AI Adoption Influences Next-gen Digital Infrastructure & Cloud Security: AI TechBytes with Microland’s Sunil Sarat

Next-generation digital infrastructure refers to the latest technology and systems used to support and manage digital services, like websites, apps, and data storage. It involves advanced equipment and software that make these services faster, more reliable, and easier to use.

Cloud security is like a protective shield for digital information stored and accessed over the internet. It keeps data safe from hackers, viruses, and other cyber threats. It involves tools and protocols to ensure that only authorized users can access and modify data and that it remains confidential and available when needed.

Microland pioneers next-gen digital infrastructure and cloud security solutions. Leveraging cutting-edge technology, Microland ensures robust protection and seamless operations for businesses worldwide. Their expertise spans cloud management, network security, and data privacy. From implementing scalable cloud architectures to fortifying cyber defenses, Microland offers tailor-made solutions for diverse needs.

Today, AiThority.com’s AI Techbytes interview features Sunil Sarat, SVP and Global Head of Cloud, Applications & Digital Workplace Services at Microland. Sunil explains AI’s role in cloud security and infrastructure management. We feel that his views would benefit enterprise cloud leaders, CIOs, and CISOs.

Here’s the full interview with Sunil.

Hi Sunil, welcome to our AiThority Interview Series. Please tell us about your journey in the industry and how you have embraced AI in your role.

Thank you, and it’s truly an honor to be featured in your interview series. I began my journey in the tech world as a Unix System administrator in the late ’90s and have since traversed various roles within the IT industry. Over the years, I’ve been involved in and led diverse teams, including delivery, practice, presales, solutions, and innovation.

With a comprehensive background in IT infrastructure services—encompassing Cloud, Data Center, Digital Workplace Services, Networking & Security. I’ve spent over 23 years with Microland. Currently, I hold the position of Global Head of Cloud & Data Center, Digital Workplace & Application Services Portfolio.

My engagement with AI commenced a couple of years ago. Staying abreast of industry developments, I certified myself on Microsoft Azure AI and ensured my teams followed suit. I actively seek out AI use cases, both for our clients and internally within our organization. My advice regarding AI is to start small and scale gradually. Given the constant evolution of AI, staying informed is crucial. Effectively embracing AI can provide executive leadership with a strategic advantage, fostering innovation, enhancing efficiency, and uncovering new business opportunities.

In addition to my responsibilities, I’ve co-authored books, contributed to whitepapers, blogs, and articles, and regularly speak at conferences and events. I’m passionate about driving positive change through technology.

How can CIOs stay abreast of the latest in cloud security and infrastructure management tools? How does Microland empower CIOs and other IT officers in this field?

This is indeed an insightful question, and while there may not be a one-size-fits-all solution, a proactive commitment to continuous learning and training is paramount. Conferences serve as invaluable platforms, facilitating engagement with peers for practical insights and diverse perspectives. Active involvement with industry associations such as CSA, NIST, and OWASP is crucial. Additionally, maintaining close ties with vendor partners ensures staying abreast of their product roadmaps, new features, and security enhancements.

Practical exposure is unparalleled in the learning process. Establishing an innovation lab for hands-on exploration of new products and technologies is a recommended practice. By adopting a proactive stance towards learning, collaboration, and industry trends, CIOs can adeptly navigate the dynamic landscape of cloud security and infrastructure.

Over the past three decades, Microland has been an invaluable partner to CIOs, offering extensive support in the realm of IT infrastructure and security. A few ways that we support include:

Deep Technological Expertise: Our dedicated practice boasts profound knowledge of emerging technologies, encompassing the latest strides in infrastructure (cloud, AI, IoT etc) and security (threat detection, incident response, compliance). CIOs can leverage this expertise to make informed decisions without the need for extensive internal development.
Strategic Guidance: As strategic partners, we provide valuable insights into technology roadmaps, investment decisions, and long-term planning, assisting CIOs in aligning their strategies with the rapidly evolving tech landscape.
Rapid Technology Adoption: We aid CIOs in swiftly adopting new technologies by offering expertise, implementation services, and ongoing support. This empowers them to seize new opportunities and maintain a competitive edge.

