Daily Tech Digest - November 09, 2017

Graphic collage of laptop with international map and networking
So what can those companies do? Instead of plugging data in random tools online, tell employees to route all translation through a professional provider. Translation vendor selection is usually based on quality, turnaround and cost. To ensure data security, ask prospective resources how they receive and deliver files for translation. If they say email, watch out. “[Email is] 10 times riskier than any [online] solution because it’s very easy to break into people’s email,” Vashee says. Email is also readily forwarded — something many translation companies depend on. A human translator gets the job by specializing in that content type and the language direction needed — English into Polish, for example. If either of those factors change, so does the translator. As a result, even the largest translation companies don’t have in-house resources for everything you need. 


What to consider when deploying a next-generation firewall

When consulting with vendors on a NGFW deployment, one of the first conversations will be around the organization’s security posture. No amount of technology can replace the critical work of evaluating an environment and prioritizing the most important business-critical assets that need to be protected. This is a conversation that may include multiple departments, from IT to network and security services, to HR and executive leadership. “Basically, organizations need to figure out, if they don’t already know, where the pearls of their data are and make a plan around protecting that,” says Gartner researcher Hils. Organizations typically gather these requirements and approach multiple vendors for a quote. Most firewalls are still deployed at the perimeter of the data center, but depending on if customers have adopted microsegmentation and network virtualization there could be firewalls deployed within the data center as well.


Tim O'Reilly: The flawed genie behind algorithmic systems

The algorithms took on the biases of the user, delivering content that reflected their likes -- and dislikes. Algorithmic systems, he argued, are a little like the genies of Arabian mythology. "These algorithms do exactly what we tell them to do. But we don't always understand what we told them to do," he said. Part of the problem is that developers don't know how to talk to algorithms and ask for the right wish, he said. Consider the financial markets, which today are vast algorithmic systems with a master objective function to increase profits. "The idea was that this would allow businesses to share those profits with shareholders who would use [them] in a socially conscious way," he said. "But it didn't work out that way." Instead, financiers are gaming the system, creating income inequality.


Q&A: Secure data centres and fintech companies


We believe there are two core aspects to a data centre that make them attractive to Fintech businesses. The first is security. This does not just include physical and cyber security, which of course are immensely important, it also includes security of service. Fintechs need to know that their product will always be available, that they won’t experience any outages or disruption in service, that could potentially prove to be a huge cost financially and to their reputation. Data centres must ensure that they have a robust infrastructure in place to ensure that they can provide a secure and reliable service to their partners. ... Fintech businesses must be able to prove to the Financial Conduct Authority (FCA) that they are not introducing any degree of risk to the financial services environment, so opting for a data centre provider who has a pedigree in compliance and security is vital.


3 Ways to Mitigate Insider Threat Risk Prior to an Employee’s Departure



We are seeing a disturbing insider threat trend impacting operations and causing reputational harm in the days leading up to an employee’s departure from an organization. For example, last week a Twitter employee deleted President’s Trump’s Twitter account prior to leaving the premises on his last day of employment. In September, a contractor was convicted of cyber sabotage on an Army computer toward the end of his contract, costing U.S. taxpayers millions. These cases highlight the importance of ensuring that the appropriate insider threat risk mitigations are in place to help your organization prevent, detect, and respond to an insider incident. Whether termination or resignation, an employee’s pending departure from your organization increases the chance that data leaks or sabotage will occur that could impact operations, lead to the loss of competitive advantage, affect shareholder value, or result in embarrassment and devaluation of image and brand.


Three trends to keep top of mind when crafting an AI strategy

New interfaces will dramatically change the way consumers and employees access computing resources, Andrews said. Specifically, the new wave of interfaces relies on natural language processing and generation, visual analytics and gesture interpretation -- technologies powered by AI.  ... AI capabilities are being embedded into the internet of things (IoT) devices that operate on the computing edge, but those capabilities will be limited. Model building with AI will happen elsewhere, but runtime analysis and "interaction into action models" that provide, say, visual analysis can live on an edge device, Andrews said. ... AI-powered applications will be able to tell each other what they need to meet a goal without human interaction. But to create this kind of commonplace AI, application diversity is crucial. "In any ecosystem, strength comes from that diversity and from multiple perspectives," he said.


Xerox CISO: How business should prepare for future security threats

10 threat landscape apocalypse ruins
As we move to AI, then we also have to move into AI in a security space ‑‑ thinking about the talent shortage, thinking about the fact that we're not going to close this talent gap. How do we close the talent gap? How do we get around it? By allowing AI, allowing robots and smart learning and things like that to play a role in this. We need to challenge our vendors and say, “You've got great platforms that perform analytics for me, but now I need these great platforms to not just perform the analytics, but to actually do something.” That's where it stops. It stops at analytics, and then it expects you've got a team of people that will actually do [something with the data]. It would be great if, as the smart security people that we are, we could say these are the list of security things that I am comfortable with a machine doing for me.


Hacking medical devices is the next big security concern

“Successful exploitation of these vulnerabilities may allow a remote attacker to gain unauthorised access and impact the intended operation of the pump,” says the ICS-CERT report. In other words, with enough skill, a hacker could change the quantities of medication administered to a patient. Smiths Medical said the chances of this happening are “highly unlikely”, but has promised a software security update to resolve the issues by January 2018. Smiths Medical is not the only device manufacturer under fire. There are plenty of others, including St Jude Medical, which is currently battling lawsuits relating to vulnerabilities in its implantable cardiac defibrillators and pacemakers. These triggered a recall of some 465,000 devices in August this year, which will involve patients attending hospitals and clinics in order for the devices to be updated. No invasive surgery will be needed, but the procedure must be carried out by medical staff.


Why speeds and feeds don’t fix your data management problems

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Applications and storage have long been oblivious to each other’s capabilities and needs. The majority, if not nearly all, of today’s enterprise applications do not know the attributes of the storage where its data resides. Applications cannot tell if the storage is fast or slow, premium or low cost. They are also unaware of storage’s proximity, and factors like network congestion between storage and the application server, which can significantly impact latency. Conversely, storage does not know what data is the most important to an application. It only knows what was recently accessed, and uses that information to place data in caching tiers, which will increase performance if that same data happens to be accessed again. Some enterprises try to address these issues with caching tiers, but unfortunately caches do not have the intelligence needed to reserve capacity for mission-critical applications.


