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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.
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.
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.
A new report from Microsoft and Open AI shows how attackers are increasingly using artificial intelligence (AI) to improve their cyberattacks. The report found that nation-backed groups use LLMs for research, scripting, and phishing emails.
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?
Zero-day attacks are considered the number one cybersecurity threat to company networks large and small. Understand what they are, why it’s so hard to detect them, and how artificial intelligence (AI) is helping to solve this modern problem.
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.
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.
Cybersecurity vendors promise the moon when it comes to AI. As the recent TechRepublic article, “Why cybersecurity tools fail when it comes to ambiguity,” makes clear, often, these promises fail short in real world network environments.
Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isn’t possible with supervised-AI.
While many security solution providers promise to protect your network by establishing a baseline of your network behavior, the definition of “baseline” can vary widely.
Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …
Whitepaper: Self-Supervised Learning – AI For Complex Network Security Read More →
We interviewed MixMode CTO Dr. Igor Mezic to better understand how machines should be protecting themselves with AI, what roles humans play, and how the DoD and the private sector both benefit from rapid innovation in cybersecurity.
For the past few years, a major problem has been mounting in the cybersecurity industry: a people shortage. Even before the outbreak of the current global pandemic, enterprises were hurting in the cybersecurity hiring department. Companies are struggling to find employable cybersecurity professionals to handle an ever increasing and evolving number of new threats from …
Many companies are scrambling to find a way to better protect their now-remote team of employees, and as they do so, hackers will take every advantage to find the weaknesses in these spread-out company networks.
MixMode’s leadership team came together to share more on how our context-aware AI builds network baselines for organizations across the globe, it’s predictive capabilities, and how MixMode helps you more intelligently discover anomalies within your network environment.
In today’s ever evolving cybersecurity landscape there are major problems facing professionals that continue to worsen. These problems center around a shortage of tools advanced enough to understand the baseline of a network in order to pinpoint anomalies and a massive information overload problem in the form of security alerts.
While it’s true that having a SIEM is better than forgoing network monitoring all together, a standalone SIEM solution is simply insufficient in today’s cybersecurity landscape. Hackers and other bad actors have become more sophisticated — many of today’s cybercriminals can easily outsmart a standard SIEM setup.
This year on the MixMode blog, we have covered headline stories, analyzed every pain point within network security, and shared what we believe to be some of the most innovative solutions to help you analyze network traffic, surface threats and anomalies, and stop attacks using autonomous AI.
Not all Artificial Intelligence is created equal. As we move towards a future where we lean on cybersecurity much more in our daily lives, it’s important to be aware of the differences in the types of AI being used for network security.