Hello everyone. Welcome to the session. I am Superman Maade. Today we are going to talk about observability in the cloud, especially in the banking domain, how it enhances the security and trust. Let's explore how the observability transforms security and compliance in cloud-based banking systems. We will go through some practical frameworks, real world examples and technical solutions for today's most pressing financial technology challenges. Let's begin with the landscape of banking. It. We have traditional infrastructures where banks completely relied on-premises data centers with the full control or all the systems with the recent changes in the cloud. Organizations began moving selective workloads to cloud providers while maintaining critical systems in-house. Still, this hybrid transition is somewhat a better when compared to maintaining the criticality at the in on-premises and still leveraging the cloud solutions. But there are for some modern financial institutions. Completely leveraging the distributor, microservices, and serverless architecture for agility. The call services cloud native banking. This is where we have this. The need for advanced monitoring across all the layers becomes essential for the security and the compliance that comes with the observability first approach here. Let's look at some of the challenges in the banking, especially when you're talking about the banks. We have some financial institutions face its very stringent compliance requirements from the Basil 3G DPR, and regional banking authorities. This regulatory pressure is always there. As the applications infrastructures are changing their, the way they are built, distributed architecture makes it very complicated because it creates a complex dependencies across the services, and traditional monitoring cannot work here. The surface is also very increasing because the application landscape is moving towards multiple cloud providers and multiple regions. We are increasing the surface of the application and thereby we are seeing the security complexity as well. At the same time, the most important the data, see where in the banks must maintain a strict control or where and how the customer financial data flows and precise and forth. We need to resolve all these things in this modern architecture. So how do we build this observability? So what are the tools that we have right now? We collect a lot of information, the real time quantable measurements like that track the system performance, resource utilization, and the customer transaction volumes across the banking platform. The applications that provides logs, a comprehensive chronological records that we see, a system of events, a system of incidents and financial transactions that provide a detailed audit address. And we have addresses from end to end visibility of transaction pathways across the distribution, microservices, capturing the dependencies, latencies in the payment processing. All these three will make this observability, this robust observability framework that ensures the complete visibility into your banking infrastructure, strengthening the security posture, maintaining the regulatory compliance, and identifying potential issues before they impact the customer experience or a data integrity. Let's look at some of the benefits of the cloud observability in security view, so we can, we have an opportunity to find out these suspicious patterns early. We can identify these suspicions, patterns, anomalies before they become breach and with. Quickly trace the sources, the ability to make where the source and the scope of the security instances across the distribution systems. We can reduce the investigation time as well. Visualize all the service connections and potential vulnerability points, makes it attack surface mapping easy so that you can pinpoint where the problem is there even after the incident, the post incident of 4 0 6 make it very easy. For because of this, maintaining the comprehensive audit trials for thorough security investigations in the view of the compliance and governance, let's take some of the advantages. We have a end-to-end monitoring across all the banking infrastructures that enables a complete transparency into data flows and system interactions. And we have a real time automated compliance validations that ensure the agendas to the bases. 3G DPR and local regulatory frameworks. A flexible observative framework allows the rapid real element to evolving regulatory requirements without system redesign. Immutable audit trials and compliance artifacts provide a comprehensive evidence for regulatory examinations. When you're talking about technical terms, a technical framework, how we could actually implement a cloud observability. Here are the four steps we can say that. The instrumentation, collection and storage and analysis. When it comes to the instrumentation, we need to make our code more intelligent. How do we do that? Strategically integrate sophisticated telemetry code through the critical banking application and infrastructure components for a comprehensive visibility. This integration makes the code intelligent. NF two. Give the sources and give the traces so that we can trace them easily and identify this observability, make it better. How do we collect, not just collecting it, we need to make and normalize these metrics as well also, but how do we do that? Let's establish a robust. A fault tolerant data pipeline to efficiently gather anomal is metrics, logs, traces from various diverse banking systems. We need to keep work on the storage as well because the data differe is very important at the bank. So we need to deploy a regulatory compliant, highly secure data repositories. With a granular retention policies that satisfy both operational and the compliance requirements. Let's leverage and machine learning algorithms for analysis to correlate the data streams and detect the security anomalies and identify any performance opportunities. Optimizations there here. We are just going through one of the case study from a retail bank. It's a real world example of case where the challenge was the payment processing system. A breach went undetected. There was a payment processing system breach, and it went undetected for 72 hours, is very critical, but this could actually happen with the traditional wandering. So in this case, the bank has adopted the implementing the distributor tracing across cloud microservices with anomaly detection, resulting it to detect the any instances under five minutes with a 99.8 accuracy. So this has prevented almost mean 4.2 million in fraud losses, maintained regulatory complaints at the same time. Written on investment cloud observable. Let's talk about some statistics that we have received so far. How did the instance reduction happening after this observability implementation? There are 62 percentage reduction, meaning a very few security breaches and complex violations when in a case of issue. How fast we are responding back, the response time has increased to 78% because we know the complete infrastructure and we can visualize where the and problem is, we can increase the, that actually saves the amount of savings at the bank that roughly around $3.8 million. In the audit purpose, okay. We have a lot of documentation, reduction in compliance. Documentation effort is there because we have a comprehensive analysis of the logs, which is there, which is actually making 41 percentage reduction in the maintaining the documentation efforts that actually increases the audit efficiency. Let's look at some of the best practices for a banking observable. When you want to think, implement the observability at the bank or applications, we should see these things in the beginning itself. The build observable into your architecture from the beginning, not as afterthought. We need to create this business context of mapping the technical metrics as to a banking business outcomes and the customer experience. We should be having that map ready. Lot of a powered, and our tools are available. Let's leverage them, leverage the mission learning, and to detect the anomalies in the complex banking transactions. At the same time, we should be focusing on the cost management. This implemented data sampling strategies to balance this observability needs with the cloud expenses. And finally look at the next steps, how the observability journey goes. Let's start with the assessment. If you wanted to implement this observability at your end, these are the steps you need to follow. The first one is always with the assessment. Evaluate your current visibility gaps across the cloud banking infrastructure. Focus on high risk transaction paths first, then do a pilot implementation. Deploy observability tools in one critical banking service. Measure the security and compliance improvements. Let's roll out to the enterprise level. Scaling it to the enterprise level is next comes extend the AB observability across all the banking systems integrated with existing security operations. We need to continue this journey. The continu of continuation of the optimization refine your observability strategy as banking technologies and threats evolve. This is a repeated process. We need to do that. Thanks for your time. If you have any questions if you wanted to touch base with me, please feel to connect with me. I'm happy to help that. Thank you so much for your time again.