Hello everyone. Thank you for joining my session. I'm Mahmood Nawaz Khan Muhammad, and sesson topic is AI Powered Knowledge Systems for Resilient Cloud Incident Response. Today's presentation focuses on the evolving challenges of cloud reliability and how modern engineering teams. Can adapt their incident response and knowledge management strategies. As organization embrace digital transformation, the way engineering teams operate has changed dramatically. Cloud native architectures built on microservices, containers and serverless technologies have introduced new layers of complexity and failures that simplified did not, or simply doesn't exist in traditional systems. At the same time, deployment velocity has accelerated with many teams pushing codes into production multiple times a day. This creates a high pressure environment where reliability must be maintained despite constant change. The challenge is that most incident response frameworks were designed for slower moving monolithic systems. They're not equipped to handle the speed and the scale of today's cloud native environments. And with the growing shortage of experienced engineers, we cannot rely solely on tribal knowledge to solve problems. We need scalable, intelligent systems that support fast effective incident resolution and knowledge sharing across teams. And because of that we have many challenges and one of them is knowledge Half-life. Charlie, this challenge modern engineering organization on the rapid decay of technical knowledge, the knowledge half-life refers to the diminishing relevance of information about a particular technology or a system over time, often becoming obsolete. Or significantly less useful within a short period. There are specifically three challenges. One is accelerating innovation where cloud providers constantly introduce new services, features altering how existing systems behave and generate novel failure modes. And then there is a cascading complexity. Even with minor changes, can impact multiple system components, necessities, extensive adjustments to monitoring, altering deployment scripts and operational procedures. And the third one is continuous delivery. Agile practices accelerate system change, frequency rendering, troubleshooting expertise. Obsolete within months due to frequent updates and architectural shifts. Implication of knowledge dq The accelerating knowledge. Half-life extends beyond individual productivity. Organizations invest significant resources in training, documentation, and knowledge transfer initiatives when knowledge becomes obsolete more quickly. The return on this investment diminishes forcing organizations to allocate more resources to keeping the teams current. This creates a continuous cycle where teams struggle to maintain expertise while the underlying systems continue to evolve at an increasing pace. The foundation of AI driven eco knowledge, if. AI driven knowledge ecosystems. The foundation has four layers. The first one is the foundation layer, which primarily focuses on high performance knowledge operations with low latency access, high availability, and horizontal scalability. This leverages the vector database, optimized for similarity search and event driven architecture for real time knowledge updates. And the second layer is the processing layer. The cognitive core raw data transformations into actionable insights through advanced machine learning and natural language processing, extracted structured data or information from unstructured sources like logs, incident reports, communication threats, and then there is a third one, which is interaction layer. Which bridges the AI powered knowledge processing with engineering teams, practical needs through conversational interfaces. And the fourth one is integration layer. This embeds knowledge capabilities into existing workflows through API, integration with incident management platforms, monitoring systems, and collaboration tools. The processing layer, intelligence and insight generation. The processing layer represents the cognitive core of AI driven knowledge ecosystems, transforming raw data into actionable insights. Through advanced machine learning and natural language processing, modern cloud environments generate vast amounts of unstructured data containing valuable information. Like log files, incident reports, and communication logs and communication threats all contain knowledge that can enhance future incident report efforts. The processing layer employs sophisticated NLP algorithms to extract structured information from unstructured sources like log files. In traction layer, it's human-centered knowledge access. So there are four phases of it. One is conversational interfaces. This allow teams to ask natural language questions about system behavior, historical incidents, and troubleshooting procedures, reducing cognitive load during high stress scenarios. And then there is a visual dashboards. This presents complex system relationships, incident timelines and diagnostic information in formats, enabling rapid pattern and decision making. And the third one is personalization. This adapts to individual user preferences and expertise levels, providing detailed explanations for new team members and concise technical summaries for experienced engineers. The fourth one is context array delivery. This detects operational context based on active alerts, metrics, and user activities. Proactively surfacing relevant knowledge without requiring explicit queries. Integration layer. It designed to seamlessly integrate with workflow embedded. The integration layer ensures that AI driven knowledge capabilities become an integral part of existing operational workflows rather than requiring separate tools and processes. And how do we do that? We have four steps for that, and the first one is API integration. This provides the foundation for embedding knowledge capabilities into existing incident management platforms, monitoring systems and collaboration tools. And the second one is workflow automation. This triggers knowledge updates and distribution based on specific events or conditions automatically extracting and distributing. Key insights when teams resolve incidents. And the third one is contextual