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A new report from Microsoft and Open AI shows how attackers are increasingly using artificial intelligence (AI) to improve their cyberattacks. The report found that nation-backed groups use LLMs for research, scripting, and phishing emails.
When it comes to Cybersecurity and Generative AI, you have to look at what traditionally takes time and investment by expert practitioners. One of the major things SOC analysts have to build and maintain is an alerting framework for their environment.
At MixMode, we will look you in the eye when we explain that we know the marketplace is crowded with failed promises of cyber platforms that claim to be “AI-based, self-learning, offering real-time detection and prevention at scale.
MixMode’s Chief Strategy Officer, Matt Shea was invited to provide the opening keynote address, setting the stage for discussions on how businesses and municipalities can better protect their networks and environments from cyber attacks.
In a world where bad actors are capable of building sophisticated AI capable of sidestepping traditional cybersecurity platforms, it has become critically important to onboard tools that work in real-time, are deadly accurate, and can predict an incident before it happens.
Monthly reports that lack relevant details about an organization’s true risk level are insufficient and not representative of the further steps an organization should take to protect itself. This approach leaves organizations feeling secure against the threat of ransomware while they are actually left exposed to potentially expensive, wide-scale damage.
MixMode Inc., a leading global provider of Artificial Intelligence-powered Cybersecurity, announced today that the company was recognized as a supply side innovator in the November 2021 Gartner report: Emerging Technologies: Tech Innovators in AI in Attack Detection — Supply Side.
We recently released a new video to better explain how MixMode’s next-generation cybersecurity anomaly detection platform combines the functionality of SIEM, NDR, NTA and UEBA for advanced threat detection, zero day attack identification, false positive alert reduction, forensic investigation and more.
It’s evident that while organizations are spending more and more on legacy cybersecurity solutions, these platforms are not holding up their end of the deal and are not able to proactively defend in a modern, non-signature attack threatscape.
Cybersecurity vendors promise the moon when it comes to AI. As the recent TechRepublic article, “Why cybersecurity tools fail when it comes to ambiguity,” makes clear, often, these promises fail short in real world network environments.
Most cybersecurity vendors today tout some form of “Artificial Intelligence” as an underlying mechanism for the differentiation of their product among the market. But if everyone is saying they have AI, and everyone is also claiming theirs is the “best,” how can they all be telling the truth?
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.
Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isn’t possible with supervised-AI.
While many security solution providers promise to protect your network by establishing a baseline of your network behavior, the definition of “baseline” can vary widely.
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 →
COVID-19 has caused most corporate businesses that remain open to shift to a work from home, remote workplace. Because of this, the cybersecurity industry has been turned on its head. Security teams went from monitoring and protecting established network environments to quickly pivoting their tools, resources, and oversight to manage a distributed workforce. This has …
One Thing All Cybersecurity teams Should Have During COVID-19 Read More →
MixMode leaders John Keister, Dr. Igor Mezic, Bryan Elliot, and Russell Gray share how the single algorithm that is the foundation of MixMode’s self-learning AI can understand and continually build a generative baseline of your network without human training.
We interviewed MixMode CTO Dr. Igor Mezic to better understand how machines should be protecting themselves with AI, what roles humans play, and how the DoD and the private sector both benefit from rapid innovation in cybersecurity.
Throughout the tech community, “artificial intelligence” has become a blanket term often used to describe any computing process that requires little human input. Tasks like routine database functions, scheduled system scans, and software that adds automation to repetitive actions are regularly referred to as AI. In truth, AI can play a part in these processes, …
Not All Artificial intelligence is created Equal Read More →
For the past few years, a major problem has been mounting in the cybersecurity industry: a people shortage. Even before the outbreak of the current global pandemic, enterprises were hurting in the cybersecurity hiring department. Companies are struggling to find employable cybersecurity professionals to handle an ever increasing and evolving number of new threats from …