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Gartner just released its Emerging Tech Impact Radar: Security, which looked at technologies that could help organizations effectively detect and respond to attacks and create better efficiencies through AI-based security hyper-automation.
The star of the moment is Large Language Models (aka LLMs), the foundational model that powers ChatGPT. There are plenty of documented examples of truly impressive feats built on this technology: writing reports or outputting code in seconds. At its core, LLMs basically ingest A LOT of text (e.g., think Internet) as a corpus of training data and rely on human feedback in a type of supervised training called reinforcement learning.
In an era where cyber threats are becoming increasingly sophisticated, it is crucial for organizations to stay ahead of attacks. By leveraging the power of self-supervised AI, MixMode offers a game-changing solution that can revolutionize threat detection and response capabilities.
The establishment of the AI Task Force by the DHS demonstrates a commitment to harnessing the potential of AI in addressing emerging threats and safeguarding national security. By leveraging AI technology in various areas, such as supply chain integrity, countering drug trafficking, combating online child exploitation, and securing critical infrastructure, the DHS aims to stay ahead of evolving risks and protect the nation more effectively.
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.
The SANS Institute recently released an analyst First Look Report on MixMode titled, “Self-Supervised Learning Cybersecurity Platform for Threat Detection.” Matt Bromiley, Senior Security Analyst at SANS and author of the report, explores the barriers SOCs face to conquering the vast amounts of data generated by modern enterprises and the solutions that MixMode provides for detecting and investigating threats including our utilization of self-supervised AI for cybersecurity.
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 focused on building sophisticated AI capable of side-stepping traditional Cybersecurity platforms, it has become critically important to onboard tools that work in real-time, are exceedingly precise, and can detect an incident before it happens.
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.
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.
In our newest case study, “How a Major U.S. City Rapidly Modernized Its Cybersecurity Defenses,” we share how the City cut its cyber tool footprint in half, gained visibility into advanced foreign adversary attacks, and greatly improved the productivity of its SOC staff.
It’s the open secret no one’s talking about — too many cybersecurity solutions in the marketplace stand no chance of providing comprehensive coverage because they are incapable of handling data arising from all sources. Many available solutions are effectively legacy platforms hiding within fresh marketing packages.
MixMode’s unsupervised, third-wave AI computes patterns of interaction over many different timescales, contrasting it over the next 5-minute interval with what was seen previously. Should patterns deviate, the platform performs an assessment of the security risk implied in that deviation and presents it to the user.
Keeping up with security alerts can be a Herculean task without the right tools on board. Security teams face more than 11,000 alerts per day on average, according to industry analysts — including thousands of false positives triggered by legacy security solutions.
MixMode’s Head of Sales and Alliances, Geoff Coulehan, discusses the data retention cost and challenges that arise when legacy cybersecurity platforms require historical data to be organized in a proprietary format for after-the-fact investigation processes.
As Forbes reports in the recent article, “The 7 Biggest Artificial (AI) Trends in 2022,” the World Economic Forum identified cybercrime as “potentially posing a more significant risk to society than terrorism” in 2021.
The much-anticipated fifth generation (5G) of broadband cellular technology has arrived, ushering in unprecedented network speed and connectivity. The tech is also spurring innovation into new tech solutions to meet an ever-growing appetite for instant, reliable connectivity, often, faster than most enterprise Cybersecurity teams can handle. If there was ever a time for AI to deliver on the promises made by Cybersecurity platform vendors, it’s now.
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.