Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users' behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren't expending the time and effort to hack into systems; they're looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon's 2019 Data Breach Investigations Report, 'Phishing' (as a pre-cursor to credential misuse), 'Stolen Credentials', and'Privilege Abuse' account for the majority of threat actions in breaches (see page 9 of the report).
Estonia, a tiny Northern European nation of fewer than 1.4 million inhabitants, has made impressive strides in digitizing, streamlining, and modernizing its government functions. Estonia famously launched its "e-residency" program that allows practically anybody -- including foreigners -- to access Estonian government services.
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.
Great Learning, India's leading Ed-tech platform for working professionals today announced that it is expanding its geographic footprint globally to Europe, Asia Pacific, Africa and the Middle East. The company will offer three of its most popular programs in Data Science & Business Analytics (PGP-DSBA - a special international variant of its business analytics program PGP-BABI ranked #1 in India for the last 4 years), Artificial Intelligence & Machine Learning (PGP-AIML) and Cyber Security (SACSP - Stanford Advanced Computer Security Program) in these geographies. Offered in association with two of the top universities of the world, Stanford University and The University of Texas, Austin, these online programs have already attracted learners from 17 countries including the UK, Singapore, South Africa, UAE, etc. These programs, designed and developed by the top-notch faculty of UT Austin and Stanford, are delivered online by Great Learning, utilizing its unique mentored-learning model where personalized mentorship is provided by expert instructors from Great Learning's 750 Global Guru network. The mentors include industry veterans from companies like Google, Microsoft, Amazon and Walmart.
Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization's security and compliance policies. Data scientists working on machine learning projects need a flexible environment to run experiments, train models, iterate models, and innovate in. They want to focus on building, training, and deploying models without getting bogged down in prepping virtual machines (VMs), vigorously entering parameters, and constantly going back to IT to make changes to their environments. Moreover, they need to remain within compliance and security policies outlined by their organizations.
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.