Security Think Tank: Web security down to good risk management

Encrypting data, both when it is in transit and being stored, is key, and its importance will grow with the adoption of GDPR in 2018. Similarly, encrypting the hard drives of corporate laptops minimises breaches if they are lost or stolen. However, it is not just the external threat landscape that is changing. Having robust internal controls, including access management strategies can also minimise threats. Many security fails are down to providing users with more system access than is needed to undertake the task they are required to do. Outlining the inputs that an application needs to work and then engaging in role management and access control to ensure that no more than that is available prevents this – and is particularly important where external attacks attempt to take control of an existing user in the target application.



Quote for the day:


"Education is when you read the fine print. Experience is what you get if you don't." -- Pete Seeger


Daily Tech Digest - November 08, 2017

How to Make the Most Out of Email GIFs


An email packed with info may cause readers to skim and overlook important details. Use GIFs to make pieces of information stand out—or to help explain concepts. When we redesigned MailChimp’s dashboard in 2013, for example, we used several GIFs in an email to show the changes to our users. Breaking up this information with short, digestible visuals helped our users understand the concepts and get them used to the new dashboard without spending a ton of time reading. Most email clients don’t offer great support for video. Currently, videos only render in Apple Mail, Thunderbird, iOS10’s native client, and Samsung Galaxy’s native client. On top of poor support, video files can be huge and slow to load if subscribers are using a poor connection, which can disengage the viewer. Making subscribers download large files isn’t a great idea, either, because it can send users over their data plans if they’re not on WiFi.


Top Use Cases for IoT in Property and Casualty Insurance

IoT enablement in Insurance is the new normal for both Insured and Insurer. For Insurers, it helps improve the underwriting process through finer risk segmentation, agile pricing, improved loss and combined ratios, cross sell and up sell opportunities. Further, it enables customer- centric product offerings, increased brand loyalty, customer churn reduction, simplified claims processing and more. Similarly, the Insured is able to reap the benefits of competitive pricing, quicker policy and claims servicing, personalized offerings, constant updates on risk variations through proactive alerts and advice on risk management and more. The incremental adoption of IoT enabled connected device usage by Insurers is helping conceptualize a “Pay as You Use” model that offers customized pricing and servicing to eligible customers.


What ‘born in the cloud’ means for developers


Sigler notes that cloud-native means developers no longer have to keep reinventing the wheel, and “going cloud native acts as a ‘forcing function’ for how applications are built on top of infrastructure”. By standardising on the behaviour of lower-level components such as compute and networking, he says businesses are effectively telling individual teams working on these smaller, more agile units of software to stop wasting their time on changing everything below the application layer. This, says Sigler, is different to the approach previously taken with traditional or virtualised application designs, where developers tended to spend lots of time reinventing how they would ship the software. Not only is this a painful process, it is one that does not often result in useful business value, he says.


How Artificial Intelligence Could Change the Medical Field

How Artificial Intelligence Could Change the Medical Field
Most intriguing is the possibility of AI identifying new associations and correlations that are yet to be detected by humans. For example, UK researchers turned over the data of about 295,000 patients to AI, to allow them to correlate medical history with the rate of heart attacks. After that, the AI was given another record of 82,000 patients whose history of heart attacks were already known for the AI to predict the ones that are most likely to have a heart attack. The result of the AI when compared to the predictions based on current “best practice” American College of Cardiology/American Heart Association (ACC/AHA) guidelines, which include patient age, smoking history, cholesterol levels, diabetes history, etc. the AI beat the human's hands down.


How to Create an Intelligent Company

Design Thinking is one of the ways in which this change can be brought about. Design Thinking is part of a broad methodology that amalgamates elements of imagination, intuition, holistic reasoning, and logic to explore all the probable solutions for a given problem. It includes the identification of all unarticulated needs expressed by a consumer. After the identification of the needs, the team creates solutions that address all needs and end up creating the “wow” effect. The solutions are generated creatively and analytically as Design Thinking is more solution oriented than being problem oriented. Reaching a feasible conclusion is frequent in Design Thinking. The risk inherent within innovative solutions is minimized by transitioning users through numerous prototypical solutions that give leverage for learning, testing and completely refining the ultimate solution.


How to choose a database for your microservices

How to choose a database for your microservices
In many cases these new databases were “NoSQL” or “non-relational”—solutions based on data models other than that dominant relational model, such as document, key-value, column oriented, and even graph databases. Frequently these databases sacrificed some of the familiar guarantees of relational databases like strong consistency, ACID transactions, and joins. At the same time as this revolution in database technology, the SOA trend of the early 2000s was maturing into the microservices architectural style, as many organizations began to move away from heavyweight SOA infrastructures such as the enterprise service bus (ESB) toward a more decentralized approach. The appeal of microservices architecture lies in the ability to develop, manage and scale services independently. This gives us a ton of flexibility in terms of implementation choices, including infrastructure technology such as databases.


Cheat sheet: How to become a data scientist

"One of the big reasons we continue to see such demand for data scientists is every company out there is becoming a tech company," Allison Berry, Glassdoor community expert, told TechRepublic. "In any industry that has to deal with digitized data, or has an app or an online presence, you need people who can help support all of that and find insights from the data." However, we are currently facing a shortage of professionals with data science skills: By 2020 the number of annual job openings for all data savvy professionals in the US will increase to 2.7 million, IBM predicted. Those with data science skills can command an average salary of $96,441 in the US as of October 2017, with 0.9% year-over-year growth, according to Glassdoor. To help those interested in the field better understand how to break into a career in data science, we've created a guide with the most important details and resources.


Cyber threat, not credit, is what keeps today’s bank CEOs up at night

“The new cyber threat to deal with — which we’ve never dealt with before — is how do we ensure that the information from our customers is really accurate? Is it really our customers?” Sloan said, pointing to the “amount of data that is now out there” following the Equifax hack. “We haven’t dealt with that, and we’re going to all figure it out,” Sloan added. Several of the executives emphasized the importance of collective action in addressing the growing threat from cybercriminals. “We have a tremendous amount of data on our customers,” says Grayson Hall, chairman and CEO of Regions Financial. “With that information comes an awful lot of responsibility and accountability.” The comments — made at an industry conference sponsored by The Clearing House — illustrate some of the most pointed commentary to date on what the massive Equifax breach means for banks’ core businessess.


