Good day everyone. Welcome to our session. In this talk, we will discuss several practical strategies for optimizing incident response workflows and leveraging aipowered solutions for intelligent decision making. We'll dive into how each of these strategies and solutions can ultimately build resilience in incident management process. Before we start, we'll quickly introduce ourselves. I am Sophie Soliven and am the director of operations for Edamama. I have over nine years of experience in e commerce, fintech and retail in the Philippines. Over the years I have built my expertise on process management, risk management and everything else in between. I am Joshua Arvin Lat, the chief technology officer of Nuworks Interactive Labs. I am an AWS Machine Learning hero and I'm also the author of three books focusing on AI and security. Here in the screen we can see three books I've written these past three years, machine learning with Amazon Sagemaker cookbook, machine learning Engineering on AWS, and building and automating penetration testing labs in the cloud. Without further ado, I'll hand you over back to Sophie to discuss the relevant concepts to start this session. So imagine running an e commerce web application. The web application has been running smoothly without issues for about a year now, until the customer started complaining that they are unable to check out and complete the payment flow. What if this issue is resolved only after five days? This would mean that five days worth of sales is affected. Here we clearly have what we call an incident. An incident is an unforeseen event that adversely affects a system, service or operation, necessitating a response to mitigate the impact here, these primary objective of the team is to restore normal operations as quickly as possible while minimizing disruption and learning from the incident to prevent recurrence. That said, it is unacceptable to wait for five days CTo resolve such incident. If we can resolve the issue within a few minutes, then better managing incidents can be compared to navigating a labyrinth for several reasons. Just like a labyrinth can have intricate and convoluted pathways, incident management often involves complex and multifaceted situations. Incidents can be caused by a variety of factors, involve multiple stakeholders, and require intricate problem solving. Labyrinths are designed to disorient and challenge individuals by obscuring the path forward. Similarly, incidents often arise unexpectedly and there may be a lack of clarity about the root causes and the best course of action. Labyrinths contain dead ends or paths that lead to nowhere, and incident management can also involve encountering obstacles or dead ends where the chosen approach does not lead to a resolution. In both cases, it requires backtracking and reassessment. Just as individuals navigating a labyrinth may feel a sense of urgency to reach the exit, incident management often comes with time constraints. Rapid response and resolution are essential to minimize the impact of incidents. Is there a way to significantly speed up the incident management process? Yes, there is. We can utilize various automation strategies and tools to efficiently detect and categorize incidents, send alerts to relevant personnel, and even initiate predefined response procedures to mitigate the impact of the incident. By reducing the need for manual intervention, automation enables a more rapid and consistent response to incidents, which can lead to minimize downtime and improve operational resilience. Nowadays, automation strategies may also be aipowered as well. AI, with its ability to learn from data and improve over time, can significantly enhance the effectiveness and efficiency of incident management automation. For instance, AI can help in the rapid identification and categorization of incidents, even predicting potential issues before they occur based on historical data and real time monitoring. Moreover, AI can automate these analysis of incidents to uncover underlying trends and provide insights which can be instrumental in not only resolving incidents more quickly, but also in proactively improving system resilience against future incidents. The fusion of AI and automation heralds a new era of intelligent incident management, enabling organizations to better anticipate, respond, CTO, and learn from operational disruptions. One of the practical applications of AI and automation involves chatbots that can provide immediate responses to common incidents or queries, helping to alleviate the workload on human operators. These AI tools can interact with users to gather initial information about the incident, guide them through basic troubleshooting steps, or escalate the issue to the appropriate personnel if necessary. Additionally, by analyzing past interactions and continuously learning from new data, these chat bots can become increasingly proficient at handling a wider range of incidents, further enhancing the efficiency of the incident management process. Now that we have a better understanding of the relevant concepts such as automation and AI, let's now talk about pragmatic automation. Pragmatic automation emphasizes a balanced approach, CTO automation, by identifying and automating only those specific aspects of a process that yield significant benefits, such as improve efficiency, accuracy, or cost savings. It operates under the understanding that while automation can provide substantial advantages, not every aspect of a process needs to be or should be automated. By carefully selecting which task to automate, organizations can ensure that they are investing their resources wisely, achieving meaningful improvements without overcomplicating their processes or systems. Consider a scenario where a company utilizes a cloudbased infrastructure to host its application and manage its data. The cloud system is set up to generate alerts for a variety of issues such as unexpected traffic spikes, unauthorized access attempts, or system failures. Pragmatic automation comes into play by selectively automating responses to certain types of alerts, for instance, auto scaling resources during traffic surges or blocking suspicious IP addresses after unauthorized access attempts, while leaving other types of alerts for manual review and intervention by the IT team. When