Hello everyone. I'm Prakesh Vanga. Overall, 13 years of exchange experience specializing in AWS cloud engineer and identity access management. Currently serving as an A WS data engineer, designing and implementing scalable and secure data pipelines, leveraging AWS services. Over the years, I had the opportunity to work across complex enterprise. Environments helping organization not only migrate to the cloud, but also optimize how they manage data securely, efficiently and intelligently. Today I'm truly excited to speak to you about a topic that close to both my technical experience and future vision. A driven integration revolutionizing data management in. Multicloud enterprise environments. In today's session, I would walk with how a power integration frameworks can transform enterprise operation by eliminating the complexity of working across multiple cloud platforms like AWS Azure GCP, improving visibility performance, and. Security and delivering measurable ROI, while ensuring organizations stay fully compliant with evolving regular standards. Whether you are in financial service, healthcare, our technology, the strategies and techniques will explode today, our designed to help you motorize your data infrastructure, reduce operational overhead, and gain a complete edge. In a rapidly evolving digital world. Let's get started. So the slide, multi-cloud reality, the integration challenge, let's start with the. Reality we face today, most enterprises don't operate in a single cloud environment anymore. They're using AWS for scalability, Azure for enterprise integration and GCP for AI services simultaneously. But with this flexibility comes complexity, we end corner four main challenges. First one is of fragmented data. And enterprise has moved to the cloud, many adaptive multi-cloud strategies leveraging AWS for scalability, Azure for enterprise integration, and GC GCP for a services. While these hybrid approaches provide flexibility, it also data ecosystems it's not helping much more. So what happened in practice is customer. Records might be stored in AWS S3, for example, applications log logs are log and index in Azure. Monitor and analytic data takes results in Google Cloud BigQuery. These environments don't talk to each other natively. Each uses different formats, different APIs. And next one, security vulnerability. Each cloud has different policies and protocols. This inconsistency introduces gaps between technology and also, let us tell one example, let us know address one of the most pressing issues in any multi-cloud architecture, security vulnerability caused by inconsistent policy enforcement across cloud providers. While each cloud being AWS Microsoft Azure or Google Cloud platform, GCP offers strong native secure controls. They are fundamentally different in design, configuration, and operation. This lack of standardization introduces significantly risk when organizations attempts to unify them under when one architecture, what causes these vulnerabilities? Different security models. Each cloud product has own IAM identity access management, AWS use IM roles and policies and Azure users. Role-based access control. GCP relies on IAM bindings and roles tied to resources hierarchies. These models are not. Interoperable and even small misconfigurations like or permission roles or inconsistent MFA policies can become entry point for attackers. Operational inefficient integration is often manual error, resources heavy and time consuming. Let's now examine a critical barrier to enterprise agility and perform in multi-cloud environments. Operational efficiency caused by manual integration. While the term may sound procedural, it impacts in strategic affecting productivity, scalability, and business continuity. So what is the manual integration? Manual integration refers to the non-automatic human driven process of connecting services, transferring data, and. Aligning workflow between cloud platform like AWS Azure and GCP compliance complexity. So for example, meeting HIPA Health Insurance Portability and Accountability Act are SOX Compliance in a multi-cloud setup requires deep expertise and constant monitoring. Having discussion, the operational security and data challenging in multi-cloud ecosystem, let's now turn to a domain that is equally critical compliance. But when your data and workloads are spread across AWS Azure and Google Cloud compliance becomes exponentially more complex. So why complex compliance is so challenging in multi-cloud? One is different regions, different rules. Each cloud provides source data in multiple regions. By default, if you don't configure storage correctly, your data might land in incorrect path where Stryker rules apply and you may not even know it. For example, I will take A-H-I-P-A Health Insurance Portability and Accountability Act mandates end-to-end encryption. And stick to access control for healthcare data, one misconfiguration can lead to regular breach and several financial penalties. And in consistent audit trail. Each cloud logs and tracks eventually differently. AWS VS Cloud trial. Azure has monitor and log analytics. GCP has cloud audit logs. Without integration, these logs become disjoint, making it extremely difficult to perform infra audits. Responses to incidents are pro compliance during external assessments, evolving regulations, so regulator requirements changes frequently, keeping up with updates across multiple compliance while aligning with multiple cloud provided configurations required constant manual efforts and. Specialized knowledge. This is not just a legal risk, it's a operational drain. We'll go to next slide. AI transformation automation integration, AI transform automat, automated data mapping. Let's now talk about how AI transforms integration, starting with one of the most powerful feature automated data mapping in a multi-cloud setup. Data exists in many formats, structures, and locations spread across platforms like AWS, Azure and GCP. Traditionally, connecting this data requires manual mapping, understanding source and target fields, writing