4 Ways the Next Generation of Security Is Changing

There are two forces that demonstrate my point. First is the reality that breaking news on a weekly basis surrounds enormous data leaks — just recently, Equifax, Yahoo, the Securities and Exchange, and Sonic — and a stunning lack of clarity around the extent and scope of data that has been compromised in each case. The second force is the European Union's General Data Protection Regulation. Organizations have not mapped out their data, and they're struggling now to comply with EU regulations. As a result, enterprises are making moves to locate, classify, and understand who's accessing their data and where it's being stored, and utilizing more advanced frameworks for data monitoring and controls. This data transparency is no longer a nice-to-have, particularly given impending regulatory deadlines.


Proactively Managing Data Compliance With Encryption Strategies

evildoers aren’t in our midst
There is a perception problem with encryption, where companies consider it to be a time-consuming process that is not worth the effort when compared to the perceived risk of being hacked. The “it won’t happen to us” mentality is pervasive, despite the industry predictions that cybercrime damages will cost the world $6 trillion annually by 2021 (according to Cybersecurity Ventures). Whether a firm believes their current safeguards are sufficient, or that hackers won’t target their business, they avoid encryption until it’s simply too late. They are not performing the usual risk/reward that organizations should consider when weighing the value of data and the downsides of a breach. Encryption is also not as mysterious and complex as many believe. It simply involves taking data and translating it into a different form that requires an access key to read, share and edit.



Quote for the day:


"Don't wait for inspiration. It comes while one is working." -- Henri Matisse


Daily Tech Digest - November 07, 2017

The best way to bring groups together and bridge the cultural divide is to identify a common benefit for the company, Streenstrup said. Potential benefits include reducing technology costs (number of software licenses, support staff requirements) reducing risk (exposure to cybersecurity attacks), agility and speed (moving more quickly than competitors) and unlocking the benefits of equipment data (“the big holy grail”). “If you can unlock the value in that equipment data, safely and reliably, now you get much better visibility into the plant or the machinery is doing,” Streenstrup said. “The two highlights there are operational efficiency, how much material energy or materials do you put in to get a benefit and also reliability – how do we know what the machine is doing so we can get in there before it fails?”


40% of IT security leaders don't change default admin passwords

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IT faces challenges in monitoring admin accounts as well: 57% of professionals said they only monitored some privileged accounts, or did not monitor privileged access at all. And 21% said they are unable to monitor or record activity performed with admin credentials at all. Gaining access to privileged accounts is the easiest way for cybercriminals to steal an organization's critical data and systems, One Identity noted in the report. "By not adhering to these best practices, privileged accounts are vulnerable to open the door to data exfiltration or worse, if compromised," according to a press release. "When an organization doesn't implement the very basic processes for security and management around privileged accounts, they are exposing themselves to significant risk," John Milburn, president and general manager of One Identity, said in a press release.


Chatbots can do more than chit-chat

Chatbots AI
For businesses, chatbots have the potential to be an incredibly financially-efficient solution. Take a use case that is commonly explored today: customer service. At their best, a chatbot can solve a customer’s query on its own, reducing the human resource required by a brand and satisfying the customer’s need – which we know is the best way to have customers coming back for more. However, the mysticism and confusion around chatbots means that, too often, businesses that are building and deploying AI and chatbot solutions focus on the wrong thing – trying to make their chatbots good at chat. This is not to say a natural conversation flow isn’t important to a customer experience – it is. However, brands should remember their bots are not going to be used in the same way Siri and Alexa are – they are there to fulfil a business function, not answer questions on the weather in Barcelona tomorrow.


5 Predictions On How Blockchain Will Drive Digital Transformation


The power of Blockchain can essentially eliminate the “middle man” in financial transactions like loans, wire transfers, and other services that require often exorbitant transaction service fees. And I don’t just mean removing the need for bank tellers. I’m talking about the ability to turn all currency digital so that it no longer needs to stored or secured at all. Even beyond Bitcoin, Blockchain could be used to develop local currency or internationally accepted money — depending on the needs of the industry or user. Although you’d think Big Business in the form of the country’s major financial institutions would be pushing hard against Blockchain because of its potential to put them out of business, you’d be wrong. Research shows 65% of banks plan to implement some form of Blockchain in the next few years. That’s how powerful this technology has become.


The Industry Just Can't Decide about DevOps Teams

For developers to take responsibility for the systems they create, they need support from operations to understand how to build reliable software that can be continuous deployed to an unreliable platform that scales horizontally. They need to be able to self-service environments and deployments. They need to understand how to write testable, maintainable code. They need to know how to do packaging, deployment, and post-deployment support. Somebody needs to support the developers in this, and if you want to call the people who do that the "DevOps team", then I'm OK with that. ... Dedicated DevOps teams are often made up of experienced operations people with a mix of skills including using version control, writing infrastructure as code, and continuous delivery. These teams typically start by addressing the things that are most painful, such as deployment automation, and if they're successful, can evolve to providing shared services for the rest of the organization.


Is artificial intelligence safe?


"Data is the feedstock of AI, especially unstructured data, giving insights into customer intent, employee behavior. However, as consumers realise quite how much data is being collected on them to fuel these models and algorithms, there will be pushback as more stringent privacy controls are demanded. "There is the danger of bias being baked into machine learning applications at any stage, be it the data, the training of models and or the programming of algorithms. Developers and owners of those applications need to guard against this but also make the applications sufficiently transparent so biases can be detected and fixed at whatever stage they occur." ... Jane Zavalishina, CEO of Yandex Data Factory, argues that firms will likely struggle to integrate AI systems into existing business operations and humans will still be more capable in other areas, such as common sense and compassion.


