Hi myself, Manu I would like to discuss, speak about autonomous ops for cloud native financial services engineering, cost governance at scale. So myself I'm manure kako, so I currently work at a leading global technology company where I specialize in customer electronics and cloud-based services. Where I lead initiatives in site reliability, engineering and platform automations and cloud cost optimizations with experience in building, scaling, and automating cloud native infrastructure. My expertise with Kubernetes and CICD cost cloud cost governance and infrastructure code. So with a strong focus on reliability performance and compliance in high regular, in industries such as financial services, where I specialized in designing resilient audit ready platforms and implementing autonomous monitoring for cloud related activities and remedies for the workflow. I am presenting this automation finance for cloud native financial services. Why I'm presenting this. So the presentation is about this. Financial services are at crossroads. So they are under immense pressure to modernize with cloud native technologies. We, while facing raising costs unpredictability, scaling, and strict regular demands. So traditional finance ops models are like largely retrospective and manual, so are no longer sufficient to keep up with the speed and complexity of the Kubernetes space multi-tenant environments. So as a lead of engineers, so I am. Working as a DevOps. So I see a an urgent need for embedding a financial intelligence directly into the engineering cycle. This session is about showing how autonomous fan ops with automation and air driven anomaly detection and policy enforcement and auditory design. Can deliver a scalable cost governance without slowing the innovations. Today's agenda is going to be on the finance service cloud dilemma. So traditional fin financial operations limitations, platform engineering with finance ops. And autonomous capabilities and implementing the blueprints. As a financial institute modernization on cloud architectures, right? So the cost governance become both on regularity requirements as a business imperative. So traditional and manual financial operation practices cannot keep up Peace. With dynamic and multi-tenant Kubernetes environments, unpredictable scaling and raising cloud provider cost. And autonomous finance introduced intelligent automations, right? So AI driven detection and embedded policy enforced to deliver real time visibility. And continuous optimization and audit ready compliance by by integrating the financial control directly to the development pipelines and operational workflow. So the financial service and organization can achieve scalable cost covenants predictability and cost spent on strong regulatory alignments without slowing the innovation. So let's go to the next slide. So this slide is about the like financial service cloud dilemma. And expand overview on this is so the financial service cloud dilemma like financial institutions are accelerating cloud adoption to modernize services and stay competitive, but face major hurdles. Legacy systems make migration complex while regulatory demand strict transparency and cost attributes. Multitenant Kubernetes environments at scalable but complicate the cost monitoring, right? So especially with unpredictable scaling driven by the market violations and raising cost provider cost and. A, a skill gap in a traditional IT finance and cloud native operations further intensify the challenge and unchecked. These urist financial stability and compliance to succeed institutes must combine real time visibility automated optimization and strong collaboration between IT finance and. Compliance teams to balance that innovation and the cost governance. So this slide is about acceleration. Digital transformations while managing complex legacy systems and, budget constraints, significant skill gaps complex and multi-tenant Kubernetes environments suffering thousands of workload. So coming to the next slide, traditional finance is falling behind. I'm gonna discuss about the reactor analysis disconnected systems and slow feedback loops. Traditional finance financial operation practices are no longer sufficient for today's cloud native. Fast moving. Financial environments, right? So they rely heavily on reactive retrospective and cost analysis. So like reviewing the cloud bills after the fact rather than providing real time insights disconnected financial and engineering systems create operational slowness. So making it a difficult to align, speeding with actual workload. As a result, like feedback loops are slow, often taking weeks to detect the address cost anomalies. So in highly regulated financial systems. So they delay in more than budgeting problems. It continues a governance failures exposing institutions to both financial risks and compliance issues. So the violation of cloud de developments has outpaced traditional financial operations, living institute vulnerabilities to cost overrun and regulatory scrutinies. So by the time caused spikes the cost spike is detected in traditional financial operations, the financial damage. Is already ineffective. So in the high regulator financial sectors, this isn't the merely a budget concern. It represent significance a governance failures. So coming to the next slides, we'd like to discuss about the platform engineering. And the financial evaluations from reactive monitoring to proactive monitoring, right? So reactive monitoring has post factor cost analysis manual works limited developer bilities, low feedback on cost anomalies coming to the practical governance from reactive. We have in Incre integrated cost optimizations for software development lifecycle real time automated insights empower development, manage speed, and automated anomaly automation. So platform engineering elevates financial operations from react to, to proactive focusing functions to the governance model. Like practical models. So this ambit across the entire engineering life cycle start off relying on cost monitoring and slow workflows. Financial intelligence is integrated directly in developing pipelines are given in real time automated insights and cost impact. This approach empowers developers to manage and optimize optimize spend a as they build while automated system remediates anomalies before they escalates. So the results is a balance model where financial institute gains a ability of cost adoption without sacrificing the cost control, compliance, and financial stability. So unlocking a greater efficient and predictability and long term value creation for the evolution