MixMode Inc., the leader in AI-driven network security, today announced the addition of Todd DeBell as VP, Channel Sales. DeBell will be responsible for continuing to expand MixMode’s channel sales strategy to accelerate MixMode’s triple-digit revenue growth of 2021 and 2022.
In the report, 451 Research explains why security analytics needs to include advanced Third-Wave AI, which autonomously learns normal behavior and adapts to constantly changing network environments, to address the next generation of cyberthreats and increase SOC productivity.
As SIEM evolved, vendors began bolting on NDR (network detection and response) and NTA (network traffic analysis) to their base SIEM offerings. The hope (and promise) was that these tools would add the real-time security solution that was lacking with SIEM technology.
Ultimately, MixMode found, the log-based SIEM approach resulted in five times the amount of data that needed to be stored, a cost that was passed along to the government entity.
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.
One thing is clear: more spend does not equal more security and the next generation of cybersecurity tools will route out these inefficiencies.
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 …
Whitepaper: Self-Supervised Learning – AI For Complex Network Security Read More →
Our newest whitepaper, “How Predictive AI is Disrupting the Cybersecurity Industry,” evaluates several common SecOps issues around Network Traffic Analysis, explaining why typical solutions are wholly ineffective and represent sunk costs versus added value. We examine how self-supervised learning AI is poised to overcome the SecOps challenges of protecting today’s distributed networks.
Imagine serving as a CISO for a prominent enterprise and being thrown into a situation where overnight 80 to 100 percent of your workforce moves inside and begins working remotely. The logistics challenges alone are almost too daunting to imagine.
Although it is not surprising at all that hackers are taking advantage of the global pandemic —phishing threat reports are always highest when there is some natural disaster happening— we have never before had such an unsafe environment to protect. Here are a few of the most popular malicious acts:
As enterprises establish their custom hybrid environments, many security departments are realizing that while they have many sophisticated tools on the on-premise corporate traffic side, they don’t have the same level of visibility of their cloud traffic.
Many companies are scrambling to find a way to better protect their now-remote team of employees, and as they do so, hackers will take every advantage to find the weaknesses in these spread-out company networks.
Consider investing in emerging tech tools that can add a deeper layer of data privacy protection. Artificial intelligence technology, in particular, holds a great deal of promise when it comes to complying with the CCPA and improving the way companies handle data in general.
Recent cybersecurity news, trends, and highlights and how artificial intelligence (AI) is transforming the industry.
In the face of intelligent cyber threats, the best defense is to supplement traditional methods with anomaly detection through unsupervised artificial intelligence (AI).
Packetsled Query Language (PQL) addresses the need of analysts to ask questions from large amounts of data without being forced to learn a complicated query language. It is a simple, intuitive expression language that lets users focus on finding what they need quickly.
Whether your network analysts are spending too much time on false positives or neglecting to recognize false negatives, when actual cyber threats go unnoticed, fear not. Here are 4 ways to combat false positives and negatives in your network security program.
Recent cybersecurity news, trends, and highlights and how artificial intelligence (AI) is transforming the industry.
Mixmode’s platform utilizes third-wave, context-aware AI to inform the user of threats (including Zero-Day, and encrypted traffic) on their network, and reduces the rate of false positives in intel and alerts consistently by 90% or more.
HighCastle used MixMode to provide insights to its Security Operations Center (SOC) analysts so they could better understand clients’ cyber risks, minimize time spent chasing false positives and spend the majority of their time responding to actionable information.
