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Network Detection and Response

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.

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.

What is Predictive AI and How is it Being Used in Cybersecurity?

Blog, Network Detection and Response, Predictive AI, Self-Supervised AI, Threat Detection / By

The predictive AI field of machine learning collects, analyzes, and tests data to predict future possibilities. AI’s neurological network is patterned on the human brain. But AI works on a scale that goes far beyond what is humanly possible. The top uses for predictive AI technologies to protect sensitive data and systems are in network detection and response (NDR), threat detection, and cybercrime prevention.

Why a Platform With a Generative Baseline Matters

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

MixMode creates a generative baseline. Unlike the historically-based baselines provided by add-on NTA solutions, a generative baseline is predictive, real-time, and accurate. MixMode provides anomaly detection and behavioral analytics and the ability to suppress false positives and surface true positives.

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

Blog, Context-Aware AI, Network Detection and Response, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, SOC, Threat Detection, Unsupervised AI, Zero Day Attacks / By

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.

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.

Whitepaper: Self-Supervised Learning – AI For Complex Network Security

Blog, Context-Aware AI, Network Detection and Response, Network Security, Network Traffic Analysis, Self-Supervised AI, Uncategorized, Unsupervised AI, Whitepaper / By

Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …

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Encryption = Privacy ≠ Security

Adversarial AI, Blog, Cloud Security, Data Breach, Data Privacy, Network Detection and Response, Network Security, Network Traffic Analysis, Self-Supervised AI, Threat Detection, Zero Day Attacks / By

For the past few years, many have been talking about the changing “threat landscape” as it pertains to the increase in zero day, insider and phishing threats. While all of these threats are on the rise, and constitute a concern, there is, perhaps, an even larger shift presenting a threat to enterprises – the shift …

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New Video: Why is network data the best source for actionable data in cybersecurity?

Blog, Network Baselines, Network Detection and Response, Network Security, Network Traffic Analysis, Threat Detection / By

In a recent blog post, our Head of Customer Success, Russell Gray, outlined the reasons why network data is the best source for actionable data in cybersecurity. He covered the limitations of each of the elements of a typical security stack (SIEM, Endpoint, and Firewall) and the importance of network traffic analysis (NTA) in the …

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3 Cyberthreats Facing Federal and State Governments in 2020

Blog, Network Detection and Response, Network Security, Regulations & Compliance, Threat Detection, Zero Day Attacks / By

Bad actors do not discriminate. Organizations across all sectors are at risk — corporations, non-profits, and increasingly, federal and state government entities. The U.S. Government Accountability Office (GAO) reported that security incidents increased by 1,300 percent from 2006 to 2015. This number is growing.

Our Top 5 Cybersecurity Insights from 2019

Alert Reduction, Blog, Case Study, Network Baselines, Network Detection and Response, Threat Detection, Unsupervised AI, Whitepaper, Zero Day Attacks / By

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.