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Self-Supervised AI

The AI Advantage: Mitigating the Security Alert Deluge in a Talent-Scarce Landscape

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The cybersecurity landscape is under siege. Organizations are bombarded by a relentless barrage of security alerts, often exceeding a staggering 22,111 per week on average. While Artificial Intelligence (AI) has emerged as a powerful tool to manage this overwhelming volume, its effectiveness isn’t without limitations, as vendors flood the market with false advertising and promises.

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

AI, artificial intelligence, Blog, Machine Learning, Self-Supervised AI, Third-Wave AI, Uncategorized / By

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?

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Navigating the Uncertain Path: Why AI Adoption in Cybersecurity Remains Hesitant, and How to Move Forward

AI, Blog, generative AI, Ponemon, Predictive AI, Self-Supervised AI, SOC, Third-Wave AI, Unsupervised AI / By

Despite AI’s potential to help defend against cyber attacks, AI adoption in cybersecurity practices remains in its early stages. Why is this the case, and how can organizations overcome these hurdles to pave the way for a secure future?

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Advanced Behavioral Detection Analytics: Enhancing Threat Detection with AI

AI, Blog, Gartner, generative AI, Predictive AI, Ransomware, Real-Time Threat Detection, Self-Supervised AI, Third-Wave AI, Threat Detection / By

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.

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

AI, Blog, Cybersecurity News, Forbes Technology Council, generative AI, Large Language Models (LLM), MixMode News, Self-Supervised AI, Unsupervised AI / By

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.

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Unleashing the Power of Self-Supervised AI: Insights from 451 Research Report on MixMode’s Dynamic Threat Detection and Response

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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.

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

AI, Blog, Federal Cybersecurity, generative AI, Government, Homeland Security, infrastructure, Self-Supervised AI, Third-Wave AI / By

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.

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

Blog, Data Overload, Data Storage Cost, Self-Supervised AI / By

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.

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

Adversarial AI, AI, Blog, Context-Aware AI, Cyber Threats, Cybersecurity News, Data Overload, MixMode News, MixMode Team, Municipalities, Network Security, Ransomware, Self-Supervised AI, SOC, Video / By

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.

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Getting Ahead of the Adversary with Third-Wave AI

Adversarial AI, AI, Blog, Context-Aware AI, Self-Supervised AI, Third-Wave AI, Unsupervised AI / By

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.

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

Blog, Cyber Threats, Cybersecurity News, MixMode News, Network Detection and Response, Network Security, Predictive AI, Rules-Based Cybersecurity, Self-Supervised AI, SIEM, SOC, Third-Wave AI, Threat Detection, Uncategorized, Unsupervised AI, Zero Day Attacks / By

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.

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Case Study: How a Major U.S. City Rapidly Modernized Its Cybersecurity Defenses

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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.

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Can Your Cyber Tools Monitor Any Stream of Data?

AI, Blog, Data Overload, Self-Supervised AI, SIEM, Third-Wave AI / By

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.

Can Your Cyber Tools Monitor Any Stream of Data? Read More →

Understanding the Evolution and Impact of AI on Cybersecurity

AI, artificial intelligence, Blog, Self-Supervised AI, Third-Wave AI, Unsupervised AI / By

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.

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Better Anomaly Detection Is Key to Solving the False Positive Problem Once and for All

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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.

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VIDEO: The Multiplying costs and challenges of Data Storage and Retention in Legacy Cybersecurity Platforms

Blog, Cybersecurity News, Data Storage Cost, MixMode News, Self-Supervised AI, Third-Wave AI, Video / By

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.

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