Could you provide your thoughts on the Next-gen Digital infrastructure, encompassing applications, hyper-cloud, distributed networks, endpoints, and IIoT?

Next-generation digital infrastructure refers to the transformative technologies and architecture that underpin the digital operations of organizations. It signifies a departure from traditional IT infrastructure models towards more agile, scalable, and efficient solutions, harnessing cutting-edge technologies as mentioned in your question.

Applications: NextGen infrastructure embraces microservices architecture, containerization, and Serverless Computing, fostering greater flexibility, scalability, and efficiency in deploying and managing applications.

Distributed Cloud: Organizations are increasingly adopting multi-cloud and hybrid cloud strategies to evade vendor lock-in, enhance resilience, and optimize costs. Hyper-cloud extends beyond centralized data centers, incorporating edge/fog computing, particularly advantageous for latency-sensitive applications and services.

Distributed Networks: NextGen infrastructure integrates SD-WAN to optimize network performance, improve connectivity, and enhance security. The rise of 5G technology is facilitating the growth of IoT and edge computing.

Endpoints: The proliferation of Internet of Things (IoT) devices and wearables is a significant contributor to NextGen infrastructure.

IIoT (Industrial Internet of Things): Industry 4.0 Integration – NextGen infrastructure in industrial settings involves the integration of IIoT devices and sensors to enable predictive maintenance, optimize operations, and enhance overall efficiency.

Cybersecurity: Utilizes the Zero Trust Security Model and AI-driven security.

In addition to these key components, Next-gen infrastructure also encompasses DevOps, CI/CD, Infrastructure as Code, and Data Management practices, including the implementation of Data Lakes.

Cloud and data center solutions have evolved dramatically in the last 2 years. Could you tell us about the contemporary and futuristic solutions that disrupt these industries?

AI

AI is on the verge of making a profound impact on the industry. As AI adoption grows, there is a corresponding surge in demand for infrastructure from both Hyper-scalers and Data Centers. The adoption of distributed multi-cloud models allows organizations to efficiently meet the data and compute requirements necessary for training AI and machine learning models at scale. AIOps integration into Cloud and Data Center Operations is becoming the new normal, and Generative AI is poised to play a key role in IT support. The convergence of AI, Generative AI, and ML into IT infrastructure is a discernible trend, enabling businesses to automate tasks, make data-driven decisions, and enhance personalized customer experiences. According to Gartner, “by 2026, more than 80% of enterprises are projected to have utilized generative artificial intelligence (GenAI) application programming interfaces (APIs) or models and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023.”

Edge Computing

Edge Computing brings data processing closer to the source, reducing latency and improving performance for real-time applications. Organizations are anticipated to invest in edge computing, driven by the growth of IoT and 5G. The emergence of Edge AI is also a trend to watch, with the challenge being the maintenance of consistent communication and data integrity between edge devices and central cloud infrastructure.

Cybersecurity

With the emergence of AI and Gen AI, LLM trained on organizations’ private security data, coupled with zero-trust security, is expected to become standard practice.

Cloud Governance & Compliance

In addition to existing statutory, legal, and regulatory compliance, the critical aspect of Governance & Compliance is amplified with new regulations drafted for AI, Gen AI, and modern computing technologies.

Industry Cloud Platforms

Industry Cloud platforms, tailored to the specific needs of a particular industry or vertical, are gaining prominence. These platforms provide a pre-configured and integrated environment, including cloud infrastructure, industry-specific software, and services. Examples include Salesforce Health Cloud and Microsoft D365 Retail.

Quantum Computing

While still in its infancy, advancements in quantum computing are anticipated to impact both cloud and data center solutions, particularly in cryptography, optimization problems, and complex simulations.

FinOps

The trend of adopting FinOps practices is likely to persist, with more organizations leveraging these practices to achieve cost efficiency, accurately allocate expenses, and drive financial accountability across cloud operations.

What are the true performance benchmarks to measure the effectiveness and quality of AI-assisted business operations?

