September 29, 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
We’re starting to see an amplification of recognition tools, of coaching platforms, of new and exciting ways to learn that are leveraging mobility and looking at how people want to work and to meet them where they are, rather than saying, “Here’s the technology, learn how to use it.” It’s more about, “Hey, we’re learning how you want to work and we’re learning how you want to grow, and we’ll meet you there.” We’re really seeing an uptake in the HR tech space of tools that acknowledge the humaneness underneath the technology itself. ... The second layer consists of the business applications we’ve come to know and love. Those include HR apps, business applications, supply chain applications, and financial applications, et cetera. Certainly, there is a major role in this distributed work environment for virtual application delivery and better security. We need to access those mission-critical apps remotely and have them perform the same way whether they’re virtual, local, or software as a service (SaaS) apps -- all through a trusted access security layer.
It hardly needs to be said, but over the last few decades, the internet has grown to be arguably the most integral cog ensuring a smooth-running, functioning society. It is so ingrained that almost every industry in the world would be unable to function properly without it. And this reliance will only grow as Web 3 becomes the norm, which makes it critical that we begin to educate children now on its uses and how to navigate it. Already, many of today’s adults will find it difficult to explain what Web 3 is, let alone teach the next generation how to use it. Educating children early will not only help them thrive in the future, but they will also be able to pass gained knowledge up the chain to their parents. This is, of course, just history repeating itself. It is the equivalent of kids showing their parents how to use a touch screen or work their email. But the revolution Web3 is about to bring is on a different scale to any previous technological advancement. Soon, the greatest opportunities will be solely available on the new internet, and it is critical we ensure every child has the opportunity to succeed.
Without appropriate data governance procedures and training in place, healthcare organizations are likely to find themselves in danger of noncompliance. HIPAA violations in particular can occur at any level of an organization; if an undertrained staff member or unsecured database is operating in your organization, there’s a huge likelihood that they will eventually misuse patient data and breach HIPAA regulations. This kind of breach can lead to noncompliance, fines, legal issues, poorer patient experiences and even a loss of trust within the greater medical community. Data governance means the difference between a successful and fully operational facility and a facility that gets shut down by the government. On the other hand, when data governance principles are applied successfully in the healthcare sector, a slew of benefits outside of basic compliance can be realized. Patients feel confident that their information is safe and begin to refer their friends and family members to your network. Data becomes easier to find, label and organize for new operational use cases and emerging patient technologies.?
Nowadays, the increasing need for security on all fronts has fueled collaboration between teams on a regular basis. This, in turn, has spurred more proactivity from an internal IT operations perspective. Proactivity, bolstered by a unified view into traffic and communication, is a key aspect of closing the gap between cloud complexity and performance — because it starts at the IT cultural level. Technical capabilities like deep observability can support team prioritization of detection and management on a more holistic level, addressing all aspects of IT infrastructure. With this, organizations can feel more confident in overcoming cloud-based challenges and mitigating connected cyber vulnerabilities as a collective force. An all-encompassing, proactive approach is needed to speedily detect cyber threats, respond to the corresponding activity, and enact a remediation plan. Within hybrid and multi-cloud environments, data and communication costs can skyrocket. The most common use cases stem from packets, which can interfere with control of and visibility into the right data.?
Blockchain is an innovative digital information storage system storing data in an encrypted, distributed ledger format. In work, the data is encrypted and distributed across multiple computers, which creates tamper-proof. It is a secure database that can only be read and updated by those with permission. There are a few examples on the web today of blockchain and artificial intelligence being interconnected. Academics and scientists conducted the study. But we see the two concepts working well together. ... Today’s computers are extremely fast, but they also require a constant supply of data and instructions, without which it is impossible to process information or perform tasks. Therefore, the blockchain used on standard computers requires significant computing power because of the encryption processes. Secure data monetization could be the result of combining blockchain and artificial intelligence. Monetization of collected is a source of revenue for many companies. Among the big and famous ones are Facebook and Google resources.
Among the many reasons why there are a growing number of vendors in the sector, a significant one is because building and deploying ML models is often a complicated process with many manual steps. A primary goal of MLops tools is to help automate the process of building and deploying models. While automation is important, it only solves part of the complexity. A key challenge for artificial intelligence (AI) models, that was identified in a recently released Gartner report, is that approximately only half of AI models actually end up making it into production. From Guttmann’s perspective, with application development, developers tend to have a linear way of building things. This implies that for example, new code written six months after the initial development is better than the original. That same view does not tend to work with machine learning as the process involves more research and more experimentation to determine what actually works best. “Development is always money sunk into the problem until you actually see the fruits of the effort and we want to decrease that development time to a minimum,” he said.