February 06, 2022
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
An organization incurs technical debt whenever it cedes its rights and perquisites as a customer to a cloud service provider. To get a feel for how this works in practice, consider the case of a hypothetical SaaS cloud subscriber. The subscriber incurs technical debt when it customizes the software or redesigns its core IT and business processes to take advantage of features or functions that are specific to the cloud provider’s platform (for example, Salesforce’s, Marketo’s, Oracle’s, etc.). This is fine for people who work in sales and marketing, as well as for analysts who focus on sales and marketing. But what about everybody else? Can the organization make its SaaS data available to high-level decision-makers and to the other interested consumers dispersed across its core business function areas? Can it contextualize this SaaS data with data generated across its core function areas? Is the organization taking steps to preserve historical SaaS data. In short: What is the opportunity cost of the SaaS model and its convenience? What steps must the organization take to offset this opportunity cost?
Breaches grew rapidly in 2021, noted Lucas Budman, founder and CEO of?TruU, a multifactor authentication company in Palo Alto, Calif. “We exceeded the number of breach events in 2020 by the third quarter of 2021,” he told TechNewsWorld. A number of factors have been contributing to that increase, he added. “The ever-increasing sophistication of threat actors, a greater number of connected IoT devices, and the protracted shortage of skilled security talent all play a role in increased breach activity,” he said. Budman also maintained that Covid-19 has contributed to growing data breach numbers. “Data shows that the surge in remote and hybrid work and other factors resulting from the Covid-19 pandemic have fueled the rise of cybercrime by 600 percent or more,” he said. ... “Since an exceedingly large percentage of attacks focus on the end-user, this move to remote has proven very fruitful for attackers,” he told TechNewsWorld. “Similarly,” he continued, “the pandemic has dramatically changed the way goods and services are manufactured, dispatched and consumed. ...”
Biases including cognitive bias, incomplete data, flaws in the algorithm, etc, slow down the growth of AI in an organisation. Research and development play an important role in addressing these issues. Who understands this better than ethicists, social scientists, and experts? Therefore, businesses should include such experts in their AI projects across applications. Data architects also play a key role in governing AI products. Companies should have a complete pipeline of data or metadata for AI modelling. Remember, AI’s success depends on a well-sorted data architecture that is error and noise-free. To do so, data standardisation, data governance, and business analytics are a must. HR plays a key role in shaping the AI governance function. For instance, they should find candidates who “fit” into the organisation’s existing AI framework and create training material for the existing workforce to help them understand how to create ethical AI applications. Ensuring AI products don’t cross any legal boundaries is critical for smooth deployment. AI solutions meet the stipulated compliance guidelines of the organisation and the industry in which the organisation operates.
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An architecture of the enterprise is a carefully designed structure of a business or company entrepreneurial economic activity. One can easily assert that these entrepreneurial or economic activities include people, processes, and systems working in harmony to yield important business outcomes. These structures include organizational design, operational processes producing value, and the systems used by people during the execution of their mission. Enterprise architects use the business-prescribed operational end-state (results of value) to guide (like a blueprint) the enterprise to accomplish its mission—frequently, the end-states include vision, goals, objectives, and capabilities. Can a business exchange goods and services without technology and survive? Of course not. ... The enterprise architecture is neither the business architecture (operational viewpoint) nor the system architecture (technical viewpoint)—rather, the enterprise architecture is both architectures created in an integrated form, using a standardized method of design, and usable and consumable by both operational and technology people.
Enterprises that don’t plan ahead to move an application off a specific cloud but are forced to do so at some future point will also become losers. There is a lot of cost and risk involved in modifying applications to remove specific cloud native services and replace them with other cloud native services or open services. Clearly, this is the dreaded “vendor lock in.” Most applications that move to cloud platforms won’t ever move off that platform during the life of the application, mostly due to the costs and risks involved. Another drawback is that you’ll need cloud specific skills to take full advantage of cloud native features. This talent may not be available in-house or in the general labor pool, and/or it could drive staffing costs over the budget. The pandemic drove a massive rush to public cloud providers, which meant the demand for cloud migration skills exploded as well, driving up salaries and consulting fees. Moreover, the scarcity of qualified skills increases the risk that you won’t find the skills needed for cloud native systems builds, and/or the required level of talent will be unavailable to create optimized and efficient systems.
While big data focuses on the huge volumes of information that individuals and consumers produce for businesses to look at and AI programs to sift through, small data is made up of far more accessible bite-sized chunks of information that humans can interpret to gain actionable insights. While big data can be a hindrance to small businesses due to its unstructured nature, masses of required storage space, and oftentimes the necessity of being held in SQL servers, small data holds plenty of appeal in that it can arrive ready to sort with no need for merging tables. It can also be stored on a local PC or database for ease of access. However, as it is generally stored within a company, it’s essential that businesses utilize the appropriate levels of cybersecurity to protect the privacy of their customers and to keep their confidential data safe. Maxim Manturov, head of investment research at Freedom Finance Europe has identified Palo Alto as a leading firm for businesses looking to protect their small data centrally. “Its security ecosystem includes the Prisma cloud security platform and the Cortex artificial intelligence AI-based threat detection platform,” Manturov notes.