This realm is continuously evolving, and the absence of “true performance benchmarks” necessitates a tailored approach. The choice of specific benchmarks depends on the industry, business objectives, and the unique characteristics of the AI solution. Consideration of various performance benchmarks includes:

Prediction Accuracy: Assessing the accuracy of predictions made by the AI solution.
Precision and Recall: Evaluating the precision of positive predictions and the recall of actual positive instances.
Processing Time: Measuring the time taken by the system to process data and provide outputs.
Throughput: Evaluating the system’s capacity to handle and process a specific volume of data.
Performance Under Load: Gauging the system’s efficiency and effectiveness when subjected to high-demand scenarios.
Resource Utilization: Assessing how efficiently system resources are utilized during operation.
Error Handling: Examining the solution’s ability to identify and manage errors or unexpected inputs.
User Satisfaction: Capturing user feedback and overall satisfaction with the AI solution.
User Experience: Assessing the overall experience of users interacting with the AI system.
ROI (Return on Investment): Evaluating the financial returns and benefits derived from the AI implementation.
Bias and Fairness: Ensuring fairness and impartiality in the outcomes generated by the AI solution.
Resilience Against Attacks: Assessing the system’s ability to withstand and recover from security threats or attacks.
Compliance: Ensuring adherence to relevant regulatory and legal standards.
Adaptability to Change: Assessing how well the AI solution adapts to evolving circumstances and requirements.
Learning Curve: Understanding the ease with which users can understand and operate the AI solution.

When using generative tools, are there security & privacy risks we should be aware of?

The use of generative tools introduces significant security and privacy risks, including:

Deepfakes and Synthetic Media: Generative tools can craft highly convincing deepfakes, portraying individuals engaging in actions or making statements they never did.
Fake News and Propaganda: These tools can generate deceptive content, leading to the creation of fake news articles and social media posts that appear legitimate, contributing to misinformation and propaganda.
Data Leaks and Breaches: Due to the substantial training data required by generative tools, there’s a risk of including sensitive personal information. This data could potentially be leaked, leading to privacy breaches.
Unauthorized Use of Personal Information: Generative tools can be exploited to generate personalized content, such as fake emails or social media profiles, enabling impersonation and unauthorized use of personal information.
Algorithmic Bias: If trained on biased datasets, generative tools can perpetuate existing societal biases, potentially leading to biased outputs.
Targeted Attacks: Adversaries may utilize generative tools to craft personalized attacks against individuals or specific groups, posing a targeted threat.
Manipulation of Output: There’s a risk of adversaries manipulating generative models by providing meticulously crafted inputs to generate specific, potentially malicious outputs, compromising the integrity of the generated content.

Being vigilant about these security and privacy risks is crucial when incorporating generative tools to mitigate potential harm and safeguard against misuse

Which applications, software, or tools are indispensable for you?

This again, is a great question. In my daily toolkit, I rely on a mix of essential tools and platforms that cater to various aspects of my professional life. To start with we have the usual suspects such as Outlook for email, teams for collaboration/conference calls, office productivity suites including Word, Excel, and PowerPoint, our one-stop shop mobile app for employees MicrolandOne, our business dashboards and strategy planning/mind map tools.

Being a techie, I have an IDE (visual studio) on my laptop, access to cloud repositories, access to various cloud portals for my learning / playing around with the latest technologies, access to GenAI tools like chatGPT, bard, online e-learning platforms and various advisor / analyst portals.

What is the future trajectory for AI/Machine Learning and other intelligent technologies post-2024?

Predicting the precise future trajectory of AI, machine learning, and other intelligent technologies beyond 2024 poses a challenge due to the dynamic nature of the field. However, we can outline a few directional trends:

Fusion of Technologies: AI/ML is set to converge and collaborate with emerging technologies like robotics, quantum computing, and the Internet of Things (IoT). This synergy is poised to create even more powerful and intelligent systems.
Explainable AI (XAI): Establishing trust and comprehending how AI systems make decisions will be paramount. The focus on explainability is expected to rise significantly in the development of AI systems.
Advancements in Deep Learning: Continuous strides in deep learning techniques, architectures, and algorithms are anticipated. This progress will lead to the creation of more potent and efficient models across diverse tasks.
AI Ethics and Responsible AI: The ethical dimensions of AI development will gain prominence, encompassing considerations such as fairness, accountability, transparency, and bias mitigation. The industry is likely to witness the establishment and adoption of standards and guidelines for responsible AI practices.
Edge AI and Federated Learning: The trend towards more AI processing and model training on edge devices will intensify. This shift aims to reduce the necessity for extensive data transfer to centralized servers. Additionally, federated learning will gain traction, enabling models to be trained across decentralized devices.
Natural Language Processing (NLP) Advancements: Striking improvements in NLP are on the horizon. These advancements will result in more sophisticated language models and applications, fostering enhanced language understanding and generation capabilities.

