False Positive Alerts

eBook: The Inefficiencies of Legacy Tools – Why SIEMs Alone Are Ineffective At Detecting Advanced Attacks

Relying solely on legacy Security Information and Event Management (SIEM) technology is no longer sufficient to protect enterprise organizations from the rising amount of modern, ai-developed, sophisticated cyberattacks. In our newest eBook, we examine the limitations of SIEMs and emphasize the need for an AI-driven dynamic threat detection and response platform.

U.S. Cities Relying on Legacy Cybersecurity Plagued By False Positives and Negatives

Cybersecurity teams working in municipal settings face a constant struggle — protecting vital public network infrastructure with limited resources. The situation can reach a breaking point when these teams become overwhelmed managing false positive and negative flags triggered by legacy cybersecurity solutions.

What is Anomaly Detection in Cybersecurity?

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.

Whitepaper: The Failed Promises of SIEM

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.

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

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.

Why The Future of Cybersecurity Needs Both Humans and AI Working Together

A recent WhiteHat Security survey revealed that more than 70 percent of respondents cited AI-based tools as contributing to more efficiency. More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.

Our Top 5 Cybersecurity Insights from 2019

This year on the MixMode blog, we have covered headline stories, analyzed every pain point within network security, and shared what we believe to be some of the most innovative solutions to help you analyze network traffic, surface threats and anomalies, and stop attacks using autonomous AI.

5 Ways to Modernize Your MSSP Security Monitoring Program

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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