Hello all. I'm Na Kala. I'm excited to talk to you today about how Cloud is evolving and also more importantly, how we can bring together different cloud platforms and make them work seamlessly and securely. We'll be discussing four main key points. In this presentation the first one is AI driven, API Management, followed by confidential computing, zero trust and event driven architectures. These are game changes for any of the industry that is already into multiple clouds or that is going to invest into multiple clouds. Moving on to the very first slide, multi-cloud reality. 94% of enterprises use multi-cloud platforms. They'll be using AWS Azure or Google Cloud to leverage specific capabilities. The advantage is that vendor lockin is totally avoided, but the cons is that, integration issues exist because the by the data communication to and fro between the systems needs some effective and comprehensive integration strategy. And mostly will be facing data synchronization hurdles, which will lead to implementation delays and also, which will lead to budget overruns. And also there'll be many issues. With security policies because each cloud will come with its own security and we need to create a unified security policy that can be used to overcome any of the issues. Okay. Moving on to the second slide. Benefits of effective cloud integration. So as long as we have a comprehensive integration strategy, the deployment rate will be faster and we can see 30 to 40% of improvement. And also there'll be grade agility. Agility is directly proportional to marketer around or business turn turnaround because we will build and, deployed things faster, so that will give a positive impact to the whole business. And also resilience, especially during service interruptions will be benefited. I identified few of the integration complexity challenges that will be commonly faced. One thing the main thing is that with each cloud edition the integration complexity will be increased by at least 70%. And also each cloud will have different security models. AWS will have its own model and Google Cloud will have its own model. We have to make sure that the security policies is correctly, are correctly laid as discussed before. And also there'll be a lot of inconsistencies with the a p structures and authentication and authorization mechanisms. And also there'll be differences between data formats and storage mechanisms. AI driven API management platforms. There are mainly efficiency gains and governance benefits, and this is not a traditional approach. This is AI driven, so the bugs will be decreased. And also the overall, implementation time will be cut down and there'll be several other benefits for few of the benefits are identified and written here. So reduction in API design improvement in overall API, usability metrics and also reduction in MA manual documentation. Efforts and benefits are mostly related to performance optimizations and also related to compliance and security. So we can see policy violations reduction, compliance verification, time improvement, and then latency through traffic optimization reduction and also resource provisioning. Costs will get reduced. And moving on to the next slide enhanced security through AI driven a BI management. So there are in this chart. The dark color ones are a traditional approach, and the lighter color ones the green the bright green ones belong to ai, a approach or AI enhanced approach. So have the they will be reduction in false positive rate and also exploitation reduction. So we'll be able to, du reduce exploitation by detecting them early and also attack detection will be showing a positive impact when we go with AI models. So overall on an average 78.3 successful exploitation effort attempt. And then, machine learning models can detect up to 93% of API based attacks while maintaining false positive rates below 4%. So AI driven is more secure and also it'll mitigate more protection vulnerabilities before even the exploitation items are occurred. And before even the traditional methods or traditional strategies kick in, AI will take care of most of them. And confidential computing. So confidential computing mostly rotates around the concept of half data is secured. So data, when data is in use, AI models will secure it even more even more in a better way. When I mean in use, it's nothing but being in transit, but data at rest. We will be predicting it either based using traditional or security model. So traditional or AI based security model. So data addressed, sophisticated encryption algorithms, protect store data from unauthorized access and data in transit. T-L-S-S-S. S SL protocols and encrypted channel safeguard data during transmission and data in use. Hardware based ttes create secure enclaves. That product data during computation and complete protection, comprehensive security framework ensures data remains encrypted throughout its entire lifecycle. These specialized enclaves whatever we are discussing about, especially for data in use or for TES maintain encryption during during active computational process, creating a secure, isolated environment that. Prevents otherized access. So even for users or system administrators or cloud service themselves will not have access. So this is more secure and confidential computing performance breakthrough. So it is 33 times more fast. Organizations implementing confidential computing for analytics and their AI workloads have reported up to 33 x fast. Processing and hardware enforced security. The tes Predict application from attacks with privileged system access, effectively mitigating threats from any of the administrators, hypervisors BIOS and OS and other operating systems. Expanded capabilities also exist. So Intel's third SGX implementations have expanded secure memory capacity from 1 28 MB 2 5 12 gb dramatically increasing the scope of workloads that benefit from hardware based protection. Industry applications of confidential computing healthcare, definitely healthcare organizations can securely process a PHI in the cloud while maintaining HIPAA con compliance. Enabling advanced analytics on patient data by disclosing all the sensitive in information and financial services. Like financial institutions can perform complex analytics. And research collaboration. Federated learning frameworks allow multiple organizations to collectively train AI models on their combined data sets while mathematically guaranteeing that no party can access the original data from other participants. Zero trust architecture. So never trust, always verify. Zero trust architecture principle is that it will. Require a continuous authentication and authorization of all entities regardless of their location or network connection. So least privilege access will be enforced here. So minimum necessary permissions and then isolated network zones by workload. And also it'll calculate all the real time behavior analytics. And then we, it'll have lot of identity verification principles, like advanced authentication frameworks and also custom authentication frameworks that can be used. So this approach replaces all the traditional security with a model that is Zooms breach and verifies each request as though it originates from untrusted network. It continuously keeps pinging us and verifying us which is great. This is definitely having a lot of impact. And pros on every everybody's business. Organization implementations experience of frameworks, experience 43% reduction in successful network introductions, 37% reduction in security incident response costs, and 29% decrease in overall security management overhead. And also advanced authentication frameworks reported a 92% success rate in pre preventing credential based attacks. While organizations employing real time behavior analytics detected suspicious activities, 73% faster than those using periodic security scanning approaches, event driven architectures for real time integrations. So event driven architectures mostly, or rotate around using system bus and using subscribers, publishers, and making the tasks even more fast and easier. So even generation system changes trigger events and routes them to subscribers and subscribers React to the events. Also there'll be state synchronization where system will maintain consistency across all the services and keep updating the right data and also getting the right data. Even architecture is loosely coupled. It supports real time data synchronization across drivers cloud environments. So whenever there is a change, immediately through through the bus, you can kick off the change. Organizations imple implementing EDA frameworks experience a 58% reduction in data integr integrated latency compared to traditional scheduled batch processing approaches. Just a second. Yep. And the performance metrics I gave few of the performance metrics in the slide. So what is the latency reduction and how many events will we processed per second message delivery guarantee, responsiveness increase, incident reduction maintenance cost reduction. So this architectural approach allows organizations to build responsive systems that immediately react to business events as they occur. I would say this is more proactive in nature and it'll be very much helpful for industries to use them as their real time or near near real time most of the time. Financial services industrial, IOD and global Enterprises are heavily using all the EDI architectures. And then for financial services, 63% faster. An anomaly detection for production for potential fraud scenarios and industrial iot. There is a 88% improvement in real time processing capability, enabling monitoring systems to process data from over 25,000 sensors with a sub-second response time. And also for the Global Enterprises, there is a 71% reduction in cross-regional data inconsistencies while using EDA, which is really great. And overall the future of the secure platform integration is all the four aspects that we have discussed right now. We have to create a framework in such a way that all of these, at all of these key points that we have covered are balanced in each and every strategy that we come up for the organization. So as organization continues their digital transformational journeys these approaches will become increasingly essential components of a strategic technology foundation. Thank you all. That's my take.