Demand for enterprise architecture surges

"At the same time as these technology developments are happening companies are also globalising, innovating their business models while having to deal with different regulatory regimes around the world. "Enterprise architects are therefore required to ensure that IT landscapes are optimised: cost-effective, open and collaborative yet secure and private, scalable and flexible," Carpenter explains. Roland Woldt, director of KPMG's Enterprise Architecture Practice, says EA has evolved substantially from its early days when it was seen strictly as a technical way to wire up an organisation's infrastructure. Today's EA is more focused on business outcomes – what KMPG calls "capability-centric architecture": the capabilities needed to make digital transformation happen. According to Woldt, with its many moving parts and a myriad of direct and indirect relationships with partners, customers and vendors, EA has become incredibly complex.


CIOs should lean on AI 'giants' for machine learning strategy


"It may not be true that you can solve it with machine learning," Wilder-James said. "This is one important difference from other technical rollouts. You don't know if you'll be successful or not. You have to enter into this on the pilot, proof-of-concept ladder." The most time-consuming step in deploying a machine learning model is feature engineering, or finding features in the data that will help the algorithms self-tune. Deep learning models skip the tedious feature engineering step and go right to the training step. To tune a deep learning model correctly requires immense data sets, graphic processing units or tensor processing units, and time. Wilder-James said it could take weeks and even months to train a deep learning model.


SaaS, PaaS, and IaaS: Understand the differences

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According to 451 Research analyst Carl Brooks, for a technology solution to qualify as "as a Service," it has to meet the National Institute of Standards and Technology (NIST) definition parameters, which he paraphrased as "self-service, paid on-demand, elastic, scalable, programmatically accessible (APIs), and available over the network." In a general sense, the cloud is divided into three distinct layers: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The fundamental model of cloud computing that underpins all three of these layers is a service rental model, according to Forrester Research principal analyst Dave Bartoletti. "You are renting infrastructure, or you are renting development platforms and tools, or you are renting software. That's IaaS, PaaS, and SaaS," Bartoletti said.


Bridging the Cyber Security Skills Gap


As threats have become increasingly dynamic and automated, DDoS detection and mitigation solutions are rising to the challenge with their own increase in automation and adaptability. According to Radware’s Cyber-Security Perceptions and Realities: A View from the C-Suite report, 38% of IT executives throughout the United States and Europe indicate that automated security systems – such as machine learning and AI – will be the primary resource for maintaining cyber security within the next two years. But it presents a catch-22 for the next-generation security professional. As a security professional, when you’re increasingly relying on automation to defend the network, you’re not “practicing” or fine tuning your skill sets. The DDoS mitigation solution is doing a lot of the heavy lifting and the network security professional is receiving and digesting reports. This can create a void in skill sets due to lack of “practice.”



Quote for the day:


"Hiding from yourself is the surest path to self hatred, self pity and a whole lot of missed potential." -- Jon Westernberg


Daily Tech Digest - November 06, 2017

Google can read your corporate data. Are you OK with that?
The big concern from enterprises this week was not being locked out of Google Docs for a time but the fact that Google was scanning documents and other files. Even though this is spelled out in the terms of service, it’s uncomfortably Big Brother-ish, and raises anew questions about how confidential and secure corporate information really is in the cloud.  So, do SaaS, IaaS, and PaaS providers make it their business to go through your data? If you read their privacy policies (as I have), the good news is that most don’t seem to. But have you actually read through them to know who, like Google, does have the right to scan and act on your data? Most enterprises do a good legal review for enterprise-level agreements, but much of the use of cloud services is by individuals or departments who don’t get such IT or legal review.


How microservices governance has evolved from SOA


Governance with monoliths is centralized. Decisions are made top-down, and rigid control is maintained to ensure uniformity across the organization and the application stack. Over time, this model degenerates, creates a system that becomes technologically and architecturally stagnant and slows down the pace of innovation. Teams are forced to merely conform to the set order of things rather than look for new, creative solutions to problems. For microservices governance, a decentralized model works best. Just as the application itself is broken down into numerous interdependent services, large, siloed teams are broken down into small, multifunctional teams. This follows the progression from development, testing and IT teams morphing into smaller DevOps teams.



5 cyber threats every security leader must know about

The first is Consumer IoT. These are the devices we are most familiar with, such as smartphones, watches, appliances, and entertainment systems. Users insist on connecting many of these to their business networks to check e-mail and sync calendars, while also browsing the Internet and checking on how many steps they have taken in the day. The list of both work and leisure activities these devices can accomplish continues to increase, and the crossover between these two areas presents increasing challenges to IT security teams. ... The cloud is transforming how business is conducted. Over the next few years, as much as 92 percent of IT workloads will be processed by cloud data centers, with the remaining 8 percent continuing to be processed in traditional on-premises data centers.


Inside-Out: How IoT Changes Everything


"Design thinking is a way to place the user at the heart of the innovation process," he said. "Our company strategy is really that innovation is not coming from startups or technologies, but from the end users and the customer observation. It's really focused on the end user. We are working, for example, with ethnologists and psychologists to understand the problems and to describe the problems. It's really important for us." Celier explained that VISEO created specialized innovation centers as part of their One Roof program. The idea is to bring clients into their production studios, much like filmmakers bring all the talent into a studio for producing movies. "We are incubating our customer's project in our building. It's a way to go faster. They come with their vision, their idea, and they leave with a platform or product," he said.


Cybersecurity thwarts productivity and innovation, report says


The top priority of most organizations — cybersecurity — is hindering productivity and innovation, according to a recent report by Silicon Valley-based virtualization firm Bromium. Based on a survey of 500 chief information security officers in large organizations in the U.S., U.K. and Germany, 74 percent of respondents said end users were frustrated by how security requirements disrupt operations. "Our research found, on average, an organization gets complaints from users twice a week saying that legitimate work activity is being blocked or rejected by over-zealous security systems," the report reads. Citing that most — 88 percent — of organizations use a prohibition approach to cybersecurity, the firm suggests "a new approach" that allows more technological innovation within the organization.


