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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.
Companies are expected to spend up to $55 billion dollars on efforts to comply with the California Consumer Privacy Act (CCPA), which is still working out its final rules after going into effect this month.
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.
Advances in Artificial Intelligence (AI) have led to smarter, more robust network security platforms that are quickly replacing legacy security solutions.
The reality is that most companies and entities are entrusted with sensitive data. As regulations tighten and consumer expectations rise, it is more important than ever to protect data, whenever it is gathered, accessed, shared, or stored. Let’s take a look at a few of the newsworthy data breaches that happened in 2019. Often, studying these cases can inform SecOps teams about what not to do.
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.
The new MixMode & RAVENii whitepaper, “The Evolution of ‘Next-Generation’ Manufacturing and the Need for Network Security,” is a comprehensive look at how third-wave AI is improving modern network security across connected manufacturing networks and beyond.
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.
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.
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.
Recent cybersecurity news, trends, and highlights and how artificial intelligence (AI) is transforming the industry.
Effective modern network security needs to defend against an unprecedented number of threats. Today’s SecOps teams face both rudimentary hacking attempts and highly sophisticated, targeted attacks that pose serious safety and security risks.
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.
Zero-day attacks cost businesses millions of dollars in lost revenue and recovery costs and can cripple a company that is not prepared to respond decisively and effectively.
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.
In the face of intelligent cyber threats, the best defense is to supplement traditional methods with anomaly detection through unsupervised artificial intelligence (AI).
Top cybersecurity best practices for modern organizations’ network security, data protection, and bottom line safeguarding.
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.
Why Managed Service Providers should consider focusing on cybersecurity and how to build the tech stack for your new MSSP practice.