delivery. This automatically surfaces relevant knowledge based on current system conditions, matching anomalous behavior patterns with historical incidents. And the fourth and the final one is real time synchronization. And this ensures that knowledge based content system, data and operational contents are consistently up to date across all the integrated platforms, eliminating information, silos. Based on this, the real world implementation and the performance metrics are shown on this slide, as you can see. Using this, we can, we have achieved 99.8% or we could achieve 99.8% of processing accuracy. This ensures insights and recommendations are reliable and actionable, meeting or exceeding human level performance for many knowledge extraction task. And this also achieved 90% latency reduction. What that means is it takes less time to deliver relevant information compared to the traditional knowledge management approaches requiring manual searches. And this also helped us to achieve 75% reduction in the cognitive burden, decreasing mental effort required for engineers to find and apply. Relevant information during high pressure incident response scenarios. Organizations implementing these systems report significant improvements in multiple areas that directly affect their ability to maintain system reliability and respond effectively to operational challenges. Solution generation and organizational learning. The ultimate measure of knowledge system effectiveness lies in its ability to accelerate solution generation and support continuous organizational learning. Traditional approaches often require teams to rediscover solutions, but others have already developed. AI driven knowledge ecosystems can significantly accelerate solution generation by automatically identifying similar historical incidents and presenting relevant approaches, machine learning algorithms analyze content and current incident characteristics and match them against historical patterns to suggest effective troubleshooting steps. The continuous learning capabilities ensures that insights from each incident contribute to collective knowledge base, creating a positive feedback loop where incident response capabilities improve over time. Building organizational resilience at scale. The implementation of AI knowledge ecosystem represents more than just a technological upgrade. It constitutes a fundamental shift toward building organizational resilience in phase of increasing technological complexity and operational challenges. Scalable resilience requires moving beyond approaches that depend on individual expertise or manual maintain processes. AI driven systems provide the scalability necessary to maintain high levels of operational effectiveness. Effectiveness even as complexity and scope continue to grow. And because of this, there are two. Effects. One is network effects. As more teams contribute experiences and insights, the collective knowledge base becomes increasingly comprehensive and valuable. And then there is a cross team knowledge. This automatically identifies insights relevant across multiple teams, breaking down organizational silos and embedding more effective collaboration, the future direction and conclusion and continuous evolution. The field of AI driven. Knowledge system continues to evolve rapidly with the new capabilities and approaches emerging regularly. Organizations must adopt strategies for continuous evolution that allow to incorporate new technologies as they become available. And we can do this in the three steps. We can have advanced AI models. Which has large language models and advanced reasoning systems offer new possibilities for knowledge systems and knowledge synthesis at levels approaching human comprehension with while operating at machine speed and scale. And the second one is structure operational data. This helps growing availability of metrics, traces, and logs. Creates opportunities for sophisticated analysis, identifying subtle patterns that might escape human attention. And then there is a predictive capabilities, machine learning algorithms processing vast operational telemetry to identify leading indicators of potential issues, enabling proactive invent interventions. The key benefits of AI driven knowledge ecosystem, the first and the foremost is faster incident resolution. As reducing mean time to resolution by automatically delivering relevant historical solutions. The second one is preserved expertise. This helps by capturing and maintaining organizational knowledge despite team changes and turnover. And the third benefit is scalable operations and by mean that it supports growing system complexity without proportional increase in staffing. And the fourth one is continuous learning. This improves response capabilities over time through automated knowledge capture. And the fifth and the last one is enhance resilience, building organizational capacity to withstand and quickly recover from disruptions. Conclusion, the path forward. The transformation of incident response through AI driven ecosystem represents a necessary evolution in response to accelerating technological change and increasing system complexity. Organizations that successfully implement these systems position themselves to maintain high levels of operation. And operational effectiveness. Despite rapid evolving cloud technologies, as organizations navigate the complexity of modern cloud environments, AI driven knowledge ecosystems will increasingly become a computive. And competitive necessity rather than merely an operational improvement. Success of this approach depends not only on selecting appropriate AI technologies and architectural approaches, but also on fostering organizational cultures that value knowledge sharing, continuous learning, and collaborative problem solving. The organizations that achieve the, this integration of technological capability and cultural transformation will be best positioned to thrive in an increasingly complex and rapidly evolving technological landscape. By this, we come to the end of this presentation and. Again, I'm Hamud Han Mohammad, please reach out to me and share your feedback. I appreciate it and thanks for watching my session and joining with me. Thank you so much.