Did you miss a session at the Data Summit? MixMode, which today announced a $45 million series B funding round, has a massive opportunity ahead to deploy its self-learning, "third-wave" AI system to proactively secure customers against previously unknown cyberattacks, CEO John Keister told VentureBeat. A significant portion of the hundreds of billions of dollars spent each year on cybersecurity is focused on signature-based solutions, which only protect against the 20% of successful attacks that had previously been seen, Keister said. But the other 80% of cyberattacks (according to figures from the Ponemon Institute) are novel attacks -- and identification of those requires advanced AI capabilities, he said. "The existing systems simply don't address that 80%," Keister said.

Recent reports indicate Artificial Intelligence (AI) investment will reach an all-time high with even'traditional industries' jumping on board. It is also indicated that these industries will gain the greatest impact from the implementation of AI versus industries that are already highly data-driven or using advanced technology. The study suggests that, although AI technologies currently account for US$12.4 billion (around £9.40 billion) of global investment, this number will skyrocket in the next three years, with 40 percent of executives expected to increase their AI investments by 20 percent or more. Even, in current times as well we can see millions of dollars of investment in AI. Therefore, we have enlisted the top 10 AI investments and funding of April 2020.

If you're uncertain whether AI is the best or worst thing to ever happen to cybersecurity, you're in the same boat as experts watching the dawn of this new era with a mix of excitement and terror. AI's potential to automate security on a broader scale offers a welcome advantage in the short term. Yet unleashing a technology designed to eventually take humans out of the equation as much as possible naturally gives the industry some pause. There is an undercurrent of fear about the consequences if things run amok or attackers learn to make better use of the technology. "Everything you invent to defend yourself can also eventually be used against you," said Geert van der Linden, an executive vice president of cybersecurity for Capgemini.

The cybersecurity provider landscape is cluttered with impossible claims, misrepresentations, and a confusing mix of inconsistent terminology. Worse, every minute you delay making a decision is another minute hackers have to gain access and knowledge about your network. With so much on the line, choosing what kind of platform and which company to trust with your company's data privacy can become a stressful decision. Leaning toward an AI-enabled platform is a step in the right direction, but which platforms actually do what they say they do? Luckily, you don't have to become an expert in AI cybersecurity to learn how to evaluate the efficacy of AI-enabled cybersecurity platforms.

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. Dr. Igor, Chief Scientist and CTO at MixMode explains: Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning).

Historically, the MixMode platform has provided its users with a forensic hunting platform with intel-based Indicators and Security Events from public & proprietary sources. While these detections still have their place in the security ecosystem, the increase in state-sponsored attacks, insider threats and adversarial artificial intelligence means there are simply too many threats to your network to rely on solely intelligence-based detections or proactive hunting. Many of these threats are sophisticated enough to evade traditional threat detection or, in the case of zero-day threats, signature-based detection may not even be possible. In the face of this growing threat, the best defense is to supplement these traditional methods with anomaly detection, a term that is quickly becoming genericized as it is rapidly bandied about within the industry. Here we will discuss some of the opportunities and challenges that can arise with anomaly detection as well as MixMode's unique approach to the solution.