Fortunately, getting data centers to rely on clean, renewable energy sources and use that energy more efficiently are far easier tasks than reducing the carbon footprint of the billions of digital storage devices that they've replaced. Here is where economic and environmental interests may overlap. Data center companies have every incentive to maximize the efficiency of their resources and reduce their cost. For that reason alone, the world's biggest data center companies—Amazon, Microsoft, and Google—have all begun implementing plans for their data centers to run on 100% carbon-free electricity. Amazon claims to be the world's largest renewable energy purchaser, consistent with its goals of powering its company with 100% renewables by 2025 and to become carbon net-zero by 2040. Microsoft has pledged to be carbon negative by 2030 and to remove from the atmosphere all the carbon the company has ever emitted since it was founded in 1975. To achieve this, it plans on having all of its data centers running on 100 renewable energy by 2025. And Google had already reached its 100% renewable energy target in 2018, though it did so in part by purchasing offsets to match those parts of its operations that still relied on fossil fuel electricity.
Recent high-profile attacks have disrupted global commerce across the world, bringing home the critical importance of maintaining a robust IT security program. The recent ransomware attacks on the Colonial Pipeline, the largest petroleum pipeline in the US, and meat supplier JBS, highlight the cascading, society-disrupting havoc these types of attacks can create. Those concerns increasingly extend to IoT devices, as evidenced by the recent hack of cloud-based security services firm Verkada, where bad actors gained access to 150,000 of the company’s cameras, including inside factories, hospitals, prisons, schools, and even police stations. Vulnerabilities come in many forms and we have known for a long time that the onslaught of IoT devices onto corporate networks is largely unprotected. It’s little wonder then that when the Ponemon Institute surveyed 4,000 security professionals and asked why breaches still happen, the top answer was the increasing attack surface. ... As a networking vendor, connecting people and things is part of Aruba’s core mission.
Combine conventional threat intelligence (a list of all known cyberthreats to date) and use machine learning to understand risks. This should result in a better, more efficient system of threat detection and prevention. This can also help to identify any loophole or threat present in the data. In fact, machine learning can also be used to spot any abnormality or potential vulnerability in the midst of “normal” activity and warn users of a threat before it could compromise essential data. With the right systems in place, your hackers won't even realize that you know of their presence, so you can take immediate measures to ensure the safety of your digital infrastructure. ... In recent years, cryptocurrencies like Bitcoin and Ethereum have been rising in popularity. These cryptocurrencies are built upon blockchain, an innovative technical solution to store a secure, decentralized record of transactions. Blockchain can be used to enable medical records and help in security management by identifying criminal identity loopholes in the system. With blockchain technology, verification keys wouldn't be required anymore. If someone tries to hack the data, the system analyzes the whole mass of data chains.
First and most importantly, banks and credit unions must focus on placing the consumer at the center of the organization, with product silos eliminated in favor of teams aligned around the customer journey. According to the research, 64% of the banking sector’s digital masters have “created personae and journey maps to identify and serve customers better.” Beyond that, it will be imperative to create an agility and flexibility in delivery similar to what exists in fintech and bigtech firms. This will most likely require changes in the composition of boards, top leadership and departmental management who can see banking from a new perspective. New operating models will also be required that will include the collaboration with third-party providers. There also needs to be support of open banking APIs that will enable the offering of new products both within and outside financial services. Bottom line, the infrastructure of banking as well as the perspective of banking’s role in the consumer’s life must change. According to Capgemini, 64% of banks are actively working with a wide ecosystem of partners – such as startups, incubators, technology firms, and even competitors – to co-develop solutions.
Cybersecurity breaches are not always the work of nefarious actors orchestrating a sophisticated hack. Damaging data breaches may be as likely to result from unintentional human error. Even seemingly benign behaviors –– using public Wi-Fi, neglecting to put passwords on computers and mobile devices, and clicking on bad links –– can be all it takes to give cybercriminals the access they need. It does little good to build a digital fortress if there aren’t adequate controls over who gains access, and under what circumstances. ... First, establishing clear SoD helps avoid conflicts that could lead to fraud or other abuse. For large organizations with multiple lines of business, this is particularly important. Investment professionals on a firm’s buy-side, for example, should not have access to the exact same data as those on the sell-side. SoD may also help prevent control failures that can occur when too many people have access to data for which they aren’t necessarily accountable for. By segregating duties (and data access), compliance teams are better positioned to spot weaknesses, while also ensuring that teams and individuals understand exactly what data should be in their purview and what may be off-limits.
