IT departments are facing a growing challenge to stay abreast of advancements in cloud technologies, provide day-to-day support for increasingly complex systems, and adhere to ever-changing regulatory requirements. In addition, they must ensure the systems they support are able to scale to meet performance objectives and are secured against unauthorized access. ... Much like data security, adhering to regulatory compliance frameworks is a shared responsibility between the customer and cloud provider. Larger cloud vendors will provide third-party auditor compliance reports and attestations for the regulatory frameworks they support. It will be up to each organization to read the documentation and ensure the contents meet specific compliance needs. Most leading platforms will also provide tools to help clients configure identity and access management, secure and monitor their data, and implement audit trails. But the responsibility for ensuring the tools' configuration and usage meet the framework's control objectives relies solely with the customer. ... We know one of IT's core responsibilities is to transform raw data into actionable insights.
We create new-to-the-world machines, with sophisticated specifications, that are hugely capable. But to reach their potential, we have to expose them to hundreds of thousands of training examples for every single task. They just don’t ‘get’ things like humans do. One way to get machines to learn more naturally, is to help them to learn from limited data. We can use generative adversarial networks (GANs) to create new examples from a small core of training data rather than having to capture every situation in the real world. It is ‘adversarial’ because one neural network is pitted against another to generate new synthetic data. Then there’s synthetic data rendering – using gaming engines or computer graphics to render new scenarios. Finally, there are algorithmic techniques such as Domain Adaption which involves transferable knowledge (using data in summer that you have collected in winter, for example) or Few Shot Learning, which making predictions from a limited number of samples. Taking a different limited-data route is multi-task Learning, where commonalities and differences are exploited to solve multiple tasks simultaneously.
Whatever you call it, it’s an important attribute to consider when hiring or grooming the most capable IT professionals today. A continuous learner can offer more bang for the buck in one of the strongest job markets in recent years. “We have found that many companies, while their job descriptions state they are looking for a certain number of years of experience in a laundry list of technologies, are being more flexible and hiring candidates that may be more junior, or those who lack a few main technologies,” Spathis says, noting that many organizations are willing to take the risk on more junior or less specifically experienced candidates who are eager, trainable, and able to learn new skills. There’s definite agreement on the demand for continuous learners in the IT function today. “To thrive during these changing times, it’s imperative that IT organizations continuously grow and change with changing needs,” says Dr. Sunni Lampasso, executive coach and founder of Shaping Success. “As a result, IT organizations that employ continuous learners are better equipped to navigate the changing work world and meet changing demands.”
AI technology is changing the working process of software engineers and test engineers. It is promoting productivity, quality, and speed. Businesses use AI algorithms to improve everything from project planning and estimation to quality testing and the user experience. Application development continues to evolve in its sophistication, while the business increasingly expects solutions to be delivered faster than ever. Most of the time, organizations have to deal with challenging problems like errors, defects, and other complexities while developing complex software. Development and Testing teams no longer have the luxury of time when monthly product launches were the gold standard. Instead, today’s enterprises demand weekly releases and updates that trickle in even more frequently. This is where self-coded applications come into play. Applications that generate the code themselves help the programmers accomplish a task in less time and increase their programming ability. Artificial intelligence is the result of coding, but now coding is the result of Artificial intelligence. It is now helping almost every sector of the business and coders to enhance the software development process.
Before even thinking about making the transition, one has to be very clear about what a data scientist does and introspect what has to be done to fill the gaps that are needed to make the transition and the skills the person has now. A data scientist not only handles data but provides much deeper insights from it. Other than gaining the right mathematical and statistical know-how, training yourself to look at business problems with the mindset of a data scientist and not just like a data analyst will be of great help. This means that while looking into a problem, developing your critical thinking and analytical skills, getting deep into the problem to be solved at hand, and coming up with the right way to approach the solution will train you for the future. A data analyst might not have great coding skills but surely has to know it well. Data scientists use tools like R and Python to derive interpretations from the massive data sets they handle. As a data analyst, if you are not great at coding or don’t know the common tools, it would be wise to start taking basic courses on them and use them then in real-world applications.
