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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.
Knowing the difference between Discriminative and Generative Unsupervised Learning can tell you a lot about the effectiveness of a cybersecurity solution’s artificial intelligence, for example, whether or not that security solution can perform actions like identifying and stopping a zero-day attack.
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.
This is the first installment of the three part blog series on creating baselines of network behavior to improve your security stance. Here we will walk you through the basics of baselining technology.
The best way to detect threats across an entire network in the quickest manner is to have complete visibility over every part of that network with a multi-stream platform which can incorporate not only network data, but Cloud Data and SIEM logs as well.
Third-Wave artificial intelligence (also known as Wave 3 AI), is making life a whole lot easier for security systems administrators. It seems like we just passed the milestones of incorporating AI into network security. But Dr. Igor Mezic and others in this field have made huge strides in the last year. In his new whitepaper on AI for Network Security, Dr. Mezic describes how Third-Wave AI brings flexibility and intuition into the world of machine learning.
We ingest your CloudTrail logs into our platform, apply a layer of our Proprietary Artificial Intelligence to give you advanced anomaly detection and alerting, correlate those anomalies with your underlying network data and give you access to forensic search and investigation of these logs.
Recent cybersecurity news, trends, and highlights and how artificial intelligence (AI) is transforming the industry.
No one really wants to talk about the fact that most security products are rules-based systems that may check a box for compliance but do not deliver intelligent, actionable data.
Security experts and researchers are harnessing AI and ML to create products and solutions for an industry faced with rising and highly intelligent cybercrime and legacy stacks that leave advanced breaches undetected.
Not only is artificial intelligence necessary for keeping up with the onslaught of lower-tech attacks, but also, AI is becoming crucial for defending against AI-powered cyberattacks, which are just on the horizon.
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).
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.