With competitive pressure increasing drastically and the digital economy progressing considerably, enterprises need to figure out new ways to plan, develop, and add value. Therefore, to adapt to digital transformation efficiently, DevOps has become a necessity to eliminate technical and cultural constraints for offering value rapidly. Unfortunately, the conservative nature of IT enterprises has led to slower adoption of DevOps. Additionally, many organizations still need to adopt them for efficient processes. The adoption would enhance the efficiency of operational processes and reduce downtime in the development life cycle of software.
Every year, it seems, pundits and egg-heads (like me) write down our predictions. Most of the time they are self-serving. However, looking into my crystal ball, I think 2019 is going to be a defining year at the intersection of physical and cybersecurity. The Internet of Things (IoT) and advances in artificial intelligence are some of the main driving forces in this evolution. Further, with chaos happening throughout the world, and the lead-up to a turbulent US presidential election, there has never been a more perilous--and opportunistic--time for those in the IoT and security business.
Darktrace helped pave the way for using artificial intelligence to combat malicious hacking and enterprise security breaches. Now a new UK startup founded by an ex-Darktrace executive has raised some funding to take the use of AI in cybersecurity to the next level. Senseon, which has pioneered a new model that it calls "AI triangulation" -- simultaneously applying artificial intelligence algorithms to oversee, monitor and defend an organization's network appliances, endpoints, and'investigator bots' covering multiple microservices -- has raised $6.4 million in seed funding. David Atkinson -- the startup's CEO and founder who had previously been the commercial director for Darktrace and before that helped pioneer new cybersecurity techniques as an operative at the UK's Ministry of Defense -- said that Senseon will use the funding to continue to expand its business both in Europe and the US. The deal was co-led by MMC Ventures and Mark Weatherford, who is chief cyber security strategist at vArmour (which itself raised money in recent weeks) and previously Deputy Under Secretary for Cybersecurity, U.S. Department of Homeland Security.
Building smart factories is a substantial endeavor for organizations. The initial steps involve understanding what makes them unique and what new advantages they offer. However, a realistic view of smart factories also involves acknowledging the risks and threats that may arise in its converged virtual and physical environment. As with many systems that integrate with the industrial internet of things (IIoT), the convergence of information technology (IT) and operational technology (OT) in smart factories allows for capabilities such as real-time monitoring, interoperability, and virtualization. But this also means an expanded attack surface.
What makes AI cybersecurity different is its adaptability: It does not need to follow specific rules; rather, it can watch patterns and learn. "Unlike a signature-based approach that delivers a 1-for-1 mapping of threats to countermeasures, data science uses the collective learning of all threats observed in the past to proactively identify new ones that haven't been seen before," said Chris Morales, head of security analytics at Vectra, an AI threat detection vendor. After downloading ransomware, the malware would scan your files, single out what it finds important, make an encrypted copy of those files, delete the original ones and send the encryption keys to the ransomware operators so they have a unique key for every victim. "That sequence of events is pretty unique; you're not going to see a lot of credible software doing that," said Doug Shepherd, chief security officer at Nisos. This limits the usefulness of traditional antivirus software, which looks for signatures detected in known ransomware in order to block a new attack.
In this Help Net Security podcast, Chris Morales, Head of Security Analytics at Vectra, talks about machine learning fundamentals, and illustrates what cybersecurity professionals should know. Hi, this is Chris Morales and I'm Head of Security Analytics at Vectra, and in this Help Net Security podcast I want to talk about machine learning fundamentals that I think we all need to know as cybersecurity professionals. AI has become very used within our industry more and more, and here at Vectra we are an AI company as well. As you start to hear more about AI, you have to start asking yourself what is it really, what makes a machine intelligent and in the next ten minutes I just want to give a quick overview so that you can understand some of the principle operations and applications of how machine learnings apply to build AI, and just kind of a quick understanding of the different algorithms or understanding when you need to use certain algorithms for specific jobs. There has always been a very muddled use of the terms artificial intelligence, data science and machine learning.
AI technology has become widespread and accessible to hundreds of thousands of IT security professionals worldwide. Human researchers are no longer behind their computers crunching the data and numbers, nor should they be when AI technology is available. The increase in computing power, especially through economical cloud solutions and easy-to-use tools, has allowed a much wider range of users to apply sophisticated machine learning and artificial intelligence algorithms to solve their problems. At the same time, companies and security vendors have realized how difficult it is to fight cyber criminals who are constantly evolving to find new ways to infiltrate corporate networks without being spotted. For IT teams, updating and maintaining security solutions and policies to keep up with this volatile threat landscape is extremely costly and an unsustainable solution to protecting against incoming threats.
I just got back from attending IBM Think in San Francisco. Though it was a quick trip across the country, I was inundated with IBM's vision, covering topics from A (i.e. Despite the wide-ranging discussion, IBM's main focus was on three areas: 1) hybrid cloud, 2) advanced analytics, and 3) security. For example, IBM's hybrid cloud discussion centered on digital transformation and leaned heavily on its Red Hat acquisition, while advanced analytics included artificial intelligence (AI), cognitive computing (Watson), neural networks, etc. To demonstrate its capabilities in these areas, IBM paraded out customers such as Geico, Hyundai Credit Corporation, and Santander Bank, who are betting on IBM for game-changing digital transformation projects.
Forbes published an intriguing story about the capacity of AI to serve as a kind of cybersecurity sheriff. Published on February 6, the story stated that AI has already displayed limitless potential in applications across different industries. That much is certainly true. It goes on to say that deploying AI for cybersecurity solutions will help protect organizations from existing cyber threats and help identify newer malware types too. Additionally, AI-powered cybersecurity systems can ensure effective security standards and help in the creation of better prevention and recovery strategies.
This ebook, based on the latest ZDNet/TechRepublic special feature, offers a detailed look at how to build risk management policies to protect your critical digital assets. Security measures have increased significantly in the last several years, and malicious actors have similarly advanced their techniques to keep pace, particularly with advances in attack methods such as fileless malware. Likewise, the security model of'serverless' computing platforms like AWS Lambda are completely different from traditional computers. These itinerant computing concepts are not effectively secured by the traditional model of checking file hashes against known malware samples. For a robust, modern defense, an adaptive monitoring solution that leverages machine learning to identify anomalous patterns indicative of an attack in its infancy is necessary to defend enterprise systems from cyberattacks.