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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.
Relying solely on legacy Security Information and Event Management (SIEM) technology is no longer sufficient to protect enterprise organizations from the rising amount of modern, ai-developed, sophisticated cyberattacks. In our newest eBook, we examine the limitations of SIEMs and emphasize the need for an AI-driven dynamic threat detection and response platform.
An enterprise’s inability to detect cyber attacks has tangible effects on its productivity and profitability. Various reports have noted a correlation between the time it takes to spot an intrusion and the cost of recovery.
The world’s reliance on fast, reliable, secure networks has likely never been as apparent as it became in early 2020, when the world responded to the Coronavirus pandemic. Suddenly, vast swaths of the global workforce needed to access and send enormous stores of data from home. In some ways, it couldn’t have happened at a worse time.
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.
The best way to detect threats across an entire network in the quickest manner is to have complete visibility over every part of that network with a multi-stream platform which can incorporate not only network data, but Cloud Data and SIEM logs as well.
Alert fatigue is a top pain point in cybersecurity right now. Security operations center (SOC) analysts are chasing an overwhelming amount of false positive alerts taking time away from analyzing and remediating actual security threats. According to a recent study…
Effective modern network security needs to defend against an unprecedented number of threats. Today’s SecOps teams face both rudimentary hacking attempts and highly sophisticated, targeted attacks that pose serious safety and security risks.
No one really wants to talk about the fact that most security products are rules-based systems that may check a box for compliance but do not deliver intelligent, actionable data.
Security experts and researchers are harnessing AI and ML to create products and solutions for an industry faced with rising and highly intelligent cybercrime and legacy stacks that leave advanced breaches undetected.
In the face of intelligent cyber threats, the best defense is to supplement traditional methods with anomaly detection through unsupervised artificial intelligence (AI).
Whether your network analysts are spending too much time on false positives or neglecting to recognize false negatives, when actual cyber threats go unnoticed, fear not. Here are 4 ways to combat false positives and negatives in your network security program.
How AI is Solving the False Positives Problem in Network Security By Ana Mezic, Marketing Coordinator at MixMode The term “False Positives” is trending in the cybersecurity industry right now. Rightfully so. Managing the impossible amount of alerts IT teams get from their cybersecurity software is an issue that demands a solution as hackers and gatekeepers play tug-of-war …
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5 reasons why Context-Aware Artificial Intelligence (CAAI) is needed in Cybersecurity CAAI delivers understanding of the network baseline and reducing false positives By Dr. Igor Mezic, CTO and Chief Scientist Artificial Intelligence (AI) has surfaced as the technology of the day, in the same way internet, personal computers, airplanes and cars have in earlier eras. And, just like these others …
5 Reasons Why Context-aware Artificial Intelligence (Caai) Is Needed in Cybersecurity Read More →