Hey, good morning. Good afternoon everyone. My name is Prateek Dhantra Day. I'm a principal software professional working with one of the leading human capital management companies in the US. And I'm active researcher in the field of DevOps, artificial intelligence, machine learning and security. I'm excited to be here at CoN 42 observability conference to discuss the important topic today of forging secure and observable DevOps using AI ML. And as we know that DevOps is growing exponentially and in parallel it raises a concern to maintain the security and the observability. It's becoming a challenge today and but the integration of artificial intelligence and machine learning is a true game changer to help in the DevOps. And today in my presentation I'm going to talk about more into this detail. So let's get dive into my presentation. So let's start with the DevOps evolution. DevOps has revolutionized how we deliver the software, breaking down silos between the development and operations. It's enabled us to deploy much faster and more frequent with this greater reliability. But as the system scale and it becomes more distributed, the traditional DevOps practices are being stretched to their limits. Today, the DevOps environments are spread ecosystem like microservices, serverless functions, containers and cloud services all interacting in the integrate way. And this complexity makes it daunting to maintain the comprehensive visibility and ensure the robust security. So question should come like how do we monitor each and every component and how we detect the threats across this dynamic landscape. This is where the power of artificial intelligence and machine learning comes into picture. And this reshape our approach to DevOps. So by harnessing this AI ML, we can elevate our observability and threat detection capabilities to match and scale the dynamism of the modern software system. Now let's talk about the observability. So in today's DevOps world, we are generating lot of data in terms of logs, matrices, traces from the countless services. All the significant amount of data are produced every second. And this is the place where AI shines. And machine learning models can identify these patterns and go correlations that human analysts would miss. And take a anomaly detection for instance, like traditional rule based method often fails in the dynamic environment. But machine learning models can learn what's the normal across various metrics and flag deviation in the real time. This is not just about the cpu spike, it's about detecting multidimensional anomalies like unusual API call patterns that might indicate a data bridge or a bad actor. Take your data so AI can transform this root cause analysis. When an incident occurs, it's often a complex chain of event. The machine learning techniques like casual interference and the graph analysis can navigate this complexity, tracing issues and back to the source. Imagine quickly pinpointing this database. Slowdown is due to the network misconfiguration are like three hops away and that's the power of AI driven observability. Now let's talk about security. So in DevOps, speed cannot come at the cost of safety. Yet in the traditional security models like with their fixed rules and manual processes struggle to keep the pace. But this is where the machine learning becomes DevOps security multiplier. Consider a threat detection ML model excel identifying this subtle like evolving the attack patterns. By analyzing vast data set of the network traffic systems, logs and user behaviors, this model can spot signs of intuition that signature based tool might have missed and they adopt this models adopt in the real time and learn the new threat indicators as they emerge. And AI also transform like vulnerability management. In this world of continuous deployment, your attack surface is always changing and machine learning algorithms can continuously scan your infrastructure assessing like new components predictive do. They do predictive analysis to forecast like which part of your system are most likely to be targeted. And this is how help the organization to prioritize their defenses. Another game changer is AI driven threat modeling. Traditionally this is a manual and time consuming task. But now like we have a machine learning system that can automatically map your application architecture, identify data flows and pinpoint the potential attack vectors. They even suggest mitigation based on the pattern learned from the countless other systems. Now let's talk about the two most important practices like intelligent monitor and genetic psychops research. So let me start with the genetic algorithm. So this is one of the most exciting innovation in the application of genetic algorithm in secops like genetic algorithm inspired by the natural selection which are the powerful tool for the optimizing problem. Like in our work. Like if we are using them to enhance our automated security testing drastically. First like we are using this genetic algorithm to generate diverse and challenging test cases. Just as a genetic variation in nature leads to the adaptability our a genetic algorithm creates a wide array of test scenarios and from unusual API call which sequences to complex race conditions and this diversity helps to uncover vulnerabilities that predefined the test suite might miss. But this genetic algorithm truly shine in the optimizing our vulnerability detection tools. False positive and negatives are the major pain points in security testing. A high false positive rate overwhelmed teams while false negative live system exposed. So each algorithm parameter things like pattern matching rules or anomaly threads are this like genes. We run this algorithm against a data set of known code or known backbone sample and those