September 18, 2023
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
As the technology ecosystem expands, Servier Pharmaceuticals’ Yunger believes cultivating hard-to-find skill sets from within is instrumental to future-proofing the IT organization. The company, a Google Cloud Platform shop, came face-to-face with that reality when it became difficult to find specialists, shifting its emphasis to growing its own talent. Yunger takes a talent lifecycle management approach that considers the firm’s three- to five-year strategy, aligns it to the requisite IT skills, and then matches the plan to individualized development and training programs. “We provide our vision of the future to our existing team and give them an opportunity to self-select into those paths to meet our future needs,” he explains. “The better our long-term vision, the more time we have to give our team the chance to learn and grow.” The University of California, Riverside, which is undertaking a similar practice to nurture IT talent from within, makes a concerted effort to start any large-scale reskilling initiative with those most willing to embrace change.?
As fraudsters obtain more personal data and create more believable fake IDs, the accuracy of AI models improves, leading to more successful scams. The ease of creating believable identities enables fraudsters to scale identity-related scams with high success rates. Another key area where generative AI models can be employed by criminals is during various stages of the money laundering process, making detection and prevention more challenging. For instance, fake companies can be created to facilitate fund blending, while AI can simplify the generation of fake invoices and transaction records, making them more convincing. Furthermore, by bypassing KYC/CDD checks, it’s possible to create offshore accounts that hide the beneficial owners behind money laundering schemes. Generating false financial statements becomes effortless and AI can identify loopholes in legislation to facilitate cross-jurisdictional money movements.
The key to effectively integrating AI into your business lies in proactive engagement. Rather than being passive recipients of technological changes, businesses should take an active role in understanding AI's potential applications. Reflecting on prominent companies such as Kodak and Nokia, which once dominated their respective industries, but ultimately faltered due to their reluctance to adopt technological advancements, underscores the importance of embracing AI as a transformative force. Consider Netflix's evolution from mailing in DVDs to streaming and their use of AI algorithms to recommend personalized content to users. ... In the face of advancing AI technology, the role of leaders is not merely to keep up but to set the pace. By actively engaging with AI, embracing it as a partner, learning from mistakes, and strategically adapting our approach, we position ourselves to harness its potential to foster innovation and enable us to navigate the future with confidence.
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Firstly, the convergence of new core technologies like blockchain, digital twins, convergence, and virtual hospitals into the Metaverse will empower clinicians to offer more integrated treatment packages and programs. Secondly, using AR and VR technologies will enhance patient experiences and outcomes. Another benefit of the Metaverse for telemedicine is that it will facilitate collaboration among healthcare professionals. The ability to share information between healthcare professionals immediately will enable quicker pinpointing of the causes of illnesses. Moreover, the Metaverse will offer new opportunities to students and trainees to examine the human body in a safe, virtual reality educational environment. Surgeons are already using VR, AR, and AI technology to perform minimally-invasive surgeries, and the Metaverse opens up new frontiers in this area. Surgeons will be able to get a complete 360-degree view of a patient’s body, allowing them to better perform complex procedures using these immersive technologies.
Adaptive security systems employ continuous monitoring to gain real-time insights into an organization's network, applications, and endpoints. This continuous data collection allows for the rapid detection of abnormal behavior and potential threats. ... Understanding the context of an activity is crucial in adaptive security. Systems analyze not only the behavior of individual elements but also the relationships between them. This context-awareness helps in distinguishing between normal and malicious activities, reducing false positives. ... Adaptive security leverages machine learning and artificial intelligence (AI) algorithms to process vast amounts of data and identify patterns indicative of threats. These algorithms can adapt and evolve their detection capabilities based on new information and emerging attack vectors. ... Automation is a core element of adaptive security. When a potential threat is detected, adaptive security systems can automatically respond by isolating affected systems, blocking suspicious traffic, or alerting security teams for further investigation.?
As you catalog the tools in your organization, consider where most of your development takes place. Is it happening solely in notebooks requiring code knowledge? Are you versioning your work through a tool like Github, which is often confusing to a non-coding audience? How is documentation handled and maintained over time? Oftentimes, business stakeholders and consumers of the model are locked out of the development process because there is a lack of technical understanding and documentation. When work happens in a silo, hand-offs between teams can be inefficient and result in knowledge loss or even operational roadblocks. This leads to results that are not trusted, oreven worse, adoption of the outputs. Many organizations wait too long before leveraging business experts during the preparation and build stages of the AI lifecycle. ...? This might be because only some of the glued together infrastructure is understood by the business unit, the hand off between teams is clunky and poorly documented, or the steps aren’t clearly laid out in an understandable manner.