First, an ASM has to understand what a supervised project is about. This is especially important for agile development, where, unlike the waterfall model, you don’t have two months to perform a pre-release review. An АSМ’s job is to make sure that the requirements set at the design stage are correctly interpreted by the team, properly adopted in the architecture, are generally feasible, and will not cause serious technical problems in the future. Typically, the ASM is the main person who reads, interprets, and assesses automated reports and third-party audits. ... Second, an ASM should know about various domains, including development processes and information security principles. Hard skills are also important because it’s very difficult to assess the results provided by narrow specialists and automated tools if you can’t read the code and don’t understand how vulnerabilities can be exploited. When a code analysis or penetration test reveals a critical vulnerability, it’s quite common for developers (who are also committed to creating a secure system) to not accept the results and claim that auditors failed to exploit the vulnerability.
WhiteSource detects all vulnerable open source components, including transitive dependencies, in more than 200 programming languages. It matches reported vulnerabilities to the open source libraries in code, reducing the number of alerts. With more than 270 million open source components and 13 billion files, its vulnerability database continuously monitors multiple resources and a wide range of security advisories and issue trackers. WhiteSource is also a CVE Numbering Authority, which allows it to responsibly disclose new security vulnerabilities found through its own research. ... Black Duck software composition analysis (SCA) by Synopsys helps teams manage the security, quality, and license compliance risks that come from the use of open source and third-party code in applications and containers. It integrates with build tools like Maven and Gradle to track declared and transitive open source dependencies in applications’ built-in languages like Java and C#. It maps string, file, and directory information to the Black Duck KnowledgeBase to identify open source and third-party components in applications built using languages like C and C++.
No matter what anyone tells you, it’s not a zero-sum game. There is abundance out there for everyone. Of course, money becomes concentrated with various people, but wealth-mobility is very real and happening all the time. We hear people talk about the 1% all the time (often in an effort to paint them as a monolithic, evil, controlling class). What they fail to recognize is that people are constantly moving in and out of the 1% all the time. Some of this is down to inherited wealth, and some is down to hard work — but it’s happening all the time. What really lies at the heart of this is fear. We abdicate our power to an imagined ruling class because we’re afraid of the unknown. And before you think this is about blaming you: It is our subconscious being unwilling to take the risk that stops us. You have a built-in stowaway in your mind who wants to maintain a status quo. Therefore, any new growth opportunities — while intellectually exciting and appealing — will be met with emotional resistance at some point. I’m sure you’ve had this happen to you before: You get a new career-changing offer, you do a little dance and head off to celebrate.
For businesses, the combination of these factors has led to a big increase in corporate risk, putting significant pressure on any corporate investigations that need to be conducted and making the eDiscovery process much more difficult. Not only are employees and their devices a lot less accessible than they used to be, but the growing use of personal devices, many of which lack proper security protocols or use unsecured networks, leaves company data much more vulnerable to theft or loss. If that wasn’t enough, heightened privacy concerns and the likelihood that personal data will be unintentionally swept up in any eDiscovery processes can make employees even more reluctant to hand over their devices to investigators if/when needed (if investigators can even get hold of them). As a result, many companies are suddenly finding themselves between a rock and a hard place. How can they operate a more employee friendly hybrid working model while still maintaining the ability to carry out corporate investigations and eDiscovery in the event it’s required?
CIOs and IT executives should focus on three types of partner connections: one-to-one, one-to-many and many-to-many. A one-to-one connection can be taken to the next level and become a generative partnership where the enterprise and technology partner work together to create and build a solution that doesn’t currently exist. The resulting assets are co-owned and produce benefits and revenue for both partners. Generative partnerships are becoming more common. In fact, Gartner forecasts that generative-based IT spending will grow at 31% over the next five years. Beyond one-to-one connections is the formation of ecosystems of multiple partners. One-to-many partnerships work best when a single enterprise needs to focus many players on jointly solving a single problem – such as a city bringing together public and private entities to serve the citizen. Many-to-many partnerships are created when a platform brings many different enterprises’ products and services together, to be offered to many different customers. Often called platform business models, these marketplaces and app/API stores enable the many to help the many at ecosystem scale.
Quote for the day:
"Leaders are people who believe so passionately that they can seduce other people into sharing their dream." -- Warren G. Bennis