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Zero-day attacks are considered the number one cybersecurity threat to company networks large and small. Understand what they are, why it’s so hard to detect them, and how artificial intelligence (AI) is helping to solve this modern problem.
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.
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.
The cybersecurity provider landscape is cluttered with impossible claims, misrepresentations, and a confusing mix of inconsistent terminology. Worse, every minute you delay making a decision is another minute hackers have to gain access and knowledge about your network.
This is the final article in a three-part series on Network Baselining. Read the first two articles in the series here:
1) An Introduction to Baselining Technology
2) How to Create a Baseline for Your Network
We separate the parts that are wavelike and we separate the parts that are stochastic. Then, five minutes later we look again and compare what we’ve seen historically and what the AI has predicted to happen to the current state of the network. If they are different, that’s an anomaly detected.
In October, 2019 a MixMode customer experienced an incident where an external entity attacked a web server located in their DMZ, compromised it, and then pivoted internally through the DMZ to attempt access of a customer database. While the attacker was successful in penetrating the customer’s network, MixMode was able to detect the event before they were successful in penetrating the customer database.
Not all Artificial Intelligence is created equal. As we move towards a future where we lean on cybersecurity much more in our daily lives, it’s important to be aware of the differences in the types of AI being used for network security.
Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably when discussing developments in deep learning. However, there is an important difference between the two that network security professionals will need to understand in order to serve their clientele effectively.
The cybersecurity industry is in need of a massive shakeup. Rules-based software has created an exponential increase in false positive security alerts and zero-day threats are on the rise.
In the face of intelligent cyber threats, the best defense is to supplement traditional methods with anomaly detection through unsupervised artificial intelligence (AI).
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.
Unsupervised artificial intelligence, also known as context-aware or third-wave AI, is notoriously difficult to explain because there lacks an appropriate test to understand just how powerful the intelligence is. The widely known Turing Test for AI testing is no longer the right framework for modern-day AI testing.
From Network Detection and Response (NDR) to potentially helping to displace passwords as the user authentication standard, there are many reasons why AI is helping advance modern cybersecurity.
Recently, network detection and response, or NDR, has been established as a key tool for companies seeking to improve their threat response. It’s a relatively new network security strategy which developed in response to perceived shortcomings in existing network security systems. We wanted to help explain what modern network detection and response is, how it …
What is Network Detection and Response (NDR)? A beginner’s Guide Read More →
Attacking and Defending with Artificial Intelligence Often when discussing Machine Learning and AI in the cybersecurity space, we focus on the potential defense capabilities of AI systems. However, it’s time we start paying attention to the other side as well, as hackers begin to arm themselves with similar technologies. Industry practitioners believe that we will …
Silicon Valley Has Let Its Cybersecurity Guard Down According to an article by Brian O’Keefe for Fortune Magazine, Silicon Valley isn’t paying enough attention when it comes to threats posed by state-sponsored hackers. At least, that was the conclusion a group of cybersecurity experts came to in a discussion at the Fortune Brainstorm Tech conference in Aspen, Colo., on …
Silicon Valley Needs to Step up Their Security: This Week in AI Read More →
850 senior executives from Information Security, Cybersecurity, and IT Operations in seven industries across ten countries were recently surveyed by consulting and technology services firm, Capgemini, in their “Reinventing Cybersecurity with Artificial Intelligence” report. The goal being to understand today’s benefits, complexities, and levels of implementation of AI in cybersecurity across IT (information technology), OT (operation …
AI-Enabled Cybersecurity Is Necessary for Defense: Capgemini Report Read More →
Originally published on 5/20/19 on scmagazine.com, this article by Dr. Peter Stephenson is the first in a four-part series to help enterprise and security professionals discover the tools needed to deploy a next-generation enterprise security stack. As it features MixMode as the true AI system needed for your deception network, we wanted to share the article here on our …
SC Magazine: Beefing Up Your Next Generation Security Tool Set Read More →
Most security professionals are well-versed in understanding log files and their importance. Most organizations with a security program operate a SIEM (Security Incident and Event Management) so they can track events around their network. They may use Splunk, FireEye, SolarWinds, QRadar, LogRhythm or many others for a SIEM solution. A SIEM is a very important way to keep track of log …
Intro to Wire Data: Why Should I Care When I Already Have Log Files? Read More →