Hello everyone and thank you for this opportunity to present at Con 42 Platform Engineering 2025. My name Isla, and I've been working as AWS Cloud engineer for the past seven years, helping build scalable, secure, and cost efficient cloud environments. Today I'll be talking about platform engineering with AI driven self-healing AWS infrastructure. The goal of this session is to show how organizations can transform from manual. Reactive cloud operations into autonomous intelligent systems that can self-diagnose, self-heal, and continuously improve reliability. We'll explore not just the technology, but also the organizational and cultural changes needed to make this journey successful. By the end, my aim is for you to walk away with both practical strategies you can apply and a broader vision of how platform engineering and AI can help build smarter and more resilient infrastructure. Platform engineering has evolved significantly over the years. Traditionally, teams relied on manual interventions, firefighting incidents, and patching systems reactively. This approach worked when systems were small, but today's multi-cloud and large scale AWS environments are far too complex. The modern approach is about intelligent autonomous systems that can self-diagnose, self-heal, and continuously optimize. It's important to note that AI doesn't replace engineers. Instead, it amplifies human judgment by learning from instance, predicting failures and automatically implementing remediation strategies. Now, let's discuss the core challenges at scale. Overwhelming volume of alerts and monitoring data exceeds human capacity. Speed of modern systems leaves little room for human interventions rapidly evolving Security threads require continuous monitoring complaints. Requirements add another layer of operational burden. Scarcity of platform engineers expertise and cost optimization at scale requires intelligent automation. Traditional approaches to platform engineering cannot scale to meet the demands of modern enterprise entire environment. Organizations that continue to rely primarily on manual processes find themselves at a competitive disadvantage, struggling with higher operational costs, increased downtime and reduced agility. Here we introduced the AI driven self-healing architecture. First comes the intelligent monitoring using Amazon DevOps Guru to detect anomalies. Amazon DevOps guru uses ML to analyze metrics, logs, and traces to identify anomalous behavior patterns that might indicate emerging problems. Next is the security monitoring, using guard duty to identify suspicious activities. Guard duty also uses ml. Models trained on AWS global threat, inter intelligence to identify suspicious activities and security breaches. Now the issues are detected and we need more AWS services to respond to these issues. Like AWS Lambda functions serve as the execution engine for a remediation actions triggered by the events from the monitoring. Services, step functions, orchestrate complex remediation workflows that require multiple coordinated actions. The architecture continuously learns and outcomes of remediations are analyzed so that the system improves over the time. This makes operations not just automated, but adaptive and intelligent. Let's look at the real world examples before implementation. Platform teams spent most time responding to incidents. Thousands of monitoring notifications generated daily critical incidents, took hours to resolve security incidents required extensive manual investigation, but after adapting, AI driven self-healing systems available, improved significantly. Meantime to resolution, decreased dramatically. Security incident response times improved. Compliance violations addressed proactively substantial cost savings from optimized resources. The organization's platform team evolved from reactive problem solvers to strategic architects of autonomous systems, improving job satisfaction and enabling the organizations to attract and retain top talent. Now let's look into the four stage maturity model. Stage one is the reactive automation, and next is the predictive monitoring. And then we'll go with the intelligent remediation and autonomous operations. Now let's dive into each of these stages and see what all we need to do to implement these stages. So the first one is reactive automation. So in this one, automating responses to understood routine problems with scripted remediation tasks and basic monitoring systems. These include like basic infrastructure as core practices, standardized monitoring dashboards, or having automated runbooks for common scenarios. And in the stage two, predictive monitoring ML algorithms analyze system behavior patterns to identify potential problems before the impact users here. AI powered monitoring tools like DevOps Guru can be used and sophisticated electing systems. Can be adapted and expanded automated remediation scenarios. In stage three, we implement intelligent remediation process. So far it's just automating how we detect the issues and monitoring them. So now it comes to the remediation process. So AI systems automatically diagnose and resolve most routine problems without human intervention. Like comprehensive event driven architectures, complex orchestrated workflows are learning from remediation outcomes. And next comes the autonomous operations. AI systems handle the vast majority of operational tasks with minimal human oversight. Like predictive capabilities prevent most problems, self-healing mechanisms resolve issues automatically. Humans focus on strategy and exceptional situations. Progression through these strategies and stages requires careful attention to cultural change. Alongside the technical implementation, the teams needs to trust the autonomous systems making decisions which are previously reserved for human judgment. Now let's technically deep dive into some of the AWS service integrations. So the first one is DevOps group. It creates operational baselines by analyzing historical data patterns, learning normal behavior for each application component. It uses cloud wash event to trigger Lambda functions when anomalies are identified. And next is the AWS Guard, duty Guard, duty Analyzes, DNS logs, VPS flow, VVPC, flow logs and cloud rail events to identify suspicious activities. Findings are published to CloudWatch events, enabling real time integration with automated response systems. Next is a S config. It