transformational logic, ensuring data types and format align. This process is time consuming, prone to human error and needs. Constant updates whenever schema changes. So what does a do differently? So with automated data mapping, we use advanced machine learning algorithm that can scan and understand data sets, even if they come from completely different systems. Automatically detects relationship between fields. For example, it can recognize that customer ID in AWS. It matches to Azure customer number. If it don't match us, it'll cause the issue generally accurate. Mapping logic without requiring manual intervention, the results are impressive by using AI For this task, we are, we have seen 85% reduction in manual data mapping efforts, significantly improved accuracy, especially in large scale. Thousands of fields schemas. So fashion integration, timelines and fewer errors in production and intelligent data routing. So next, let's how transform that where data moves across multi-cloud environments with a concept called intel and data routing. In traditional systems, data flow are typically static. Once you define. A route from one cloud service to another. It remains fixed regardless of change in network traffic latency system performance. But in dynamic multi-cloud environments like AWS Azure and GCP network conditions can change every second. And a fixed road can easily become a slow or overloaded path, reducing performance. So what is intelligent data routing? So with a power routing, the system is no longer pass you our active learning and optimizing in real time here what it is. Analyzing network traffic conditions continuously, such as bandwidth availability, latency, and server loads, chooses the fastest, most efficient path for data to travel every time as a request is made. Instant. If something changes like traffic spike system, slow down this enterprise that your data reaches its destination faster, avoids bottleneck, and maintain consistent performance across all cloud platform. In real world, we see significant reduce reduction in latency, improve throughput, far high volume data applications, and much better system availability, especially during. Peak traffic and next governance enforcement. The final piece of a driven integration I would like to highlight is governance enforcement. A major leap forward is managing complaints across multi-cloud BroadB beds. Now, let's be honest, governance and complaints are often seen as complex, time consuming and constantly changing. Every organization must follow industry regulations like G-D-P-R-H-I-P-A, or SOCs, depending on your sector. But when you're working across multiple cloud platforms, AWS, Azure, GCP, keeping policies consistent, become extremely difficult. Each platform has different tools, different permission structures, different upgrade cycles. So manually mounting is not only slow, it is also risky. So what does AI do for governance? This is where AI powered policies, engines make a huge difference. These engines are self-learning, meaning they monitor evolving regulations and automatically apply the latest rule. They enforce compliance consistently across all cloud platform, no matter how complex your setup is. Companies that reduces the compliance monitoring workload by up to 70% real time visibility across cloud ecosystem. So issue detection, early problem identification with the ai. Now let's talk about one of the most powerful outcome of a driven integration, early problem detection in complex multi-cloud environments. Issue can arise at any time. A failed data transfer, a slowdown in response time, or even silent security misalignment. So traditionally, these problems are only discovered after they cause visible damage like an outage. CSLA or compliance were violations. So A helps a change the game by acting as 24 by seven intelligent monitoring systems. So here what it does, it constantly scans logs, data flow, and system behavior across all cloud platform. It uses patterns, recognization and detection to spot irregularities often before humans can notice them. It can even predict potential failures by learning from past system behavior. This proactive led to a 94% improvement in early issues detection, so resolution time, faster, troubleshooting with the ai, following on early detection. Let's talk about what happened next. Resolving the issue with traditional systems, even when a problem is detected, finding the root cause can take hours or even days. Teams must manually check logs, trace dependencies across cloud services, and coordinate between multiple platforms like AWS, Azure and GCP. This is slow, frustrating, and costly how a speeds are troubleshooting with a driven monitoring and diagnostics, the story is very different. A helps reduce troubleshooting time by seven six percentage. Automate root cause analysis. AI quickly pinpoints the source of problem, whether it is misconfigured permissions, a failing service or drop network package. Correlated log insights. It analyzes logs across all cloud simultaneously identifying patterns and depending this in seconds, small recommendations based on the historical data, AI can suggest the most effective resolution reducing. Guesswork and back and forth and operational efficiency, pushing it team productivity. With the ai, let's now look at one. One of the most exciting outcomes of AI driven integration is 3.5 x increase in operational efficiency. That means IT teams are getting more than three times the work, the with resources and less manual effort. In a traditional setup, your IT team is constantly switching between cloud dashboard, writing scripts, troubleshooting error, managing integrations, and trying to make sense of skater data. It's not just exhausting, it's inefficient, so a boost efficiency. With a power unified monitoring platforms, your team no longer have to chase problems across systems in shape. The benefit form single. Pan of glass visibil across AWS Azure and GCP. Maybe you can use single dashboard. No more jumping between dashboards. Everything is integrated into one intelligent