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Security leaders want to solve problems identifying and understanding anomalies or account access behaviors by correlating anomalous behaviors of specific accounts with other parameters like geography or ingress and egress points, but few rules-based Cybersecurity tools have the ability to do that without a great deal of manual data massaging and manipulating.
Overall, Black Hat 2023 demonstrated that the cybersecurity landscape continues to evolve rapidly. The event highlighted both the threats posed by emerging technologies like AI, as well as the potential for new defensive techniques.
A briefing by the FBI’s Counterintelligence Division highlights the massive potential of artificial intelligence (AI) for advancing cybersecurity and the looming risks of adversaries weaponizing AI for attacks.
Combating alert fatigue is a huge challenge faced by SOC analysts and security companies alike. The overwhelming flood of rules-based alerts and notifications can often lead to missed critical incidents and increased false positives.
One of the key predictions is that by 2027, 50% of chief information security officers (CISOs) will adopt human-centric design practices in their cybersecurity programs to minimize operational friction and maximize control adoption. This approach focuses on designing security controls around individuals rather than technology or threats. It recognizes that employees play a crucial role in cybersecurity and aims to reduce the likelihood of risky behavior.
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.
CISA identifies goals for the strategy as spearheading “the national effort to ensure the defense and resilience of cyberspace,” helping partners protect critical infrastructure, and supporting stronger “whole-of-nation operational collaboration and information sharing.” The strategy also outlines internal CISA goals focused on unification within the agency.
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.
Gartner recently released their newest report titled “Emerging Tech: Security – Improve Threat Detection and Response With AI-Based Behavioral Indications” which covers the emergent need for new AI-Based methods of using behavioral patterns to identify threats.
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 this on-demand audio webinar, featuring IDC Group Vice President Ritu Jyoti and MixMode Chief Scientist and CTO Dr. Igor Mezic, you’ll learn about cyber AI that’s built for the speed and scale of the modern enterprise.
Discover the role of Artificial Intelligence and Machine Learning in protecting against ransomware attacks. Learn how these technologies can be used to detect and prevent attacks, and how they can be leveraged by enterprises to improve their cybersecurity posture. Stay up-to-date on the latest developments in AI and ML to protect your organization from malware and ransomware.
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.
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.