automating incident management processes, it's practical to leverage existing tools and technologies rather than building systems from scratch. Optimizing established platforms and software can significantly accelerate the automation process, ensuring quicker implementation and potentially better reliability due to the matured nature of the existing tools. For instance, integrating widely used incident management tools can streamline the automation of notifications solutions and even remediation workflows. Adopting a pragmatic approach by utilizing existing tools also offers advantage of community support along with a wealth of documentation, which can be invaluable in troubleshooting and optimizing the automation processes. Moreover, it can also be cost effective, as it often requires less development time and resources compared to creating a new system from the ground up. This way, organizations can focus on customizing and configuring the automation to meet their specific needs, while also ensuring a robust and well supported incident management process. AI and automation have emerged as powerful tools for enhancing incident management, simulation, and training as well. For example, there are now aipowered tools designed to automation even these penetration testing process. Leveraging modern AI models to conduct this test, these tools can simulate a variety of cybersecurity incidents in a controlled environment, providing a realistic training ground for IT and security teams. Through the simulated scenarios, professionals can gain handson experience in responding to and mitigating potential security threats, thus improving their readiness for real world incidents. Cool, right? Another application and strategy involves AIP powered root cause analysis. AIP powered root cause analysis, or RCA, leverages artificial intelligence and machine learning to identify the underlying causes of problems or faults within a system. Unlike traditional methods, which may rely heavily on human expertise and manual analysis, aidriven RCA can sift through large volumes of data quickly to discover patterns and anomalies that might indicate the root causes of issues. Through machine learning algorithms, it can learn from historical data and, of course, improve over time, making its analysis more accurate and insightful with each iteration. Aipowered root cause analysis not only accelerates the diagnostic process, but also provides a deeper understanding of the system's behavior and the interactions among its components. Of course, even with advanced capabilities of Aipowered RCA, it's essential to have human experts review and assess the final output. Human oversight is necessary to interpret the results within the broader context, make informed decisions based on the findings, and implement corrective measures that resolve the issues effectively and sustainably. Now, let's talk about automated remediation. Automated remediation is a proactive approach towards addressing system vulnerabilities and misconfigurations. Strategies range from basic notification and logging to full automation with advanced methods automation addressing identified issues without human intervention. Amazing, right? Of course, this comes with notable risk, including unforeseen consequences, leveraging CTO system outages, as well as the need for manual oversight due to the intricacies of different systems and application. A phase approach starting with well planned and tested implementation, progressing from basic to more advanced levels of automation, is advised to manage this risk. Maintaining a balance by not overly relying on automation and ensuring human oversight can help in effectively managing these associated risk. While reaping the benefits of automated remediation, automated tagging of corrupted records or rows is a significant leap in managing data integrity issues within databases or data handling systems. By employing various techniques and algorithms, this automation process can also help quickly identify and tag data entries that show inconsistencies indicative of corruption or data integrity issues. This, in turn, significantly reduces the manual effort required, allowing data administrators to maintain high quality data efficiently and prevent future data integrity related incidents. Now we have aipowered monitoring and alerting. Aipowered monitoring and alerting systems leverage AI and machine learning to provide enhanced monitoring capabilities across various domains, including network security, performance monitoring, and anomaly detection. By employing AI, these systems not only enable quicker identification of issues, but also offer predictive insights that can help in preempting problems before they occur. Additionally, they automate the alerting process, ensuring that relevant stakeholders are promptly notified about any critical incidents, thereby facilitating quicker response and solutions. Finally, using AI to accelerate the incident report documentation process not only saves time and resources, but also ensures consistency and accuracy in the documentation process. This is crucial for analyzing incidents and deriving insights for future risk management and mitigation strategies. Moreover, the AI's ability to continuously learn from new data can contribute to the ongoing improvement of the incident documentation process, making it more refined and efficient over time. Instead of spending hours generating an incident report document manually, we may be able to generate something similar within just a few minutes. Cool, right? And that's pretty much it. In this session, we were able to discuss several practical strategies for optimizing incident response workflows and leveraging aipowered solutions for intelligent decision making. By integrating AI and automation into incident management, organizations can significantly accelerate response times, reduce human error, and continuously improve their operational resilience moreover, as these technologies continue to evolve, we anticipate further advancements that will provide even more robust and intuitive solutions for managing and mitigating incidents. The fusion of AI, automation, and human expertise opens up exciting possibilities for enhancing incident management and ensuring smoother and more reliable operations. Thank you very much and have a great day ahead.