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We have now reached the Third wave of AI: Contextual Reasoning. This revolutionary approach, pioneered by MixMode in cybersecurity, utilizes self-supervised and explainable AI to independently learn and adapt without reliance on rules or training data.
While a significant portion (53%) of respondents acknowledge their organization’s early-stage adoption of AI, only 18% report full deployment into integrated security programs. This cautious approach reflects the need for careful planning and implementation to ensure AI enhances, rather than hinders, security posture.
This week, we’re looking into how the rise of artificial intelligence (AI) has significantly altered the threat landscape, introducing challenges and opportunities for organizations seeking to safeguard their data and infrastructure.
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?
Organizations continue to face increasingly sophisticated cyber threats that require a proactive and adaptive approach to cybersecurity. As the volume and complexity of security data grows, security professionals are turning to advanced AI technologies to enhance their capabilities and stay ahead of emerging threats.
One of the key areas that the Gartner report highlights is AI Enhanced Security Operations. The research observes that most AI development in security products so far has focused on detecting threats, but there is increasing potential to use AI to improve security operations and incident response.
On October 30, 2023, the White House issued an Executive Order promoting safe, secure, and trustworthy artificial intelligence (AI) deployment. This Executive Order recognizes the global challenges and opportunities presented by AI and emphasizes the need for collaboration, standards development, and responsible government use for national security.
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.
Gartner recently released their annual Emerging Tech Impact Radar: Security. This report analyzes 23 emerging technologies and trends that will shape the future of cybersecurity products and services. It provides insights to help security leaders develop strategies and roadmaps to capitalize on these innovations.
In this buyers guide you’ll learn about the many approaches to AI Threat detection and Response, what to look for, benefits, drawbacks, and everything you need to know when choosing an AI platform for your security operations center.
Several AI approaches are used in cybersecurity, but it’s hard to make sense of the noise, especially when vendors say the same thing. In our newest eBook, we’ll explore what they are and the pros and cons of each one.
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.
Cybersecurity veterans discuss the challenges large enterprises in the Fortune 1000 face in scaling security monitoring and threat detection across big data environments.
“Zero-day Summer” refers to the period during summertime when cybercriminals take advantage of the vulnerabilities in software, applications, and computer systems that remain undetected and unpatched by security teams.
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.
The financial services industry handles vast amounts of highly sensitive financial information, including customer data, transaction records, and intellectual property, making them a prime target for cyber attacks.
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.