While the exact future remains uncertain, these directional trends indicate a trajectory towards increasingly integrated, ethical, and efficient AI technologies.

How do you use AI and automation in your daily workflows? Could you highlight a few practical benefits of using AI ML and automation in product-centric companies?

E-mail filtering and sorting, using virtual assistants such as Siri, home automation, document categorization, tagging and searching, using AI-powered browsers for content discovery

As for the second question regarding product-centric companies, the practical benefits of employing AI, ML, and automation are substantial:

Faster Time to Market: Employing AI-powered design tools accelerates prototyping and iteration, expediting the product development lifecycle.
Enhanced Product Quality: Leveraging AI for code testing and bug detection contributes to higher product quality and reliability.
Data-driven Decision Making: Utilizing ML models to analyze customer data and feedback facilitates informed decision-making in optimizing product features and predicting market trends.
Personalized Marketing Campaigns: Applying AI for customer segmentation based on preferences ensures personalized and targeted marketing campaigns.
Enhanced Customer Service: Implementing chatbots and virtual assistants provides instant and personalized customer support, enhancing overall customer service efficiency.

What are your core offerings? What is your vision for the company and the industry in general?

We “Make Digital Happen” for our clients allowing technology to do more and intrude less. We make it easier for enterprises to adopt nextGen Digital infrastructure and our offerings are as follows

Digital Networks: Transforming and Managing Software-Defined Networks with Automation, Analytics, and AIOps to enhance and secure user experience in the “new normal”
Cloud & Apps: Maximizing the potential of the Cloud by Modernizing Apps and delivering Platform-First Modern Cloud Operations across Distributed & Hybrid Clouds
Digital Workplace: Providing a productive, secure, and collaborative hybrid work environment with a commitment towards stability and effective strategic growth for the Digital Workforce
Cybersecurity: With a “Cyber Resilient First” approach, we enable clients protect and quickly recover against security risks in the current dynamic threat landscape while ensuring zero impact to businesses
Industrial IoT: Smart and secure IoT solutions with industry-specific accelerators that deliver actionable data, real-time monitoring, and predictive analytics to improve business outcomes while optimizing costs

My vision for the industry in general revolves around the harmonious fusion of Next-Gen Infrastructure and AI, propelling us into an era of unparalleled innovation and efficiency. I envision a future where the convergence of Next-Gen Infrastructure and AI becomes the catalyst for transformative progress, opening doors to endless possibilities and pushing the boundaries of what technology can achieve

Thank you, Sunil! That was fun and we hope to see you back on AiThority.com TechBytes interview soon.

[To share your insights with us as part of the editorial and sponsored content packages, please write to [email protected]]

About Sunil
About Microland

About Sunil

As the SVP & Global Head of Cloud, Data Center & Applications at Microland, I lead the vision, strategy, and execution of the company’s offerings and practices in these emerging technology areas. With over 25 years of experience in the IT industry, I have a strong perspective on the market trends, customer needs, and competitive landscape, and I leverage this knowledge to create value-added solutions and services for our clients.

About Microland

Microland’s commitment to “Making Digital Happen” allows technology to do more and intrude less. We make it easier for enterprises to transition to nextGen digital infrastructure through our extensive service portfolio including Cloud and Data centers, Networks, Digital Workplace, Cybersecurity, and Industrial IoT. We ensure that the embrace of digital services is predictable, reliable, and stable.

Microland is making digital happen for enterprises with a laser focus on services that are more relevant to our clients and prospects than ever before, guaranteeing business outcomes. Incorporated in 1989 and headquartered in Bengaluru, India, Microland has more than 4,600 digital specialists across offices and delivery centers in Asia, Australia, Europe, the Middle East, and North America.

The post AI Adoption Influences Next-gen Digital Infrastructure & Cloud Security: AI TechBytes with Microland’s Sunil Sarat appeared first on AiThority.