By Puneet Gupta, Vice President and Managing Director, NetApp India/SAARC Propelled by the evolving trends in data, data virtualization is emerging as a new-age avenue, revolutionizing the way businesses leverage their data assets. The global market for this disruptive technology is poised to take a steep growth curve, with projections estimating a value of USD 12878.39 million by 2028, with a whopping CAGR of 24.88% during 2022–2028. This underscores the immense significance of data virtualization, particularly for India, where it presents a promising opportunity to maximize the efficiency of enterprise AI ecosystems. As outlined by NetApp’s 2024 Cloud Complexity report, 70% of surveyed companies in India already have AI projects up and running or in motion, which is commendably higher than the global average of 49%. Given this increasing readiness to adopt AI models and projects, data virtualization could be the ticket for Indian industries to optimize operations, making them more flexible and scalable than ever before. Essentially, this technology offers the abstraction of data from its physical confines, facilitating seamless access and utilization across the enterprise. Legacy IT infrastructure often grapples with the demands of modern-day business operations. The significance of this advancement lies in its ability to transcend the constraints of conventional data management approaches, offering agility, scalability, and efficiency in managing extensive and diverse datasets. Within AI ecosystems, it proves to be crucial in optimizing access to critical data and expediting the development and deployment of AI-driven solutions.

Advantages of Data Virtualization

In today's hyper-competitive business landscape, rapid modernization is the key to staying ahead of the curve. Virtualization empowers corporations to unlock a wealth of new opportunities and drive competitiveness through enhanced decision-making and accelerated time-to-market. By furnishing real-time access to actionable insights, it equips businesses to make informed decisions and capitalize on budding trends and emergent opportunities. Among the many advantages that data virtualization offers, a significant one is its ability to optimize resource utilization. By consolidating virtual environments, organizations can realize considerable cost savings whilst simultaneously enhancing operational efficiency. This not only mitigates the complexity of IT infrastructure but also augments scalability, enabling businesses to swiftly adapt to changing demands and market dynamics. In the world of enterprise AI, agility is crucial. By facilitating rapid deployment of such solutions, it allows businesses to capitalize on emerging opportunities and respond swiftly to evolving customer needs. Its inherent flexibility enables businesses to adapt their AI strategies in real-time, ensuring maximum impact and value creation. Centralized management and monitoring capabilities are also essential for effective data governance and control. Simplifying IT operations by providing a unified platform for managing and monitoring data assets is yet another benefit observed. This streamlined approach not only reduces administrative overhead but also enhances visibility and compliance, ensuring data integrity and security across the corporation. Access to timely and accurate data is the lifeblood of AI-driven decision-making. Through this innovation, access to critical data can be accelerated, enabling organizations to derive actionable insights with unmatched speed and accuracy. By breaking down data silos and facilitating seamless integration, it empowers businesses to make informed decisions that drive growth and improvement. It is well-founded that digital transformation thrives on experimentation and iteration. Data virtualization fosters a culture of innovation within AI ecosystems by providing a platform for rapid prototyping and testing. Its flexible architecture enables data scientists and AI developers to explore new ideas and concepts, leading to the development of ground-breaking solutions that drive business value and competitive advantage.

The Future of Data

As we embrace the future facilitated by the adoption of enterprise AI, the strategic importance of data virtualization cannot be overstated. By leveraging this technology, businesses can streamline operations, drive efficiency, and unlock new opportunities for growth and competitiveness. Looking ahead, the evolving role of this innovation will continue to shape the future of AI, providing companies with the tools they need to stay ahead of the curve and thrive in the digital age. Disclaimer: The views and opinions expressed in this guest post are solely those of the author(s) and do not necessarily reflect the official policy or position of The Cyber Express. Any content provided by the author is of their opinion and is not intended to malign any religion, ethnic group, club, organization, company, individual, or anyone or anything. 

By Lakshmi Mittra, SVP and Head, Clover Academy In the rapidly changing and dynamic tech environment of today, future-proofing the workforce is more essential than ever. With industries constantly innovating and adapting to new technologies, the demand for next-gen tech talent professionals capable of leading change and driving innovation is on the rise. This is where skilling steps in, acting as a key player in nurturing the next generation of tech talent. The concept of future-proofing the workforce revolves around equipping employees with the necessary skills and knowledge to adapt to new technologies and industry trends. With rapid advancements in technology, traditional job roles are evolving, and new roles are emerging. Therefore, it is essential for organizations to invest in continuous learning and development to ensure their workforce remains relevant and competitive.

The Role of Skilling in Cultivating Next-gen Tech Talent

Skilling plays a pivotal role in nurturing the next-gen tech talent through its tailored learning paths and hands-on experience. It offers industry-relevant courses and collaborates with experts to ensure up-to-date and practical training. Here’s how skilling equips learners to meet the demands of the evolving tech landscape and drive innovation:

Tailored Learning Paths

One of the key strengths of skilling is its ability to offer tailored learning paths that cater to the unique needs and aspirations of each learner. Whether it's data science, artificial intelligence, cybersecurity, or software development, skilling provides a range of courses and programs designed to develop the specific skills required in today's tech-driven world.