Securing Smart Homes

“The industry is starting to get educated about the need for [better security],” Dirvin says. “Now they ask more questions about it and are willing to spend more time and effort,” but not always money. Manufacturers of smart home devices typically haven’t had to think about security in the same way as a medical device maker or a manufacturer of industrial automation. “It’s a whole new area for them, so they’re rushing to build connectivity and incorporate these devices into a broader IoT strategy,” says Warren Kurisu, director of product management in the embedded systems division at Mentor, a Siemens business. “The security, from a software perspective, is something they’re just now starting to realize that they need to do.” This is especially true in the wake of the Mirai attack. The number of connected devices is expected to reach 20.4 billion by 2020, according to Gartner.


Was BadRabbit a distraction? Malware 'used to cover up smaller phishing attacks'

Ransomware attack
"There is an open, let's say instantly obvious attack, while underneath there is a hidden, fairly well-thought-out attack, to which nobody pays attention," police chief Serhiy Demedyuk told attendees while speaking at the Reuters Cyber Security Summit in Kiev. "During these attacks, we repeatedly detected more powerful, quiet attacks that were aimed at obtaining financial and confidential information." He said the so-called "hybrid attack" – meaning a multi-pronged assault – was also found to be targeting users of a popular form of Russian accounting software called 1C. "The main theory we're working on now is that they [the hackers in both attacks] were one and the same," Demedyuk added. "The goal was to get remote and undetected access."


The Internet of Things is about much more than just connecting devices

The connected nature towards which we are migrating will allow manufacturers to better understand what their customers require on a real-time basis. This in turn enables the manufacturer to recalibrate not only the actual manufacturing part of the business and what they procure, but also to become highly competitive, super in-tune with what their customer requirements are, down to quality requirements per customer. That transparency will drive product improvement and customer satisfaction to new levels. Manufacturers will not order more raw material than they need. Think about latency and how this will be addressed. Consider this example: a customer wants a product; there’s the procurement of materials, import, export, shipping, logistics, manufacturing – it can take up to six months or more.


7 habits of highly effective digital transformations

7 habits of highly effective digital transformations
The collaborative efforts have paid off. “As a result of sharing practices, we have identified cases where we see a common failure mode in our continuous integration, delivery and operational practices — and then we are able to propagate the fix across all teams and improve and correct across all teams,’’ Fairweather says. Management also conducted a survey of its strategic foundational technology program. Fairweather recalls one comment an employee gave as feedback: “Instead of being a cog in the wheel I’m a better-informed contributor. The best part of learning from peers is gaining new contacts. We are more united as global organization in pursuing these 10 areas because we had done this.’’ ... As organizations get larger, different groups can begin to cut themselves off from one another, creating silos of information, he says.


6 Steps Up: From Zero to Data Science for the Enterprise

Different stakeholders have different views about the desire for a Customer360, but perhaps the most clarifying is that for a company to truly drive value and delight its customers, the business must understand those customers and approach every question from their perspective. Without a Customer360 built on a foundation of data science, the business will only ever have a qualitative view of customers. I believe a true, quantitative understanding of customers relies on rigorous data science. Less attention has been paid to the concept of a Product360, but it's no less important. Depending on the business, a Product360 can potentially drive more value through cost savings and cost avoidance than the business can derive from new revenue. The ultimate goal of a Product360 is creating assets that allow the business to explore each product from earliest inception through the end of its lifecycle.




Quote for the day:

"Instinct is intelligence incapable of self-consciousness." -- John Sterling


Daily Tech Digest - November 05, 2017

The end of the cloud is coming

An internet powered largely by client-server protocols (like HTTP) and security based on trust in a central authority (like TLS), is flawed and causes problems that are fundamentally either really hard or impossible to solve. It’s time to look for something better — a model where nobody else is storing your personal data, large media files are spread across the entire network, and the whole system is entirely peer-to-peer and serverless (and I don’t mean “serverless” in the cloud-hosted sense here, I mean literally no servers). ... Peer-to-peer web technologies are aiming to replace the building blocks of the web we know with protocols and strategies that solve most of the problems I’ve outlined above. Their goal is a completely distributed, permanent, redundant data storage, where each participating client in the network is storing copies of some of the data available in it.

Europe’s businesses leaving workers behind in the technology skills race
Human-centred interactions between people and machines have profound implications on the design of products and services. No longer do consumers need to command machines using a graphical interface: voice interfaces such as Alexa, Siri and Cortana etc. have changed that. Next, the emphasis will shift from understanding the meaning to interpreting intent. For example, in Toyota’s Concept-i car instead of commanding its virtual AI assistant, Yui, to turn the AC up, Yui will be able to understand intent in statements like “I’m feeling a bit cold.” It isn’t necessary to look into the future to see this trend. Already data-driven products are taking on board the emotional reactions of their users. For that reason, the best data-driven services don’t exhaust the user with endless data-gathering questions: Apple Music asks new users to “Tell us what you’re into” and presents a few bubbles containing genres to select.


Blockchain Aims to Foster Payer, Provider Trust for Value-Based Care

Blockchain and value-based care
Value-based care has accelerated the need for seamless data sharing in an environment that is both transparent and unquestionably trustworthy – one that can bring payers and providers together to improve quality, reduce costs, and enhance the patient experience. While stakeholders have offered up plenty of potential solutions for creating a free-flowing data environment that can support the complex environment of pay-for-performance reimbursements, blockchain may be the methodology that ticks the most boxes with a relatively low amount of effort. At Hashed Health, an industry consortium dedicated to applying blockchain to real-world use cases, CEO John Bass believes that the distributed ledger approach offers a number of promising improvements to the way providers, payers, and patients collaborate in a value-based world.


Future of Digital Currency May Not Involve Blockchains

Future of Digital Currency May Not Involve Blockchains
The problem with cryptocurrencies conceived before Bitcoin was their centralized structure. Without Blockchain technology, there was no “decentralized, immutable, transparent” ledger in which transactions could be recorded, leading to a centralization. Yet it looks like Blockchain may not be the be-all, end-all of digital currency technologies. Recently, a new form of crypto has emerged that leverages the Directed Acyclic Graph (DAG) organizational model for the structure of its decentralized ledger, allowing old problems to be solved and new features to be added. Today, we’re going to take a look at the technology that can potentially replace the Blockchain itself and some of its current implementations. Although the implementations that we are going to discuss today are new, the concept is not.