interface, automatic alert systems in place. And hey, doesn't just tell that you there something is wrong in, tell what, where and how to fix it. And going Next slide. Implementation framework, the assessment phase. Let's now talk about how we begin implementing AI driven integration across a multi-cloud environments. The first and most important steps in the assessment phase, this is very, we lay the foundation for success by thoroughly understanding the current environment, aligning it with the business goals and identifying. Where AI can make the biggest impact. So the step one, current state analysis, the first thing we do is map out your existing cloud infrastructure. We identify which service are running AWS, Azure and GCP based on the cloud technology. Where there are performance gaps, T process are security vulnerabilities. This queues as clear picture of the baseline. What's working, what not, and where we can improve. Think of it as performing a health checks, whatever, multi-cloud architecture and step two, business requirement mapping. So next we align the technic technical side with the business goals. What outcomes are you trying to achieve? Is it faster taxes? Lower costs, or strong compliance? When then define clear key performance indicators and return on investments metrics to measure the success of integration initiatives. This ensures that the solution is not just technical sound, but business driven. And step three, finally, we look at how data flows across your cloud platform. How much data are you moving, how fast that is needs to travel. How complex are the transformation? This help us identifying high impact integrations, opportunities, places where a can boost speed, reduce error, and improve efficiency the most. It also helps prioritize what we integrate first for maximum business values. So I'm going to next slide. Strategic platform selection. Choose the right foundation for integration. Once we complete the assessment phase, the next key step is implementing AI driven integration is choosing the right platform. This is what we call strategic platform selections. Not all platforms are created equal, and the wrong choice can lead to login, poor performance, and endless customization. So we focus on four critical factors that defines. Further ready integration platform. So the first one is interoperability. First, we look at multi-cloud or your integration platform must work seamless with different cloud APIs, various data formats, and multiple communication protocols. This means you should be able to connect AWS Azures, GCP, and even on print systems without writing C code for each connection. Think of it like a universal translator that allows all of our cloud services to talk to each other smoothly and instantly has a capabilities. We want a platform that comes with built in machine learning tool for predictive integration, like sporting potential failures before they happen. Pat recognized across data sets and. Autonomous optimization of workflow. So the system keeps improving itself over time. This isn't just about automation, it's about in automation, and next will go scalability, then comes scalability as your business grows. So the volume of data, the number of users, and the complexity of your cloud services, your integration platform must handle this growth without slowing down. For business a requirement, a full architecture. It should adapt dynamically as your environmental evolves. Think like choosing a bridge that not only handled today traffic, but is ready for tomorrow. Rush showers and next vendor independence. Finally, we took for vendor independence. Your integration solutions should have flexible architecture that doesn't tie. You one cloud to other cloud, which ensure portability of our cloud. Easier migrations, our expansions, and better dealing with the cloud vendors. We'll go with next slide. Performance optimization techniques, making multi-cloud ran smarter and posture once the right integration platform is in place, the next step is performing optimization, making the next step. Multi-cloud environments not only works, but work faster, efficiently and intelligently. AI plays a major role here. We uses a combination of small technologies to boost systems performance by 40 to 60%, and I would like to work through the four key techniques we use. The first one is predictive resources allocation. The first technique is predictive. Resources allocation. So in this technique, a constantly analyzes system phase and forecast upcoming demand, whether it's a storage, compute, or bandwidth based on automatic, a automatically scales resources up or down in real time. This means you avoid performance button, like during peak traffic, and you also save cost during. Quite period, but not overing. Next is data filtering. Instead of trans transferring and processing all data, it determines what relevant and filter out unnecessary information before it moves between systems. This reduces network traffic storage we and data processing loads. Think if going through your inbox and only keeping the important emails automatically. And next one is then we integrated edge computing. This means the data is processed close to its source at the edge of the network, rather than sending everything back to the cloud. Benefits like faster response times reduces latency and improve performance for real time applications like retail transactions. And finally, we use adapt to compressions. A Dan Kelly compress data based on the content network conditions, based on the network conditions and priority level. This ensure faster transfer, especially across regions or providers, and helps optimize bandwidth usage. We get faster performance with less traffic without compromising our data. Going to next slide. Case study, financial services transformation. Let's look at the real world example. With a power integration in place, the transformation was dramatic. Integration time dropped by 87. Percentage when took weeks, now took just days or even ours, thanks to automated data mapping. And in routing data reduces