Best practices to protect companies’ operations in the cloud are guided by three fundamental questions. First, who is managing the cloud? Many companies are moving towards a Managed Service Provider (MSP) model that includes the monitoring and management of security devices and systems called Managed Security Service Provider (MSSP). At a basic level, security services offered include managed firewall, intrusion detection, virtual private network, vulnerability scanning and anti-malware services, among others. Second, what is the responsibility shift in this model? There is always a shared responsibility between companies and their cloud infrastructure providers for managing the cloud. This applies to private, public, and hybrid cloud models. Typically, cloud providers are responsible for the infrastructure as a service (IaaS) and platform as a service (PaaS) layers while companies take charge of the application layer. Companies are ultimately responsible for deciding the user management concept for business applications, such as the user identity governance for human resources and finance applications.
30 years ago, open source [databases were not] the norm. If you told people, “Hey, here’s an open source database,” they’re going to say, “Okay? What does that mean? What is it? What does it really mean? And why should I be excited?” And so on. I remember because at Facebook I was a part of the team that built an open source database called Cassandra, and we had no idea what would happen. We thought “Okay, here’s this thing that we’re putting out in the open source, and let’s see what happens.” And this is in 2007. Back in that day, it was important to use a restrictive license — like GPL — to encourage people to contribute and not just take stuff from the open source and never give back. So that’s the reason why a lot of projects ended up with GPL-like licenses. Now, MySQL did a really good job in adhering to these workloads that came in the web back then. They were tier two workloads initially. These were not super critical, but over time they became very critical, and the MySQL community aligned really well and that gave them their speed. But over time, as you know, open source has become a staple. And most infrastructure pieces are starting to become open source.
McAfee MVISION Cloud Firewall is a cutting-edge Firewall-as-a-Service solution that enforces centralized security policies for protecting the distributed workforce across all locations, for all ports and protocols. MVISION Cloud Firewall allows organizations to extend comprehensive firewall capabilities to remote sites and remote workers through a cloud-delivered service model, securing data and users across headquarters, branch offices, home networks and mobile networks, with real-time visibility and control over the entire network traffic. The core value proposition of MVISION Cloud Firewall is characterized by a next-generation intrusion detection and prevention system that utilizes advanced detection and emulation techniques to defend against stealthy threats and malware attacks with industry best efficacy. A sophisticated next-generation firewall application control system enables organizations to make informed decisions about allowing or blocking applications by correlating threat activities with application awareness, including Layer 7 visibility of more than 2000 applications and protocols.
Customer journey orchestration allows an organization to meaningfully modify and personalize a customer’s experience in real-time by pulling in data from many sources to make intelligent decisions about what options and offers to provide. While this sounds like a best-case scenario for customers and company alike, it requires data sources to be unified and integrated across channels and environments. This is where good data governance comes into play. Even though many automation tasks may fall in a specific department like marketing or customer service, the data needed to personalize and optimize any of those experiences is often coming from platforms and teams that span the entire organization. Good data governance helps to unify all of these sources, processes and systems and ensures customers receive accurate and impactful personalization within a wide range of experiences. As you can see, data governance can have a major influence over how the customer experience is delivered, measured and enhanced. It can help teams work better together and help customers get more personalized service.
Our continued reliance on passwords for authentication has contributed to one toxic data spill or hack after another. One might even say passwords are the fossil fuels powering most IT modernization: They’re ubiquitous because they are cheap and easy to use, but that means they also come with significant trade-offs — such as polluting the Internet with weaponized data when they’re leaked or stolen en masse. When a website’s user database gets compromised, that information invariably turns up on hacker forums. There, denizens with computer rigs that are built primarily for mining virtual currencies can set to work using those systems to crack passwords. How successful this password cracking is depends a great deal on the length of one’s password and the type of password hashing algorithm the victim website uses to obfuscate user passwords. But a decent crypto-mining rig can quickly crack a majority of password hashes generated with MD5 (one of the weaker and more commonly-used password hashing algorithms).