Do More With Machine Learning Thanks To These 6 Open Source Tools

Machine Learning Open-Source Tools
One problem the industry is seeing, however, is that there’s a severe lack of developers and new talent. It’s a problem for the entire development and programming industry, not just machine learning. Many companies and brands are vying for new employees, leaving the startups and newer names in a bit of hot water. Luckily, this can be offset by adopting open-source development protocols. More importantly, you can open your projects — future and present — to an even broader development community and audience by making it open-source. Open-source tools allow anyone to contribute to a project and work on fixes for bugs, new features and new builds. You can retcon separate versions, selecting the content and elements you want in an official release. This way, even though there’s a development community behind the project, you still have a great deal of control over the central project path.


The road to artificial intelligence in mobility—smart moves required

This overall interest in what AI could enable in automotive and mobility technology leads to a considerable willingness to pay for those features. Of the consumers who indicated high interest in AD features (24 percent of those surveyed), 46 percent are willing to pay more than $4,000 for autonomous-driving features on their next car. And AD features are so important to consumers that 65 percent would switch OEMs for better AD functionality; that figure exceeds 90 percent for young consumers and those living in large cities. Expectations are high, though, and may need to be tempered. On average, consumers expect full autonomy to be widespread in about five years—a tight timeline for any player, and for regulators. Machine learning will have a significant impact on the automotive and mobility industry, since it will unlock entirely new products and value pools and not just lead to productivity improvements.


Blockchain’s explosive growth pushes job skills demand to No. 2 spot

FinTech - financial technology - blockchain network - distributed ledger wireframe
It's not hard to imagine blockchain as a "disruptive skill" that is both fast-growing and hard to find, according to Burning Glass Technologies. While the technology and hiring patterns are in their early stages, it might be a good idea for employers to start figuring out where they will find blockchain talent, "even as they are still considering how the technology will change their business. "Because of its connection with 'cryptocurrencies,' blockchain is associated with finance, and major banks like Liberty Mutual, Capital One and Bank of America have posted openings," Burning Glass Technologies said in its blog. "There are also companies devoted to building blockchain applications, like Consensys Corporation. But the demand for blockchain is much broader, including major consulting firms like Accenture and Deloitte and technology companies like IBM and SAP. ..."


There's a Lot More to AI Than Just Chatbots

There's a Lot More to AI Than Just Chatbots
Options, where the AI uses data to create a model, but does not integrate with the DMP, are okay and will deliver enhanced business results. But they will never be as powerful as a truly integrated system. Artificial intelligence perceives its environment and makes decisions which will maximize its chance of success at any given goal. This could range from optimizing profit margin, to maximizing stock efficiencies. For example, a supermarket will want to ensure it always has enough salad in stock to supply its customers, while making sure there is minimal wastage and minimal unsold product. A good AI system can take that supermarket's typical sales into account, but should also be linked to weather information, so if there is a freak heatwave in October, the weather, and not just October's average salad sales, will be considered.


Microservices Interaction and Governance model - Orchestration v Choreography


In order to understand the options for managing microservice interaction, we should first study its history. Let’s look back to a time that is almost a decade before microservices really took off. In the early 2000s, the book Enterprise Integration Patterns was published. The corresponding web site for EIP remains an important reference for service interaction even to this day. Workflow engines were a popular option back in the days of Service Oriented Architecture, Business Process Management, and the Enterprise Service Bus. The promise at that time was that you could create orchestration style APIs without needing to employ a fully trained engineer. They are still around but there isn’t much interest in this option for microservice interaction anymore, primarily because they could not deliver on that promise.


The Fear of Disruption Can Be More Damaging than Actual Disruption


The automotive industry is at the start of just such a period. Massive changes appear to be inevitable: connected cars, autonomous vehicles, battery breakthroughs, and the like. But these changes will probably take decades to be fully adopted. The vehicles themselves have been in development for years now, and their potential impact has been analyzed extensively through computer models. Many critical factors will slow down their adoption. These include technical factors, such as the difficulty of designing vehicles for a wide variety of terrains and climate conditions. Incumbent automakers have built up fundamental advantages in design, manufacturing, distribution, sales, and financing, making it hard for new entrants to compete. All manufacturers, old and new, will need time to ramp up so they can produce the necessary technologies at scale. The transition will also require new types of auto repair shops, new fleet-management companies with new sources of capital for financing them, new forms of auto insurance, and new traffic and safety regulations.



Quote for the day:


"Don't wait. The time will never be just right." -- Napoleon Hill


Daily Tech Digest - November 04, 2017

Into the Core of REST


To uncover the hidden nature of Representational state transfer style, let’s dissect its name backwards. The word transfer implies that there are at least two processes communicating through some medium which implies a distributed system. The word state means that one process of a distributed system transfers its internal "view of the (surrounding) world" to another process. This ‘internal view of the world" is all the relevant information required by the process to do its duty (see Figure 1). It contains both information gathered from the environment and the one generated internally and is expressed by nouns. The word representational means that processes do not literally send their "internal views of the world" but encode them into descriptions (representations) understandable by recipients. Representation hides the internal nature (implementation) of the processes internal state.




There are no written test cases for the above test types since they are techniques that are based on the experience of each tester to test the system. However, one certainty is that we often write test cases for test types called functional testing and smoke testing in which we apply the test case design techniques, such as equivalent partitioning, boundary analysis, constraint analysis, state transition and condition combination, to design test cases. ... We combined all type tests, such as exploratory testing/ad-hoc testing, error guessing, stochastic testing, functional testing and smoke testing, during the testing phase to make sure we had maximum test coverage. ... We cannot apply automation testing for AI since it is just useful for stable systems with written test cases. Whereas AI behaviors are very complicated and random, so AI testing is more suited for manual execution.


C# BAD PRACTICES: Learn How to Make a Good Code by Bad Example – Part 5

This article is about Open Closed Principle (OCP) in SOLID principles and you don't have to read my previous articles to understand it, but I encourage you to do so. :) My motivation of writing this article is the fact that there is a huge confusion around this principle and many different interpretations of it. This principle was confusing to myself as well and that's why I went deep inside this topic and will present my finding and thoughts about it here. In my opinion, it is besides the Liskov Substitution Principle, the most difficult one (to fully understand) from SOLID. From my experience, I can say that it is confusing, even senior engineers and most developers know only a definition of it without a deep understanding of why and when it is useful. This may lead to blindly applying this rule which can make the code base bizarre.


