Experts foresight 2020 to bring a tremendous change in the future of the Fintech industry. Goldman Sachs predicts that by the end of 2020, the worldwide Fintech pie will reckon up $4,7 trillion. Parenthetically, the interesting fact is that nearly one-third of Goldman Sachs’ employees are engineers, which makes more than on Twitter or Facebook. We found out the five main trends in Fintech banking that are going to disrupt the industry and drive the immense growth. The age of totally digital banking is approaching. The majority of existing banks already offer global payments and transfers virtually, and those who don’t yet will join the trend. The ability to trade currencies along with Bitcoin and Ethereum online will come to a daily basis, and according to the forecasts, it will lead to a drop in physical bank visits by 36% by 2020. Though in 2019 the blockchain technology became a widely discussed topic, its embodiment into financial services was relatively slower, compared to other spheres. The future of Fintech in 2020 is intimately tied to the blockchain technology, and the main reasons are transparency and trust it guarantees, significantly decreasing the time needed for transactions and improving the cash flow. 77% of surveyed incumbents expect to embrace blockchain by 2020.
It follows earlier reports on Monday that the federal government is crafting minimum cyber security standards for businesses, including critical infrastructure, as part of its next cyber security strategy. The taskforce will focus its efforts on “harmonising baseline standards and providing clarity for sector specific additional standards and guidance” and improving interoperability. It also aims to enhance "competitiveness standards by sector for both supplier and consumers” and support Australian cyber security companies to seize opportunities globally. ... “We know that the current plethora of different security standards make it difficult for government and industry to know what they’re buying when it comes to cyber security,” he said. “By bringing together industry to identify relevant standards and provide other practical guidance, we aim to make government more secure, whilst providing direction for industry to build their cyber resilience. “This will realise our ambition for NSW to become the leading cyber security hub in the Southern Hemisphere.”
However, all that data doesn't need to be handled in centralized servers; similar to the healthcare edge computing use cases, every temperature reading from every connected thermometer, for example, isn't important. Rather, most organizations only need to bring aggregate data or average readings back to their central systems, or they only need to know when such readings indicate a problem, such as a temperature on a unit that's out of normal range. Edge computing enables organizations to take and understand the data near those endpoint devices, thereby limiting the cost and complexity of sending reams of often unneeded data points to central systems, while still gaining the benefits of understanding the performance of its equipment. The ROI of this is critical: The insights into the data generated by endpoint devices enable remote monitoring so organizations can identify performance problems and safety issues early, even when no one is on-site. Using edge computing with predictive and prescriptive analytics can deliver even bigger ROI, as they enable organizations to predict the optimal time to service their equipment.
Detections are an event that looks anomalous or malicious. And the issue today in a modern security operations center (SOC) is that detections can bubble up from many siloed tools. For example, you have firewall and network detection and response (NDR) for your network protection, Endpoint Detection and Response (EDR) for your endpoints’ protection and Cloud Application Security Broker (CASB) for your SaaS applications. Correlating those detections to paint a bigger picture is the issue, since hackers are now using more complex techniques to access your applications and data with increased attack surfaces. Your team is either claiming false positives or an inability to see through these detections and get a sense of what is critical vs. noise. The main purpose of SIEMs is to collect and aggregate data such as logs from different tools and applications for activity visibility and incident investigation. That said there are still a lot of manual tasks needed, like transforming the data including the data fusion to create context for the data, i.e., enrichment with threat intelligence, location, asset and/or user information.
Intel CET deals with the order in which operations are executed inside the CPU. Malware can use vulnerabilities in other apps to hijack their control flow and insert malicious code into the app, making it so that the malware runs as part of a valid application, which makes it very hard for software-based anti-virus programs to detect. These are in-memory attacks, rather than writing code to the disk or ransomware. Intel cited TrendMicro’s Zero Day Initiative (ZDI), which said 63.2% of the 1,097 vulnerabilities disclosed by ZDI from 2019 to today were related to memory safety. "It takes deep hardware integration at the foundation to deliver effective security features with minimal performance impact," wrote Tom Garrison, vice president of the client computing group and general manager of security strategies and initiatives at Intel in a blog post announcing the products. "As our work here shows, hardware is the bedrock of any security solution. Security solutions rooted in hardware provide the greatest opportunity to provide security assurance against current and future threats. Intel hardware, and the added assurance and security innovation it brings, help to harden the layers of the stack that depend on it," Garrison wrote.