by 86 percentage A identified. And corrected inconsistent in real time leading to clean and more reliable data complaints cost fell sharply. Self-learning policies, engines ensure that all systems stayed aligned with the regulations. Reducing manual effort and audit related stress system uptime improved significantly with prior to monitoring and intelligent troubleshooting out has become rare and recover. Was much faster. I will go with next slide. Emerging technologies, reshaping integration. As we look to the future, it's clear that a driven integration is just the beginning. A new view of emerging technologies is reshaping how we manage data systems and security across the cloud, and it happened faster than ever. Let me briefly walk you. Four powerful technologies that are revolutionary, the next generation of integration. The first one is autonomous operations. First, we have autonomous operations, what we often call self-healing system. Imagine a system that detects an error and it's won, identify the root cause, and fixes the issue automatically without waiting for human interventions. These systems uses AI to monitor operational in real time. Learn from past instance and adapt continuously. It's like having an IT team that never sleeps, never makes mistakes and resolution problems before users even notice. And next one. Second, we have edge computing, a powerful approach where data is processed at near their source instead of central cloud system. This is especially useful for smart retail. Real-time monitoring in healthcare are manufacturing next year is blockchain security in multi-cloud and hybrid environments. Trust and editable are critical. Blockchain provides immutable audit trials. Records that can be changed are added across cloud providers. This creators cross-platform trust for transactions and access better compliance tracking and stronger productions. Again, data. Data time bring manipulating. It brings transparency and integration to data workflow no matter where the data lies. And finally, we have cognitive integration, a concept that brings natural language understanding to cloud operations. Imagine your voice comments are plain English into managed cloud services. For example, show me all fail integrations five this morning, or. Secure all healthcare data with GDPR level encryption. This makes cloud management more accessible and user-friendly, even for non-technical stakeholders. Go to next slide. Implementation roadmap. A phase path of a driven integration. Now that we explored the status strategy, technologies and benefits of a. Power integration. The next big question is how do we put it all into actions? We follow a structure phase roadmap that ensure that transitions to a driven integration is smooth, measurable, and aligned with business priorities. Let me break down each phase of implementation roadmap. So the phase one discovery, four to six weeks. In this first phase we took time to fully understand. The current environment, we access our existing cloud infrastructure review integration points, identify pain points, security gaps and inefficiencies, and define clear business and technical requirements. This phase gives us foundation for small decision making and phase two foundation eight to 10 weeks. So once we define the need, we move to foundation phase here. We deploy the core integration platform, connect your primary systems, whether AWS Azure, GCP, or on-prem, and establish basic data flow and monitoring. This step is all about getting the system, talking to each other in stable, scalable way. And phase three, a enhancement six to eight weeks. Now we bring into real. Power AI automation. In this phase, we implement automated data mapping, enabling indi data routing, set up self-learning policies, enforcement and configure ready to monitoring. This is where we start seeing significant performance gains and reduce error and foster operations. Your system becomes more and more proactive. And I final up phase four, optimizing ongoing. And finally, we, the journey doesn't stop. With the implementation, we move into a continuous optimizing phase where we monitor performance metrics, analyze system behavior, and fine tune integration platform for even better results forward time. This ensure the system grow with our business adopts to change and keeps improving. Going to next slide, key takeaways. So transforming your multi-cloud strategy with ai. As we come to close off the session, let's take a moment to recap the takeaway, the core message that can help transform your multi-cloud strategy using ai. These are just technical points. They are strategic shifts that can help your business move faster, smarter, and more content in the cloud integration complexity. First takeaway is simple but powerful. A dramatically simplifies integration throughout automated data mapping and indulgent routing. You can reduce manual effort by hway percentage. Unified visibility drives performance. Performance. Visibility is performance with the a. Power monitoring is in place. You can detect 94% of issues before they have an impact. And strategy Platform selection prevents lockin by focusing on interoperability, AI capabilities, and vendor independencies. You'll prevent vendor lockin and set your business up for long run flexibility. It ensures that your cloud strategy is not just effective, but also future reference and measurable R way across industries. And finally. The real business results we see in organizations in financial services, healthcare and retail achieve 40 to 60% efficiency gains through aid driven integration. This is in theory, it's happening now across industries and across the world. And next slide. Thank you all for your time and today it's been a honor to share insights how AI is transforming multi-cloud integration. Not just as a technology upgrade, but as strategic enabled for performance, compliance and innovation. I hope this session has helped you seen what possible. And we bring intel and automat and visibility into complex cloud environment. Thank you. Thank you all for your time.