continuously monitors resource states against defined policies. Integration with system manager enables automated remediation through predefined playbooks that can correct common configuration drift scenarios. And next is step functions. So step functions orchestrates complex remediation workflows with sophisticated logic for error handling, retrain mechanisms and rollback procedures. Lambda function serve as the primary execution environment for remediation logic. The event driven arch architecture enables realtime response while maintaining loose coupling between the components. CloudWatch monitoring and events serve as the tech, as the central nervous system routing events to appropriate handlers based on configurable rules. But transformation isn't just technical. It requires organizational change. Roles evolve from troubleshooting incidents to designing intelligent systems. Teams need new skills in ML operations, even driven architecture and serverless computing. Culturally, there must be trust in automation achieved through gradual rollout monitoring and clear escalation protocols. Leadership commitment proves essential for successful transformation requiring sustained investment in technology, training, and cultural change. Security and complaints are also vital in this process. Guard's ML models continuously detect malicious activity. Lambda functions immediately isolate threats and config ensures continuous complaints the than waiting for audits. Compliance violations are corrected instantly. Importantly, not all alerts are equal. The system prioritizes based on severity and business impact, like critical issues triggered immediate action and escalation. While lower level ones are queued for automated fix later, this balance ensures both agility and security. Now let's discuss change management strategies like training programs, mentorship, pairing, experienced engineering with. Newer team members are cross-training initiatives, exposing team members to different aspects of autonomous operations, external training programs, providing specialized knowledge in AI and ml. Next is the performance measurement. We should move from traditional performance measurement strategies and adapt new metrics, emphasizing systems reliability and autonomous remediation success rates based on contribution to business objectives and collaboration with ai. Communication strategies need to have clear protocols for when and how to intervene in automated processes. Procedures for escalating problems beyond automated capabilities are must methods for sharing knowledge about system behavior patterns like knowledge translations are good. Next is the risk management governance frameworks, providing appropriate oversight and addressing risk. Regarding automated decision making. So this transformation timeline typically spans multiple years, requiring patience and persistence from both leadership and team members do ahead. Future trends will take this even further. Generative AI will create remediation strategies for novel problems. Making automation more creative. Explainable AI will increase transparency, helping teams understand why certain actions were taken. Next is AI powered code generation. This will speed up development time by improving our, giving them solutions for developing infrastructure as code. At the same time, edge computing, content computing and multi-cloud orchestration will push capabilities beyond today's systems. These trends promise powerful capabilities, but also require careful evaluation of readiness and integration, complexity, simulation, and digital vents. These are emerging powerful enablers. Digital twins allow entire infrastructure environments to be replicated virtually. This lets teams simulate failures, cascading problems, and remediation responses without risking prediction. It helps identify weaknesses before they occur in real environments. This is especially valuable for industries like financial services, healthcare, or any other critical infrastructure where downtime is unacceptable. Continuous implement through simulation will be a cornerstone of future self-healing systems. Based on all of this, here are some recommended steps for implementation First, as is current challenges and identify high impact automation opportunities. And next, build a strong foundation in like infrastructure as code, or. Having monitoring services and dashboards in place and adapt event driven systems. Next is implement automation gradually, like start with the low risk areas and then go with your, like in the first step you have, you might have assessed some of the existing challenges. Just gradually increase one take one risk at a time and implement one solution at a time. So that this will help you grow more in future. Fourth is invest in training and cultural change. This is as important as the technical change like I discussed previously. Next is establishing governance frameworks for oversight. So like when all the systems are automated and the decisions are automated, you need to know what is the escalation process and when and human should. Intervene or when he shouldn't. We need to establish the trust process and escalation process when something goes wrong. And finally, continuously measure and improve. So all these stages take multi-year transformation timeline and plan for su sustained investment in technology training and organizational support. This journey requires commitment, patience, and sustained effort. But potential benefits make it essential for organizations seeking to remain. T. The future of platform engineering lies in intelligent partnerships between humans and ai. AI handles the bulk of routine operations while humans focus on strategy, innovation, and exceptional situation. This partnership enhances system resilience, reduces operational overhead, and allows platform engineers to deliver more strategic value. Organizations that embrace this transformation thoroughly and thoughtfully will build scalable, resilient, and competitive capabilities for the future. Thank you all for listening. I hope this session helped you understand not only the technology. But also the cultural and organizational shifts required for AI driven self-healing infrastructure. This is not just about automation. It's about enabling platform engineering teams to deliver higher value, innovate faster, and create more resilient systems. Thank you again, con 42 for this great opportunity.