Network Detection and Response, Threat Detection, Unsupervised AI, Zero Day Attacks / By

While it’s true that having a SIEM is better than forgoing network monitoring all together, a standalone SIEM solution is simply insufficient in today’s cybersecurity landscape. Hackers and other bad actors have become more sophisticated — many of today’s cybercriminals can easily outsmart a standard SIEM setup.

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5 Ways to Modernize Your MSSP Security Monitoring Program

Blog / By

MSSPs are helping their customers deal with a fast-paced and ever-evolving threat landscape. It is critical, now more than ever, to evaluate new monitoring tools that produce more actionable data and alerts to help unearth and combat these modern threats more efficiently.    I recently read an article titled  “4 Technologies SMBs Can Use to Modernize

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