Hands-on Experience:

Skilling emphasizes hands-on learning, allowing learners to gain practical experience and apply their skills in real-world scenarios. Through projects, case studies, and practical assignments, learners not only acquire theoretical knowledge but also develop problem-solving and critical thinking skills essential for success in the tech industry.

Industry Collaboration

Skilling collaborates with industry leaders and experts to develop up-to-date and relevant content that is aligned with industry standards and practices.

Fostering Innovation and Growth

By empowering learners with hands-on and industry-relevant training, skilling promotes a culture of continuous learning. It provides learners with the tools and resources to explore and develop creative solutions, cultivating a workforce capable of driving innovation and sustainable growth.

Enhanced Employability

Skilling enhances the employability of learners by equipping them with industry-relevant skillsets and knowledge. This increased employability not only benefits the learners by opening up new career opportunities but also provides organizations with access to a pool of skilled and qualified talent.

Conclusion

Future-proofing your workforce is essential in today's rapidly evolving tech landscape. It benefits not only the employees but also provides organizations with a competitive edge by ensuring they have a skilled and adaptable workforce capable of driving innovation and growth. In this digital age, skilling is not just about acquiring new skills, but fostering a culture of continuous learning, adaptability, and achieving sustainable growth. Disclaimer: The views and opinions expressed in this guest post are solely those of the author(s) and do not necessarily reflect the official policy or position of The Cyber Express. Any content provided by the author is of their opinion and is not intended to malign any religion, ethnic group, club, organization, company, individual, or anyone or anything. 

By Nathan Wenzler, Chief Security Strategist, Tenable India is ranked third globally among nations facing the most severe cyber threats, as per the World Economic Forum. However, despite this alarming statistic, there exists a significant disparity between the escalating volume of threats and the resources allocated to combat them. The cybersecurity sector is grappling with a colossal skills deficit, with a shortage of 4 million professionals worldwide. Even seasoned cybersecurity experts find it daunting to navigate and decipher the increasingly intricate landscape of modern cyber threats across the ever-widening attack surface due to limited resources.

Role of Generative AI in Enhancing Cybersecurity Strategy

In response to this challenge, organizations are turning towards generative AI to bridge the expertise gap and enhance their resilience against risks. A survey reveals that 44% of IT and cyber leaders express high levels of confidence in the capacity of generative AI to enhance their organization’s cybersecurity strategy. Security teams are increasingly consumed by the arduous task of scrutinizing various attack vectors in their systems and analyzing the tactics, techniques, and procedures employed by potential threat actors. Often, they find themselves reacting to cyberattacks post-incident, rather than proactively thwarting them—a strategy far from ideal for robust cybersecurity. Organizations in India must shift towards a proactive stance, actively pursuing and understanding threats to establish a robust line of defense. The expanding attack surface, coupled with the rapid adoption of cloud services, virtualization platforms, microservices, applications, and code libraries has added immense complexity to the security landscape. Organizations now must contend with vulnerabilities, cloud misconfigurations, and risks associated with identity access, groups, and permissions. Conventional attack path analysis tools offer insights into threat actor entry points, which assets are key targets, and what threats may exist but this can demand painstaking manual effort to decipher implications step-by-step. While attackers require just one entry point to infiltrate and laterally move within a system, defenders face the formidable task of analyzing the entire threat landscape all at once, identifying all potential attack paths, and implementing security measures in the places that can mitigate the most risk, especially when operating with limited staff.

Empowering Security Teams with Generative AI

Generative AI emerges as a potent solution to these challenges, empowering security teams by providing them with the perspective of attackers to map out potential threats and prioritize mitigation strategies based on criticality. By consolidating data from disparate sources, generative AI offers an easier way to understand the complexity of the attack surface, enabling organizations to more quickly assess exposures, prioritize actions, and visualize relationships across the entire attack surface. This means security teams can make risk decisions more quickly, leaving less time for an attacker to take advantage of an exposed asset and begin their assault on the organization. Generative AI-powered attack path analysis amalgamates and distills insights from vulnerability management, cloud security, web application, and identity exposures, enabling organizations to comprehend their risk from the perspective of an attacker. This facilitates informed and targeted cyber defense strategies, allowing organizations to anticipate threats and fortify their defenses accordingly. Through succinct summaries and mitigation guidelines, generative AI equips security teams with a quicker and more efficient view of actionable insights, sparing them the tedious task of manually researching what the threats are and what the correct security controls should be, whether that’s identifying specific patches or version numbers or understanding how to correct unauthorized user access. Even team members with varying levels of expertise can draw actionable conclusions from generative AI, simplifying complex cyberattack paths and enabling effective threat mitigation. In summary, generative AI supports a more comprehensive and proactive approach to cybersecurity, empowering organizations to understand and address potential threats quickly. By breaking free from the constraints of siloed security data, organizations can develop strategies to predict, prevent, and mitigate cyber risks effectively and faster than ever before. Disclaimer: The views and opinions expressed in this guest post are solely those of the author(s) and do not necessarily reflect the official policy or position of The Cyber Express. Any content provided by the author is of their opinion and is not intended to malign any religion, ethnic group, club, organization, company, individual, or anyone or anything.