Self-Supervised AI

Unveiling The Applications and Distinctions of Machine Learning and Artificial Intelligence in Cybersecurity

The terms “machine learning” and “artificial intelligence” are frequently used in cybersecurity, often interchangeably, leading to confusion about their precise meanings and applications. Both machine learning and artificial intelligence play pivotal roles in fortifying cybersecurity defenses, yet they encompass distinct methodologies and applications. What are the disparities between them? And how do these technologies converge to bolster cyber resilience?

Forbes Technology Council: Why Large Language Models (LLMs) Alone Won’t Save Cybersecurity

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.

AI Offers Potential to Enhance The U.S. Department of Homeland Security

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.

SANS First Look Report: Self-Supervised Learning Cybersecurity Platform for Threat Detection

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.

New Video: Broken Promises and Bright Future – Preparing for the Next Wave of AI in Cybersecurity

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.

451 Research Finds Self-Learning Technology to Address Cybersecurity Blind Spots and Reduce Analyst Burnout

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.

As Enterprises Embrace 5G, AI-Enhanced Cybersecurity Emerges as Top Security Priority

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.