Craig Schleicher, who heads innovation for City National Bank in California, said he thinks the industry will see more automation in security. "As we move toward a lot more transactions being automated with triggers, we're going to see an evolution from card controls to much more dynamic and robust controls around individual transactions and what you permit without a human in the loop and what you don't. It's going to be a fun space to see evolve," he said. Schleicher said the concept of a financial institution's fiduciary relationship — doing everything in the best interest of the client — can be applied to how banks can help customers manage their identity. "We're seeing a lot of appetite for value-add services around identity theft protection and dark web monitoring," he said. "Some of these services started out with preventing financial fraud, but are now looking to protect their clients in other ways." Jig Patel, chief innovation officer for Fiserv's digital banking group, said it's imperative that fintechs and banks forge partnerships to combat security threats.
In many ways, disruptive technologies are like a travel adventure – a journey beyond “business as usual” to “business unusual and unexplored.” These technologies offer opportunities to go back to basics, reimagine processes in the context of today’s realities, and recreate satisfying customer and employee experiences. Silently and gradually, disruptive technologies – such as the Internet of Things (IoT), cloud platforms, analytics, robotic process automation (RPA), artificial intelligence (AI), and machine learning – have made it to the list of must-have technologies for most progressive and innovative organizations. With the cost of devices and storage falling, the variety of available protocols and technologies is deep, and the pool of experts is growing. However, the journey from initial experimentation to full deployment of disruptive solutions requires the ability to deal with the uncertainties of a complex enterprise application landscape.
Time-series data is broadly defined as a series of data stored in time order. Examples of time-series data can range from stock prices over a period of many years to CPU performance metrics from the past few hours. Time-series data is widely used across many industry verticals. It has carved out its own category of databases, because relational, document-oriented and streaming databases do not fulfill the needs of this particular type of data. ... A typical time-series database is usually built to only manage time-series data so one of the challenges it faces is with use cases that involve some sort of computation on top of time-series data. An example would be capturing a live video feed in a time-series database. If you were to apply some sort of an AI model for face recognition, you would have to extract the time-series data, apply some sort of data transformation and then do computation. This is not ideal for a real-time use case. Multi-model databases that also manage other data models solve for these use cases where multiple data models can be manipulated in place.
According to Forbes, with time the trust factor in the capabilities of blockchain is expected to rise. The real impact of a distributed ledger is still under speculation, but given the spurt of applications already crowding the markets, it is only a matter of time before blockchain penetrates every industry sector. This universality of blockchain can be compared to “all things digital,” which Gartner predicted in 2017, and within two years that prediction turned into a formidable reality. Something that could reduce the growth period for blockchain is the existing transactional-integrity features of cryptocurrency. In near future, critical data will reside on distributed data stores — combining on-premise, cloud, and remote facilities — and blockchain will emerge as a savior for transactional integrity. According to J. Christopher Giancarlo, Chairman of U.S. Commodity Futures Trading Commission, free markets foster “creativity and economic expression to promote human growth and advancement.” This assertion comes from the belief that “sustained prosperity” is a natural byproduct of “open and competitive markets, free of political interference, combined with free enterprise, personal choice, voluntary exchange and legal protection of person and property.”
The mission of the Architecture of the Enterprise remains crucial though. It has to integrate all disparate views and diagrams in the enterprise in one enterprise blueprint. Hence, the IT Enterprise and Business Architectures approaches need to be properly merged/ linked though so that they can deliver the entire blueprint of the enterprise. In addition, the enterprise level architects should also consider integrating all enterprise level activities that deliver process modelling, quality processes and products, non-IT schematics and engineering disciplines that ensure the trimming of the operation by measuring and adjusting the processes, and provide security, availability and scaling of the enterprise. To sum up, the top Architect of the Enterprise should operate higher up in the enterprise hierarchy to cover the business architecture and integrate it with the technology and people architecture. This architect should ensure that it is the full blueprint of the enterprise that it is delivered rather than the IT blueprint or solutions. The architect should make sure that the audience is the whole enterprise rather than IT.
The crux of the issue is this: although the GDPR sets out requirements relating to security – appropriate technical and organisational measures – it is not very prescriptive. The text is inherently legalistic and businesses are often left wondering how to apply the requirements. So, while a processor may be required to comply with the legal requirements, the processor’s view of what technical and organisational security measures are appropriate may differ from the controller’s own views. Likewise, where processors perform commoditised processing activities, they may not have sufficient knowledge of the personal data and how the controller uses it to assess the risks adequately. ... Clearly, if a processor is responsible for a security failure in breach of the GDPR, then the processor will have direct responsibility under the regulation.
If you have ever developed SOAP base Web services using WCF, you might have enjoyed using the client API codes generated by SvcUtil.exe or Web Service References of Visual Studio IDE. When moving to Web API, I felt that I had got back to the Stone Age, since I had to do a lot of data type checking at design time using my precious brain power while computers should have done the job. I had developed some RESTful Web services on top of IHttpHandler/IHttpModule in 2010 for some Web services that did not handle strongly typed data but arbitrary data like documents and streams. However, I have been getting more Web projects with complex business logic and data types, and I would utilize highly abstraction and semantic data types throughout SDLC. I see that ASP.NET Web API does support highly abstraction and strongly typed function prototypes through class ApiController, and ASP.NET MVC framework optionally provides nicely generated Help Page describing the API functions.
The enterprise security perimeter has all but dissolved, and business apps and data are increasingly dispersed across devices and networks that companies don’t own or control. Cybercriminals have jumped on this widespread disruption to take advantage of security gaps to launch all kinds of attacks, such as phishing, man-in-the-middle, device takeovers and more. In the past, security professionals were able to lock everything down behind a firewall, but now we can’t put the genie back in the bottle. Enterprise mobility is here to stay, and it’s up to every CIO to figure out how to make enterprise data and user privacy securely coexist on employee-owned devices. We need to address these challenges head-on because enterprise mobility and BYOD trends will only continue to expand rapidly around the globe. Worldwide, the BYOD and enterprise mobility market is projected to grow by $84 billion, driven by a compounded growth of 16.3%. So while it’s clear that mobile enterprise users aren’t going back to their old PC workstations any time soon, enterprise security strategies must catch up to the rapid evolution of modern mobility ASAP.
What is Bitcoin? A question with many answers. Digital gold, magic internet money, a hedge against macro risk, tulip mania? One thing is for certain, Bitcoin found a product–market fit as a new form of money owned by the people. The Bitcoin brand is well known around the globe, the userbase is growing fast, and it continues to attract developers to the ecosystem. However, Bitcoin is not a panacea. When Satoshi first launched Bitcoin, he made design choices that were optimal for becoming a hard money with a limited attack surface at the cost of base-layer scalability and an expressive scripting language. One of those major choices was to implement a distributed proof-of-work (PoW) system to form network consensus. In other words, Bitcoin is great at being money but not very good at all the other potential use cases for a blockchain. The lesson here is that design choices come with tradeoffs, and Bitcoin has already cemented its path. This leaves room open for alternative blockchain architectures to capture value in a different market — such as supply chain management, enterprise software, social media, voting, prediction markets and more.
Quote for the day:
"Ninety percent of leadership is the ability to communicate something people want." -- Dianne Feinstein