process. So coming to the embedded financial intelligence. So here we are gonna discuss about infrastructure as the code templates and continuous integration, continuous deployment, financial gates Kubernetes administration controls. And real time telemetry. Embedded financial intelligence means building cost awareness and governance directly into the engineering workflow. And rather than treating it as a after true this is achieved through like these four mentioned qualities like infrastructure as a code templates. CICD, KU administration, real time telemetry. So by waiving financial control into development and deployment process, financial interest can achieve continuous cost, governance and proactive anomaly detection and predictability cloud span like all while maintaining compliance process. So coming to the pre-deployment cost estimation. Technical implementations involves averaging static analysis for infrastructure as a code templates for precious. Our resource cost calculations utilize historical users datas for accuracy integrating directly with a cloud provider like pricing, APIs and realtime stuff. Optimizing costs through reverse instance and saving like scale down and stuff. Financial government benefits includes enabling proactive budgeting validation prior to the development. Providing cost estimation confidence intervals for better financial planning. Faculty Accu Curate project level financial forecasting. Maintain a clear audit trail for cost of brewers, so pre pre-development, cost estimate, and based like financial accountability early in the software or development lifecycle by predicting cost spend before workloads are launched. Technically this involves analyzing infrastructure code. Templates using historical usage pattern for high traffic or high user data patterns. And pulling the real time pricing data from cloud providers on enterprise cloud subnets, or to calculate expected costs. Optimizing strategies such as or as mentioned rivers instance and savings plans further refine these estimations. For financial governance perspective, these these enables institutions to validate budgets proactively, right? And produce a confident base forecasting and maintain an audit trial to cost approvals. So this results in more predictable financial planning and reduce their risk to unexpected cl cloud financial burdens or expenses. Moving on to the slide of policy-based budgeting gating. Define budget policies. So I'm gonna is about implementing enforce points build approval workflow and create a feedback mechanism. So policy based budget getting enforce financial guardrails throughout the software or development pipelines, right? So by defining. Granular YAML based budgeting pro policies to the project team, or application level organizations can ensure spending limits are respected. These policies are enforced at a critical stage of code commits, bills, deployments, scaling events to preview. To the previous comments are reviewing the level of compassion. So to prevent the audit runs before they occurs, right? So automated approval workflow handles exceptionals with proper authorization and audit logging. So while real time dashboards are alerts, provide continuous visibility into the. Compliance and budget consumption. So this approach ensures cloud spending stay on predictability avoid cost surprises, and maintain regular compliance in the financial services. So coming to the next slide, we have automated tagging and compliance. So automated tagging and. Compliance of automated finance. So financial operations ensure every resource is accurate, tracker and allocated and completed. So this includes enforcement managed tag schemas at creation enabling tagging inheritance to consistent line age. And dynamically applying tags based on the deployment context. Tag validation gates with continuous integration kind deployment pipelines previously non-compliant deployments while remediation bots automatically fixes untagged or mist tagged resources, right? So comprehensive compliant report. Provider audit readiness, visibility and accountability, supporting both financial transparency and regulatory requirements. Yeah. Coming to the next slide, we are going to discuss on real time anomaly detection. So this involves time series analysis, resource fingerprinting. And cultivation detection and predictive forecasting. Real time anomalies that users machine learning to move beyond static alerts and provide adaptive, pro and proactive monitoring for cloud spanning right by applying time series analysis. It identifies Devi station from historical usage and seasonal trends. Resource fingerprint creates a behavioral baselines for workloads while co correlations with detection line cost spikes for the deployments and as well as the application events or external triggers. So additionally proactive forecasting projects future costs, right? So with confidence interval enabling terms to anticipate issues before they escalate they approach ensure financial institutes can rapidly detect it or, they can investigate, right? And remediate the anomalies. By maintaining cost control, compliance, and operational stability by maintaining the governance in dynamic cloud environments. How like self-healing cost for remediation works. So it can help out with anomaly detection, root cause analysis remediation workflow, and automated actions. So typically, self-healing remediation action includes right size over provision resources automatically applying these computer reservation discounts suggesting and executing storage to your operations. Recommending face id architectural changes for sustaining savings. So self-healing cost remediation represents the next stage of intelligence financial operations. So where cloud environments not only detect the anomalies, but also resolve them automatically to prevent financial leakages. And compliance risks. So the process begins with a machine learning driven auto anomaly detection which identifies unusual cost patterns are policy violations in real time. So once flagged automated root cause analysis pinpoints the exact resource workloads. Or even the deployments change behind the issues. So based on the severities of the clarifications a remediation workflow is triggered using a predefined playbooks. These workflows may execute in self-service mode or with developer improvements or in a full automated fashion with approvals And, complete audit trials to maintain the governance. So these are the common selfish self-healing action included in the slide. So by by combining AI driven insights automatic and governance, self-healing remediation