From the wide adoption of cloud-based services to the proliferation of mobile devices. From the emergence of advanced new cyberthreats to the recent sudden shift to remote work. The last decade has been full of disruptions that have required organizations to adapt and accelerate their security transformation. And as we look forward to the next major disruption—the move to hybrid work—one thing is clear: the pace of change isn’t slowing down. In the face of this rapid change, Zero Trust has risen as a guiding cybersecurity strategy for organizations around the globe. A Zero Trust security model assumes breach and explicitly verifies the security status of identity, endpoint, network, and other resources based on all available signals and data. It relies on contextual real-time policy enforcement to achieve least privileged access and minimize risks. Automation and machine learning are used to enable rapid detection, prevention, and remediation of attacks using behavior analytics and large datasets.
The reason why managed Kubernetes is now gaining traction is obvious, according to Brian Gracely, senior director product strategy for Red Hat OpenShift. He pointed out that containers and Kubernetes are relatively new technologies, and that managed Kubernetes services are even newer. This means that until recently, companies that wanted or needed to deploy containers and use Kubernetes had no choice but to invest their own resources in developing in-house expertise. "Any time we go through these new technologies, it's early adopters that live through the shortfalls of it, or the lack of features or complexity of it, because they have an immediate problem they're trying to solve," he said. Like Galabov, Graceley thinks that part of the move towards Kubernetes as a Service is motivated by the fact that many enterprises are already leveraging managed services elsewhere in their infrastructure, so doing the same with their container deployments only makes sense. "If my compute is managed, my network is managed and my storage is managed, and we're going to use Kubernetes, my natural inclination is to say, 'Is there a managed version of Kubernetes' as opposed to saying, 'I'll just run software on top of the cloud.'" he said. "That's sort of a normal trend."
"While it may be impossible to eliminate the risks inherent in AI, we are developing this guidance framework through a consensus-driven, collaborative process that we hope will encourage its wide adoption, thereby minimizing these risks," Tabassi said. NIST noted that the development and use of new AI-based technologies, products and services bring "technical and societal challenges and risks." "NIST is soliciting input to understand how organizations and individuals involved with developing and using AI systems might be able to address the full scope of AI risk and how a framework for managing these risks might be constructed," NIST said in a statement. NIST is specifically looking for information about the greatest challenges developers face in improving the management of AI-related risks. NIST is also interested in understanding how organizations currently define and manage characteristics of AI trustworthiness. The organization is similarly looking for input about the extent to which AI risks are incorporated into organizations' overarching risk management, particularly around cybersecurity, privacy and safety.
The worsening skills shortage comes as companies are adopting breach-prone remote work arrangements in light of the pandemic. In its report today, IBM found that the shift to remote work led to more expensive data breaches, with breaches costing over $1 million more on average when remote work was indicated as a factor in the event. By industry, data breaches in health care were most expensive at $9.23 million, followed by the financial sector ($5.72 million) and pharmaceuticals ($5.04 million). While lower in overall costs, retail, media, hospitality, and the public sector experienced a large increase in costs versus the prior year. “Compromised user credentials were most common root cause of data breaches,” IBM reported. “At the same time, customer personal data like names, emails, and passwords was the most common type of information leaked — a dangerous combination that could provide attackers with leverage for future breaches.” IBM says that it found that “modern” security approaches reduced expenses, with AI, security analytics, and encryption being the top three mitigating factors.
An open-source machine learning framework, BERT, or bidirectional encoder representation from a transformer is used for training the baseline model of NLP for streamlining the NLP tasks further. This framework is used for language modeling tasks and is pre-trained on unlabelled data. BERT is particularly useful for neural network-based NLP models, which make use of left and right layers to form relations to move to the next step. BERT is based on Transformer, a path-breaking model developed and adopted in 2017 to identify important words to predict the next word in a sentence of a language. Toppling the earlier NLP frameworks which were limited to smaller data sets, the Transformer could establish larger contexts and handle issues related to the ambiguity of the text. Following this, the BERT framework performs exceptionally on deep learning-based NLP tasks. BERT enables the NLP model to understand the semantic meaning of a sentence – The market valuation of XX firm stands at XX%, by reading bi-directionally (right to left and left to right) and helps in predicting the next sentence.
Quote for the day:
“Whenever you find yourself on the side of the majority, it is time to pause and reflect.” -- Mark Twain