Threat Detection

What is Predictive AI and How is it Being Used in Cybersecurity?

The predictive AI field of machine learning collects, analyzes, and tests data to predict future possibilities. AI’s neurological network is patterned on the human brain. But AI works on a scale that goes far beyond what is humanly possible. The top uses for predictive AI technologies to protect sensitive data and systems are in network detection and response (NDR), threat detection, and cybercrime prevention.

Encryption = Privacy ≠ Security

For the past few years, many have been talking about the changing “threat landscape” as it pertains to the increase in zero day, insider and phishing threats. While all of these threats are on the rise, and constitute a concern, there is, perhaps, an even larger shift presenting a threat to enterprises – the shift …

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How the Role of the Modern Security Analyst is Changing

As organizations began to rely more heavily on networking to carry out their operations over the past decade, IT teams added security analyst positions. These professionals focused on network security and providing regulatory compliance oversight.  Over time, the role of the security analyst has expanded to include threat hunting tasks. That is, evaluating security platform …

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New Whitepaper: How Predictive AI is Disrupting the Cybersecurity Industry

Our newest whitepaper, “How Predictive AI is Disrupting the Cybersecurity Industry,” evaluates several common SecOps issues around Network Traffic Analysis, explaining why typical solutions are wholly ineffective and represent sunk costs versus added value. We examine how self-supervised learning AI is poised to overcome the SecOps challenges of protecting today’s distributed networks.

The Many Ways Your Employees Can Get Hacked While Working From Home and How to Respond

Although it is not surprising at all that hackers are taking advantage of the global pandemic —phishing threat reports are always highest when there is some natural disaster happening— we have never before had such an unsafe environment to protect. Here are a few of the most popular malicious acts:

What the Clearview AI Breach Tells Us About Cybersecurity Today

The 2020 Clearview AI data breach spawned hundreds of attention-grabbing headlines, and for good reason. The company works closely with law enforcement agencies and other entities by sharing personal information about millions of people, for a variety of purposes. The breach raised many questions about the vulnerability of personal data in general.

Hacks and Breaches of 2019: A Year in Review

The reality is that most companies and entities are entrusted with sensitive data. As regulations tighten and consumer expectations rise, it is more important than ever to protect data, whenever it is gathered, accessed, shared, or stored. Let’s take a look at a few of the newsworthy data breaches that happened in 2019. Often, studying these cases can inform SecOps teams about what not to do.

The Evolution of “Next-Generation” Manufacturing and the Need for Network Security

The new MixMode & RAVENii whitepaper, “The Evolution of ‘Next-Generation’ Manufacturing and the Need for Network Security,” is a comprehensive look at how third-wave AI is improving modern network security across connected manufacturing networks and beyond.

Generative Unsupervised Learning vs. Discriminative Clustering Technology: Which Prevents Zero-Day Attacks?

Knowing the difference between Discriminative and Generative Unsupervised Learning can tell you a lot about the effectiveness of a cybersecurity solution’s artificial intelligence, for example, whether or not that security solution can perform actions like identifying and stopping a zero-day attack.

Case Study: MixMode AI Detects Attack not Found on Threat Intel

In October, 2019 a MixMode customer experienced an incident where an external entity attacked a web server located in their DMZ, compromised it, and then pivoted internally through the DMZ to attempt access of a customer database. While the attacker was successful in penetrating the customer’s network, MixMode was able to detect the event before they were successful in penetrating the customer database.