Home » Security Boulevard (Original) » News » Vectra Raises $100M More for Cybersecurity AI Vectra has garnered another $100 million in funding to accelerate development of a threat detection and response system running in the cloud that makes extensive use of artificial intelligence (AI). This latest round of funding brings the total investment in Vectra to $200 million. Company CEO Hitesh Sheth said Vectra's Cognito platform applies machine learning algorithms to network metadata captured across the extended enterprise.
Cyberattacks have increased on an unprecedented scale. The main reason obviously is our increasing dependence on computing devices (computers, smartphones etc) and the internet for our day-to-day needs. The technology that we depend on today has interconnectedness as one of its salient features. This, plus our habit of using unsecured networks and devices (like, for example, public Wi-Fi) for convenience's sake, too has proven to be the cause for an unprecedented increase in cyberattacks. Of the various technologies that we use today to prevent cyberattacks and to ensure cybersecurity, machine learning deserves special mention.
Every time you connect to the internet from a computer, tablet or smartphone, there is a growing risk of cyberattack. If the threat is aimed at your workplace, then the entire organization around you could be vulnerable as well and, too often, the result is a major data breach. A well-run company, regardless of its size or global reach, must eventually acknowledge that cybersecurity requires a significant investment. But what tools and processes return the most bang for your buck? A growing number of experts believe that new technology based on machine learning and artificial intelligence are where the smart money lies when it comes to computer, network and data security.
According to a survey conducted by Senseon as part of a research project on Small and Medium-sized Enterprises (SMEs), 81 percent of participants believe that AI would improve the future of cybersecurity while only 3 percent disagree. The remaining 16 percent stated that they were uncertain. Due to how innovative AI has become over the past few years, it is reasonable that the survey generated such a result. The question is, how does the aspect of machine intelligence provide benefits to the company and what objectives and initiatives must the developer, analyst and decision maker establish to enable those types of AI to benefit the security of the cyber age? The Microsoft Vice-President of Cybersecurity Solutions Group, Ann Johnson, told the RTE News during a meeting in Dublin that she sees about six and a half trillion cyber threats that Microsoft receive every day.
While AI is being leveraged in a wide number of areas, cybersecurity is one that has received special attention because of the rate at which threats are evolving and the volume of attacks. Organizations require a solution that can keep up. AI sometimes is championed as that solution – a silver bullet that will "solve" cybersecurity. While that isn't the case, AI is an exciting technology that provides some real-world benefits today, and promises to have even greater potential for the future.
The Gartner Security & Risk Management Summit is just a few days away, and I'm delighted to have the opportunity to chat with attendees about how anomaly detection and machine learning can help give your organization a more proactive security posture. You don't need to have been in the cybersecurity space for long to be bewildered by and unsure about vendor claims around artificial intelligence, machine learning, and analytics. At Interset (acquired by Micro Focus in February of this year), we have regular conversations with security professionals who struggle to understand which techniques and tools are effective in boosting breach defense in the real world. Ultimately, these conversations lead to an important question for us: How can you implement user and entity behavioral analytics (UEBA) in a way that will enable an efficient security operations center (SOC)? There are multiple factors that go into an effective UEBA implementation, but it's helpful to start with ensuring that the math and machine learning powering the solution are suitable for your security objectives.
With cyber threats growing in complexity, this world increasingly reliant on computers cannot afford to lag in security. One way we can sure we're always up-to-date is through the use of artificial intelligence (AI) and machine learning (ML) in our cybersecurity solutions. AI and ML enable cybersecurity experts to scour the cyber terrain for threats faster than any human could. The capacity of AI and ML systems to analyze large amounts of data and look at patterns enables them to deploy security solutions quickly. The way we work with cybersecurity couldn't ever hope to keep up with the ability of AI and ML to adapt to the quickly-changing threats as well as their wide offering of solutions.
The interconnectedness of technology, coupled with the growing number of mobile devices, quickly evolving technologies, and more prominent use of Wi-Fi has resulted in a severe uptick of cyber attacks. To ward off impending threats, we're increasingly turning to machine learning for help. Machine learning has the potential to offer better, more efficient solutions than what's currently available on the market to prevent cybercrime. In this article, we'll give a deep-dive into how machine learning is currently improving cybersecurity. Machine learning can be used to monitor and detect breaches in a certain network, and can also help generate an automated response to an attack.
When it comes to training AI/ML models, a popular debate is whether "supervised" or "unsupervised" learning should be used. Supervised learning is based on labeled data and features extracted to derive a prediction model. For malware, this means human experts classify each sample in the data set as good or bad, and feature-engineering is performed to determine what attributes of the malware are relevant to the prediction model prior to training. Unsupervised learning gleans patterns and determines structure from data that is not labeled or categorized. Unsupervised learning proponents claim that it is not limited by the boundaries of human classification and remains free from feature-selection bias.
This is Part 2 of a 3-part series. Security teams today are under-staffed, over-worked, under-funded and struggling to stay abreast of the ever-changing threat landscape. Many security analysts work long hours poring over millions of security events to protect systems and fix vulnerabilities. Simply put, there is too much information and not enough analysts. Fortunately, humans are not the only answer for solving the cybersecurity crisis.