We are now starting to see MLOps bring DevOps principles and tooling to ML systems. MLOps platforms like Sagemaker and Kubeflow are heading in the right direction of helping companies productionize ML. They require a fairly significant upfront investment to set up, but once properly integrated, can empower data scientists to train, manage, and deploy ML models. Unfortunately, most tools under the MLOps banner tend to focus only on workflows around the model itself (training, deployment, management) — which represents a subset of the challenges for operational ML. ML applications are defined by code, models, and data4. Their success depends on the ability to generate high-quality ML data and serve it in production quickly and reliably… otherwise, it’s just “garbage in, garbage out.” The following diagram, adapted and borrowed from Google’s paper on technical debt in ML, illustrates the “data-centric” and “model-centric” elements in ML systems.
Although data warehouses can handle unstructured data, they cannot do so efficiently. When you have a large amount of data, storing all your data in a database or data warehouse can be expensive. In addition, the data that comes into the data warehouses must be processed before it can be stored in some shape or structure. In other words, it should have a data model. In response, businesses began to support Data Lakes, which stores all structured and unstructured enterprise data on a large scale in the most cost-effective way. Data Lakes stores raw data and can operate without having to determine the structure and layout of the data beforehand. In the case of the Data Lake, the information is structured at the output when you need to extract data and analyze it. At the same time, the process of analysis does not affect the data themselves in the lake — they remain unstructured so that they can be conveniently stored and used for other purposes. This way we get the flexibility that Data Warehouse hasn't. Thus, the Data Lake differs significantly from the Data Warehouse. However, LSA's architectural approach can also be used in the construction of Data Lake(my representation).
Some IT organizations already had a robust VPN setup, as well as sufficient laptops for staff to continue their work from home. Others, particularly those without remote work policies already in place, had to rush to adjust. But even after all the firefighting, some IT organizations have used the pandemic as an opportunity to identify potential improvements within their environments -- whether through automation or AI, security updates or a streamlined help desk. Use the below synopses of five recent SearchITOperations articles by TechTarget senior news writer Beth Pariseau to explore the adjustments organizations have made to maintain, manage and even optimize IT operations during a crisis. With the overwhelming shift back to localized work environments in the 2010s, many IT organizations in 2020 scrambled to accommodate new restrictions and complications related to COVID-19. In-person, impromptu discussions and weekly co-located meetings became impossible with all staff offsite, which created bottlenecks and, in some cases, a slow-down in productivity.
Treck says in a statement that it has updated its TCP/IPv4/v6 software to fix the issues. JSOF notes that organizations should use Treck's stack version 188.8.131.52 or higher. JSOF dubbed the flaws Ripple20 to reflect how a single vulnerable component can have a ripple effect on "a wide range of industries, applications, companies, and people." The company is due to present its findings at Black Hat 2020, which will be a virtual event. Four of the flaws are rated critical, and two of them could be exploited to remotely take control of a device. Others require an attacker to be on the same network as the targeted device, which makes these flaws more difficult - but not impossible - to exploit. "The risks inherent in this situation are high," JSOF says. "Just a few examples: Data could be stolen off of a printer, an infusion pump behavior changed, or industrial control devices could be made to malfunction. An attacker could hide malicious code within embedded devices for years." Simpson of Armis says that patching will be time consuming since administrators may have to manually update every make and model of vulnerable device.
The cracks began to show in the wake of the global financial crisis as the banks were faced with a difficult challenge in that they had to both drive the costs of their IT infrastructure down to allow them to maintain competitive banking products in the market, as well as having to adapt to shifting consumer expectations and increasingly stringent regulator demands. A notable example of the latter being the introduction of the third installation of the Basel accord, Basel III, which places increasing demands on banks to adapt their core systems in ways that seem to directly clash with the traditional model of end of day batch style processing, such as the requirement of intraday liquidity management. All of a sudden the banks found themselves facing two key challenges that seemed to conflict with each other. To drive the cost of their infrastructure down they needed to get rid of their mainframes and run on leaner infrastructure which would lower the glass ceiling on the amount of processing power in the system. At the same time, to adapt to regulatory changes they had to increase the frequency of their batch processing jobs, which would require more processing power.
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