AI: How big a deal is Google's latest AlphaGo breakthrough?


"AlphaGo is an interesting computer science accomplishment, this is algorithm development. [But] I don't think it is necessarily a big meaningful step," he said. "It does allow you to explore a whole bunch of things, related AI algorithms, what are called reinforcement AI algorithms and so on, in that sense it does contribute to the whole thing. "But when it comes to real-world applications in enterprises, I'm not sure AlphaGo makes by itself a significant difference." From Microsoft's perspective, he says that pursuing research that will make it easier for people to chat to computers using text or speech will really transform what's possible with AI. "Really solving every language in every kind of context, being able to create conversational applications and doing so really well, I think that's an incredibly important part of AI innovation, because no matter what, the vast majority of high-value interactions in this world happen using language."


Microsoft quietly announces end of last free Windows 10 upgrade offer

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Part of the stated justification for the original exception was the fact that Microsoft was still working on accessibility options for Windows 10, with a specific call-out to changes scheduled to arrive as part of the July 2016 Anniversary Update. There have been two feature updates since then, and the Anniversary Update is now the oldest supported Windows 10 version on the market. ... Corporations that have planned their upgrades to Windows 10 aren't making budgets based on this loophole. Individuals and small businesses that have said no to the upgrade for more than two years are hanging on to the original operating system on older hardware by choice. One practical question is whether Microsoft plans to tighten its activation code and start rejecting the automatic issuance of a digital license for Windows 10 when upgrading from Windows 7 or Windows 8.1 on older hardware.


11 Simple Java Performance Tuning Tips

Most developers expect that performance optimization is a complicated topic that requires a lot of experience and knowledge. Okay, that’s not entirely wrong. Optimizing an application to get the best performance possible isn’t an easy task. But that doesn’t mean that you can’t do anything if you haven’t acquired that knowledge. There are several easy to follow recommendations and best practices which help you to create a well-performing application. Most of these recommendations are Java-specific. But there are also several language-independent ones, which you can apply to all applications and programming languages. Let’s talk about some of these generic ones before we get to the Java-specific performance tuning tips.


Car Autonomy Levels Explained


The levels of autonomy are a progression of self-driving features that engineering experts SAE International have outlined. These levels range from no self-driving features at all through fully-autonomous driving. ... It's important to note that today, right now, the highest level of autonomy available to us is Level 3—not full autonomy, or even high autonomy, no matter what marketing materials or other automotive publications say. No autonomous car currently exists that can be trusted with the full autonomy of dynamic driving tasks. Audi AI can take over sometimes, under certain conditions, but even Audi AI requires the driver to take over once the system's limitations are exceeded. Audi has correctly dialed back its earlier claims that "The driver no longer needs to monitor the car permanently." Even the press release we criticized last July no longer contains this misleading statement.


The biggest headache in machine learning? Cleaning dirty data off the spreadsheets

“There's the joke that 80 percent of data science is cleaning the data and 20 percent is complaining about cleaning the data,” Kaggle founder and CEO Anthony Goldbloom told The Verge over email. “In reality, it really varies. But data cleaning is a much higher proportion of data science than an outsider would expect. Actually training models is typically a relatively small proportion (less than 10 percent) of what a machine learner or data scientist does.” Kaggle itself is intended to help. The site is best known for its competitions, where companies posts a specific data-related challenge and then pay the person who comes up with the best solution. And this means Kaggle has also become a repository of interesting datasets that users can play around with. These range from a collection of 22,000 graded high school essays to CT scans for lung cancer to a whole lot of pictures of fish.


Why security in microservices continues to fall short

The microservices world has made things very complex for security individuals in organizations. But it's also made it very difficult for QA testing and DevOps [teams] because it has taken some of the complexity and pushed it down to a DevOps space that didn't exist before. So to me, when people talked about security from an API or a microservices perspective, very often, what they're focusing on is the security of the container or the configuration management tool. So the guys are talking about something about Chef's container configuration management tool or Tenable's patch management tool for those containers as well. All of that is great. But what they're not focusing on is the fact that the way the software is being developed itself is completely different. So, let me give you a few examples of how software development and QA processes haven't caught up to deal with the microservices world.


Asset & Wealth Management Revolution: Embracing Exponential Change 

The AWM industry is a digital technology laggard. Technology advances will drive quantum change across the value chain – including new client acquisition, customisation of investment advice, research and portfolio management, middle and back office processes, distribution and client engagement. How well firms embrace technology will help to determine which prosper in the years ahead. Technology giants will enter the sector, flexing their data analytics and distribution muscle. The race is on ... Things will look very different in five to ten years’ time. Fewer firms will manage far more assets significantly more cheaply. Technology will be vital across the business. And, some firms will have discovered new opportunities to create alpha, and restore margins. With change accelerating, all firms must decide how they will compete in tomorrow’s world.



Quote for the day:



"Most successful entrepreneurs are trying to solve real-world problems that they encountered over years of working for someone else." -- Dan Simon


Daily Tech Digest - November 03, 2017

artificial intelligence / machine learning / binary code / virtual brain
Transforming the organization into a cognitive enterprise will be an arduous task and an evolutionary process. Jobs will not disappear overnight, and many organizations will outright fail to leverage the power of this technology — and will suffer the business consequences as a result. This lack of inevitability is because there are two significant problems when it comes to leveraging machine learning in the enterprise: data and bias. Machine learning only works with data. Lots and lots of data. It’s called machine learning because the machine must be ‘taught’ by giving it data from which it can distill patterns, and, in most cases, the teaching data must be ‘clean’ — meaning that it must be accurate and represent the desired outcomes. This reality means that for machine learning to work, an organization must begin with lots and lots of good, clean data. 