allows financial institutions to continuously optimize and the cloud environments. That do not only reduce costs, but also enforce compliance improves predictability and enhance operational resilience. Ensuring financial stability is high, highly dramatic. So they dynamically want to reduce, regulate the cloud ecosystem. Oops. So coming to the audit audit ready infrastructure design. This eng includes immutable cost logs metadata governance audit reporting, so critical capability providing the financial controls where enforce consistently across all cloud resource of at all time. Audit ready infrastructure and financial operations, financial services. So in highly regulated financial environments cloud infrastructure support, a comprehensive audit or auditability. And strict fi, financial ance automation, financial operations solution address. These need to be provided Im immutable. Cost logs standardization, metadata, schemas tamper proof recording for all the financial conditions. Approvals, cost at attributions, so those system enable automated reporting aligned with regular frameworks such as GDPX SOX compliance. PCI compliance DSS. So ensuring these financial controls are constantly enforcing across all cloud resource at all times. Prebuilt reports and structural governments framework simply compliance reduce the risks and provide verifiable evidence for the audits. That helps. Yeah. Coming to the next slide, building up the financial platform for technical implementation blueprints. So the core components involves cost estimations, service integration with infrastructure as code policy engine for budget enforcement financial, elementary collection and storage machine learning pipeline for anomaly detection. Remediation workflow and orchestration. So integration points involves but not less than version control, pre-commit hooks, integration, energy deployment, pipeline stages. Kubernetes administration controls cloud provider APIs and even streams existing observability platforms. Building your financial operation platform for a technical blueprint is a robust platform financial operation platform that combines costs, services, and strategic integrations to manage cloud cost efficiently. And core components includes a cost estimation service integrated with infrastructure as a code a. And a policy engine for budget enforcement as mentioned. So not only the financial telemetry collection and storage, but the mission learning part, anomaly detection pipeline. So to max the, to maximize impact organizations should focus on high value integration point points first, and expand to the platform integr integrations. And to ensure scalable and sustainable financial operation systems. Coming to the next slide, so we are gonna discuss about measuring financial success for cost reduction cost predictability policy compliance and response time. Measuring the financial success is in financial services, right? So efficiently evaluating the financial success requires a multi-dimensional approach that goes beyond simple cost savings. One key metrics is cost reduction, which measures the quantifiability savings. But achieving through the autonomous optimization and intelligent cloud management like also come back to the traditional manual work manual methods complementing these to a cost predictability, which tracks percentage to cost spend that constantly aligns with predefined forecasting range. That enables organizations to plan budgeting with a greater accuracy and reduce financial uncertainty. Another critical measures is policy compliance where to ensure that all cloud resources are currently tagged and allocated to the respective cost centers. This is not only maintains, ad hoc to the financial governance, but also provides clear visibility to audit and regulatory reporting. Beyond these direct financial metrics, a successful financial program financial operations program, I would say also delivers broader organization value, right? Improved financial governance reduced the risks to audit discrepancies and regular penalties. So enhanced self-service capabilities, imports developers to developers and the cloud team to operate more efficiently forecasting involving while maintaining cost discipline. By combining measure measurable savings and predictable budgeting and regulatory compliance. Also, I a rapid anomaly response. Organizing these can historically access the impact of their financial operations initiative and ensure the cloud investments deliver both. Finance and the benefits of the organizations. Coming to the final slide on the takeaways. So the key takeaways are shifting finance operational lift empower, engineering automated governance design to design for the compliance. Coming to the key takeaways for efficient financial operations implementation. So this is important because to achieve maximum impact for financial operation initiative organizations must adapt a strategic, proactive approach that embed financial intelligence throughout the software. Delivered life cycle. And shifting financial operations left insurers the cost valuable and financial controls are integrated earlier than the expected. So in this development and deployment process so allowing teams, also allowing teams to anticipate and migrate cloud spending risks before they escalate, right? Also equally important in empowering engineering teams while self-service tools and actionable insights are in action while developers and excuse me, and operations have ability to make information cost conscious stations without bottleneck necks and, and other issues. So organization benefits from fast innovation cycle improve accountability and risk reduced for budget over automating governance. In other critical is the other critical pillar for this financial operations initiative by moving from manual reviews to automated policy enforcement. So financial. Controls can be considerable applied across all cloud resources at scale reducing human errors and ensuring compliance with internal policies and external regulations. So finally designing for compliance is essential in regulator industries. So systems must be architect for audit readiness with immutable logs of transparent financial records and standardized report framework that demonstrates add all to regular regulatory requirements. So by combining proactive cost management developer empowers automated governance and compliance focus designs. Organizations can optimize cost investments while maintaining financial discipline operational affectiveness and regulatory assurance. So this is about the financial operations initiative, and thank you.