that correctly identify vulnerabilities while minimizing the false alarm which are considered fit. And through the process of mirroring, crossover and mutation, we evolve this algorithm over thousands of generations. And the results are really remarkable. Like our genetic algorithm optimized tool show a 30% reduction in false positive and the 25% improvement in detecting the noble vulnerabilities compared to old solutions. This means our devsecops, which is like a dev operations with security spend less time on noise and have a greater confidence in the security posture. Now let's discuss about the intelligent monitor. So while observability gives us the visibility into the system behavior, the future is intelligent monitoring, where AI doesn't just watch, but understand and act. This engine uses the deep learning techniques, specifically like long short term memory, which is LSTM networks, to understand the narrative of your system operations. So this LSTM are excellent at learning the sequences, which makes them perfect for grasping the flow of the event in the distributed systems. And we use application tools like new relic app dynamics, which have the stream of logs, metrics and traces. But our LSTM models learn the story, the events of those logs, and based on the past incident. And now this new telemetry comes in. This engine doesn't just see the data points, it comprehends the unfolding narrative. So understanding is only the half battle. But cognitive ops engine also uses the gen AI, just like GPT-3 to, you know, create the responses. So when it detect an emerging issues, it just doesn't alert it. It generates a tailored playbook, like a natural language for a potential database deadlock. It might say. I'm seeing a pattern similar to enrollment search like last year, considering raising a connection limit and retrying transaction. And here is the exact command, something like that. It's like a generative AI response. So moreover, like our engine continuously refines its knowledge using a technique called experience replay. From reinforcement learning, it revisits the past incidents, learning which actions were most effective. And so it's not just monitoring, it's. It's an AI operation expert that grows wiser with every challenges that it faces. So now let's talk about the artificial intelligence in the devsecops lifecycle. So as we embrace like AI in the DevOps, we must navigate challenges thoroughly. Like ML models are only as good as their training data, but biased or incomplete data set can lead to missed threats or false positive. So we need diverse and well curated training data to represent the full spectrum of this DevOps scenarios. So there is also a black box problem. Like many AI system, especially like deep learning models, they are like OpEC in the decision making. In the security critical Devsecov task, the lack of explainability is concerning. We must prioritize interpretable AI techniques that show their reasoning or and that's how you're building the trust with the DevOps teams. So ethical consideration also loom large here. So AI power to analyze a system and user behavior raises a privacy concern. So we must implement techniques like differential privacy to ensure our observability tools don't compromise personal data. Furthermore, like AI makes autonomous decision in our DevOps pipeline and we need to robust governance to ensure it aligns with our organization values and it does not harm any reputation of the organization. So now let's talk about the DevOps devsecops best practices. So now we stand at this juncture. Like the fusion of AI ML with the DevOps is not just promising, it's becoming essential. Like in this era of cloud native global distributed system, maintaining security and observability is like too complex for a traditional methods. But AI and ML provide this capability and adaptability and it insights that we need. But like this journey also requires continuous learning and collaboration. Like DevOps professional must upskills in the AI ML concept and data scientists must understand the DevOps workflows, security expert must guide the ethical application for these technologies and cross functional teamwork is also our path to the DevOps future, you know. So in conclusion, I would say the integration of AI and ML learning into DevOps is not a distinct version, it's like an evolution of happening now. And this genetic algorithm optimizing our security tools to LSTM understand our system narratives. So there are some challenges that we need to navigate like data quality, model interpretability and ethical use. But potential rewards are immense. Like DevOps environment are self monitoring, self defending and it aligns with the human values. So to further extend this like maybe if I would like to put like three points, significantly expand expanding your key research area. One should be like devsecops which is like added concrete examples of your AI driven devsecops platform showcasing how it uses NLP and reinforcement learning to enhance the security without slowing down the development. The second is like genetic algorithm which is like elaborated to your use of GA like genetic algorithm in Sec Ops, explaining how you use them to generate diverse test cases and more importantly to evolve from accurate vulnerability detection algorithms. Lastly, like we need the intelligent monitor which introduced our cognitive Ops engine which uses long term short term memory network to understand the system behavior, narratives and the generative AI to provide the human readable responses. So all of this additions not only just provide the technical depth, but also highlight applying this edge cutting AI to DevOps technology and collaboratively like we can shaping a secure digital future. So that's all I have. Thank you.