How to select the best self-service BI tool for your business
If most of your data is on Azure, you might want to rule out BI systems that run only on Amazon Web Services, and vice versa. If possible, you want the data and the analysis to be collocated for performance reasons. Vendors tend to cite analyst reports that are most favorable to their product. Don't trust the vendor's skimmed abstract or take the diagram they show you at face value: Ask for and read the whole report, which will mention cautions and weaknesses as well as strengths and features. Also take the fact of inclusion in an analyst's report with a large grain of salt: ... Some BI platforms now use in-memory databases and parallelism to accelerate queries. In the future, you may see more highly parallelized GPU-based databases built into BI services — third parties are building these, demonstrating impressive speedups.


Where is my data!? Why GDPR is good for Mainframes

The implications for the mainframe and GDPR are vast. The increased use of mobile devices alone are driving exponential growth in transaction volumes, and that data contains massive amounts of PII. This personal data is spread across the organization, widely used, transformed and accessed in different ways by different people, meaning application-based controls are not enough for complying with the regulation. The key first step toward achieving GDPR compliance for mainframe data is beginning with the identification and classification of the data, and determining which data contains PII information. Based on that classification, you will have a view of what personal data is being stored and where, and therefore a view at the levels of risk in your organization. If personal data is circulating outside the assigned channels and flows, it’s important to understand why and assess the associated risk to that data.


Tapping into big data’s potential

Tapping into big data’s potential
With big data you have different aspects, and there is relevance to how central banks deal with the data in general. When you look into the responses to the survey, they clearly show that, although it is unstructured data as far as the research is concerned, it could be structured and voluminous for other purposes – such as the credit register. I think there is a question about what the data is used for, and not so much the size or the structured versus unstructured demarcation. ... Firstly, there are those who say big data is primarily the type of unstructured data the private sector is dealing with. According to a recent BIS review, central banks are clearly interested too, for example, in looking at internet searches for nowcasting. A second area that is really key for central banks is in dealing with very large administrative and financial datasets. It is not simply because it is large that makes it big data, but because it is large and complex.


Facebook's plan to throw humans at security, ... equates to indictment on AI progress

For Facebook, the crisis isn't due to Russians tinkering with election sentiment. The crisis for Facebook is trust. You are the product. If you don't trust Facebook's information you may not engage as much. Facebook needs you to pass along information. The fact that there is shock -- shock I tell you -- over how Facebook can be used to manipulate the masses is almost comical. After all, those tools are the same reason marketers are fueling Facebook's financial dominance over the ad industry. But this rant isn't an indictment of social media lemmings or Facebook's controls or approach to ads. The Facebook conference call -- and Zuckerberg's solution to double headcount on security and throw humans at the fake news and trust issue -- is really an indictment on its AI prowess. Facebook simply doesn't have the tools or AI engineering to automate its way out of its mess.


Stratis: Blockchain-as-a-Service (BaaS)


Stratis is a flexible and powerful Blockchain Development Platform designed for the needs of real-world financial services businesses and other organizations that want to access the benefits of Blockchain technologies without the overheads inherent in running their own network infrastructure. ... Stratis is designed with the integration of fiat gateways in mind from the outset. It allows financial organizations to use the blockchain for the transfer of existing currencies that are both readily accepted by mainstream consumers and are not subject to damaging volatility: tokens of value that are simply digital equivalents of regular money. This ‘best of both worlds’ approach means that businesses can maintain compliance in whatever way they see fit, according to jurisdiction and organisational policy, while simultaneously using the blockchain as a store of value


The Future of Cybersecurity Part II: The Need for Automation

istock 166419812
Threats are evolving so quickly on the black hat side that the only way to combat them is through automated and intelligent defense layers that can quickly identify new and existing threats and then make decisions to mitigate them. I call this type of cybersecurity defense “actionable intelligence.” It requires deploying interconnected security solutions everywhere across your expanded network, including deep into the cloud, The goal is to create a security solution that is able to see and identify the stages of a threat and then make a decision on its own. Such an expert system is able to identify and block attacks at network speeds so that we don’t have to rely on humans, who often miss too much and respond far too slowly, to take action. This may require rethinking – and even retooling – your security infrastructure. To start, devices need to be able to see each other and share threat intelligence.


Data lake and data warehouse – know the difference

If you’re still struggling with the notion of a data lake, then maybe the following analogy will clarify matters. Think of a data mart or data warehouse as a storage facility rife with cases of bottled water. Those cases didn’t just magically appear overnight. People and machines gathered and purified the water. After packaging it, only then was it ready for people to buy and drink. By comparison, think of a data lake as a large body of natural water that you would only drink if you were dying of thirst. If you need 50 gallons of water to put out a fire, you don’t need to buy cases of bottled water and empty them out one by one. It’s all there, ready to go. In keeping with this analogy, the “water” in a data lake flows from many places: rivers, tributaries and waterfalls. That is, the data lake doesn’t hold only one type of water (that is, data). Data lakes can house all types of data: structured, semistructured and unstructured.


Blockchain Technology and The Changing Global Economy at the Ethereal Summit

Ethereal Summit 3
There are many parallels between the adoption of blockchain technology in emerging markets and the mainstream adoption of telecommunication in the 21st century. Instead of using phone lines, developing countries utilized newer technology and developed their infrastructure using satellite wireless communication. By "piggybacking" on the cell technology of developed countries, developing countries were able to incorporate new technology in an efficient and cost-effective way. Similarly, countries with fewer established financial systems are taking advantage of decentralized financial institutions powered by blockchain technology instead of establishing traditional banks. Although implementation speeds will vary by country, blockchain technology has the potential to empower all markets, including those looking for a technological piggyback.


What Is "Cloud-native" Data and Why Does It Matter?


Be aware that in cloud-native systems, the unified log often becomes the system of record. Materialized views show you a representation of that data for a given purpose. This is different way of thinking of data storage, and for many, turns the idea of a database inside out! The unified log holds individual transactions from your various inputs. Those items may inflate into objects or records in your applications or cache. This may be a new way for you to store data, but it’s proven to be an excellent solution at scale. That said, you don't have to throw out your trusty relational database. Instead, reassess how you use it. For example, if you've been using your relational database for application session state, consider introducing something like Redis and get familiar with key-value stores. At the same time, introduce modern relational databases like Google Cloud Spanner that are designed for geographic resilience and cloud-scale performance on demand.



Quote for the day:


"If you are filled with pride then you'll have no room for wisdom." -- African Proverb