Ask yourself what it would take for employee experience to be a delight — for example, through gamified training modules or KPIs. We work with a leading technology firm that asked itself this very question and developed its tools for surveying employees accordingly, designing them to be simple and intuitive, satisfying, and not frustrating. The firm used layman’s terms and an appealing tone of voice in written content such as instructions, explanations, and requests. It avoided jargon. And it invested in interesting, stimulating visual interactions rather than ones that were bland and text-heavy — the new experience was less like a spreadsheet assignment to be endured and more of an opportunity to engage. ... Don’t neglect the foundations. Ultimately, employees have a right to expect that “it just works,” whether “it” is their human resources self-service portal, their expense management system, or their system interoperability. It’s also critical that user experience be accessible to all, including employees with any type of disability.
We started this journey [of leveraging AI] long before we applied machine learning to some other more mature use cases, including our fraud models and some credit risk models. And in the past couple years, especially in the past five years or so, we started to see with certainty that deep neural network models started to outperform almost every other machine learning model when it comes to high dimensional data and highly unstructured data. We not only deal with some of the traditional fields, like customer transactions, but also there are tax consequences and volume history data. Neural network models can effectively deal with all of that. ... First, I think it’s really about recognizing patterns. And if you look at certain use cases where you have customer behavior that’s being repeated and you can expedite that behavior, then that tends to be a real sweet spot for machine learning capabilities. The other thing I would add is we take the decision to apply machine learning techniques quite seriously. We have an entire AI governance board that cross-checks all the models that we build for bias and privacy concerns. So even taking the approach of AI, we have to justify to a number of internal teams why it makes sense.
If network infrastructure is not your specialty, you might question how much requirements for connectivity could really change over 10 years? Does the Network Team really need to develop a completely new solution and live the DevOps dream? The answer to that is a resounding yes! Today’s (not to mention tomorrow’s) requirements for security features and performance are significantly different from 10 years ago; the network infrastructure is key in the cyber security area of protecting vital business processes and applications by controlling data traffic, and the network must support the vastly increasing amount of data traffic that is the result of new streaming and IoT services, for instance. The Network Team was not able to deliver to these expectations with the legacy technology that we were fighting to operate and maintain, and thus, the business was impacted. Internally, the Network Team themselves were also impacted. They felt the heat from several CXOs who were frustrated that they couldn't satisfactorily support top priorities such as the cyber security agenda.
For many large systems, the only possible way to find the best action path is with simulation. In those situations, you must create a digital model of the physical system you want to understand in order to generate the data reinforcement learning needs. These models are called, alternately, digital twins, simulations and reinforcement-learning environments. They all essentially mean the same thing in manufacturing and supply chain applications. Recreating any physical system requires domain experts who understand how the system works. This can be a problem for systems as small as a single fulfillment center for the simple reason that the people who built those systems may have left or died, and their successors have learned how to operate but not reconstruct them. Many simulation software tools offer low-code interfaces that enable domain experts to create digital models of those physical systems. This is important, because domain expertise and software engineering skills often cannot be found in the same person.
Do you have multiple Kubernetes clusters and a service mesh? Do your virtual machines and services in a Kubernetes cluster need to interact? This article will take you through the process and considerations of building a hybrid cloud using Kubernetes and an Istio Service Mesh. Together, Kubernetes and Istio can be used to bring hybrid workloads into a mesh and achieve interoperability for multicluster. But another layer of infrastructure — a management plane — is helpful for managing multicluster or multimesh deployments. ... Using Kubernetes enables rapid deployment of a distributed environment that enables cloud interoperability and unifies the control plane on the cloud. It also provides resource objects, such as Service, Ingress and Gateway, to handle application traffic. The Kubernetes API Server communicates with the kube-proxy component on each node in the cluster, creates iptables rules for the node, and forwards requests to other pods. Assuming that a client now wants to access a service in Kubernetes, the request is first sent to the Ingress/Gateway, then forwarded to the backend service
Quote for the day:
"A good leader can't get too far ahead of his followers." -- Franklin D. Roosevelt