Hi everyone. Good morning, good afternoon, good evening. Depending on where you are when you are listening to this presentation. I'm Smith Verma and I'm thrilled to discuss how AI powered autonomous systems are revolutionizing incident response for site reliability engineering teams, and how AI is transforming the way the things are done, and by making a significant impact on security landscape. My goal for this session is to explore the challenges of modern infrastructure, the transformation role of AI in incident management, and then provide practical steps to implement these systems effectively. So let's dive into the challenge. In today's digital landscape, SRA, teams are getting overwhelmed with the volume of alerts, sometimes numbering in thousands per minute. Obviously with that many alerts, it's nearly impossible to manually triage and respond to these alerts, especially when mere minutes can mean the difference between stability and cascading failures. The complexity is further amplified by the modern architecture, microservices, containers, hybrid cloud environments. These technologies, while they are powerful, but they introduce interdependencies, that traditional human-centric incident response model struggle to manage even the most skilled teams find it that challenging to maintain system reliability at this scale. Let's try to understand this with a hypothetical situation. When a microservice in a contra containerized environment starts exhibiting latency, the single issue can ripple through dependent services leading to widespread degradation. If we were to manually identify and mitigate such an issue between thousands of alert, we can compare the situation to finding a needle in a haystack. So now let's talk about how we can better the situation. So enter AI powered autonomous systems, a paradigm shift in incident management. These system use advanced pattern recognition to detect anomalies across vast and disparate data sets. Identifying subtle correlation that is undetectable to human operators in real time. Beyond the detection, these systems excel in intelligent classification, assessing threats in within the context to prioritize response effectively, they can execute predefined playbooks at machine speed, ensuring that there is rapid mitigation of incidents. Moreover, these con, the continuous learning capabilities mean they evolve with each incident, be becoming more experienced over a time. So let's take an example. Imagine an AI system that detects unusual pattern of database queries, which indicates a potential SQ injection attack. It classifies the threat, it blocks the malicious IP and alerts the security team. All within seconds, isn't it? Just awesome. Now, this doesn't end here. The implementation of these AI powered autonomous system, the yield amazing results. So let's look at them closely. Let's talk about meantime to resolution reduction. We have seen that organizations have reported significant decrease in meantime to resolution with some achieving even 60% over 60%. This acceleration in resolution time minimizes downtime and enhances user satisfaction. Now let's talk about false positive. These advanced AI systems, they have achieved a false positive rate as low as 5%. Ensuring that alerts are both accurate and actionable. These precision reduces alert fatigue and allows teams to focus on genuine threats 24 7 coverage. So unlike human operators AI systems, they can provide continuous around the clock monitoring without fatigue. Ensuring that these incidents are detected and addressed promptly at any hour. And last, but not the least, the return on investment, the efficiency and accuracy of these systems, they translate to a substantial ROI, with some organizations even witnessing a 95% return within the first year of implementation. These metricses underscore the profound impact of integrating AI into incident response workflows. Now imagine a situation where a financial situa financial institution, they implement an AI driven incident response system and observe a 60% reduction in MTTR, meantime to resolution reduction and enhancing their service, availability and customer trust. Wouldn't it be just great for their business? Modern autonomous systems, they're equipped with sophisticated response capabilities that were once the domain of seasons human experts, network segmentation, automatically isolating Compromise network segments to prevent lateral movement of threats effectively containing the potential breaches. Container isis isolation, immediately quarantine the suspicious containers while maintaining service availability through redundant in instances, ensuring that malicious activities occurred without disrupting operations dynamic resource allocation. Intelligently redistributing compute resources to maintain critical service performances during incidents, optimizing resource utilization and ensuring service continuity. Automatic rollbacks, just instantaneously reverting to known good configuration when deployment. Trigger performance degradation, minimizing the impact of faulty deployments. And the key advantage, it lies in the speed. These autonomous systems, they operate at a machine timescales often containing and mitigating threats within seconds. Example take an event of detected malware outbreak within a container. The system can automatic autonomously, isolate the affected container, deploy a clean instance, and reroute traffic seamlessly. That's such a great experience. Now let's try to dive deep into case studies. Let's examine a real world incident of preventing cascading database failures. So in t plus zero seconds an anomalous query pattern is detected in the primary database cluster in t plus 1.5 seconds. The AI system classifies this as a potential resource exhaustion event. In t plus three seconds. An automatic query is throttling, tottering is activated for noncritical services to alleviate load in plus six seconds. The re traffic is rerouted to secondary replicas to distribute the load effectively. And then in D plus 30 seconds, an alert is generated for the SRE team with comprehensive diagnosis diagnostic data. So in this scenario, the autonomous system identified and mitigated the threat within seconds, preserving the system's availability and preventing what could have been a major service. Outage affecting millions of users. And if you were to think of this in a real world scenario, consider an e-commerce platform during a peak sale event. An unexpected surge in database queries could overwhelm the system. So an AI driven response can detect the search, throttle the non-essential queries, and maintain service continuity. Now let's take another case study. Mitigating a zero day vulnerability. So in another instance, a previously unknown vulnerability was exploited in a web application framework. So a couple of things here. We had behavior anomaly detection. So the AI system, it identified unusual process behavior across multiple instance indicative of a potential compromise. Traffic pattern analysis. So they were able to co correlate these anomalies with suspicious network connection, strengthening the suspicion of malicious activity. Next, in the containment, the isolated affected the isolated, the affected containers. And implemented traffic filtering rules to prevent further exploitation. And then forensic package creation. They generated comprehensive forensic data for the security team to analyze and address the root cause. The rapid autonomous response that, that contained the attack before the sensitive data was exfiltrated showcasing, showcases the critical advantage of behavior based detection over the traditional signature based approaches. Now, if I were to think about this from a real world example think of a tech company which has faced a zero day exploit targeting their web servers. An AI driven system would be able to detect irregular server behaviors, isolate the compromised instance, and provide detailed logs for forensic anana analysis effectively neutralizing the threat. Now let's talk about implementing challenges, implementation challenges, and solution. So implementing AI powered autonomous incident response. Is not as easy as, plugging in us on a smart system and forgetting about it. So it requires thoughtful planning and problem solving. So let's just talk through the top three challenges that we have seen that businesses face and what are the strategies to overcome them. So the first one is algorithm reliability. So the toughest challenge is trust, right? These systems, they are making decisions during high stake incidents. What happens if the model faces something it has never seen before? So we can mitigate this by introducing confidence scoring. If a model is uncertain, the system will es escalate the incident to a human rather than guessing. Additionally, we continuously retrain models with fresh incident data and regularly run adversial test to make sure that the system is resilient to the edge cases. Next is integration complexities. So most companies, they don't have a single source of monitoring truth. Instead, they use a patchwork of tools across different environments. We address this by building standardized API adapters for all major observability and logging platforms. We also roll out integration in read-only observation mode at first. This gives the team a chance to evaluate the AI performance without risking the unintended actions and the third piece is human oversight. So we don't want to replace SREs. We want to free them from the noise so they can focus on what matters. We apply a tired tiered au autonomy framework. So systems starts with minimal authority and gradually earns trust as they prove themselves. Everything is auditable and the teams always retain the ability to override or reverse autonomous decisions. The challenges are real, but with the right strategies, they are solvable. Next, let's talk about maintaining observability and transparency. So transparency is the foundational requirement. SREs doesn't, don't understand how a system made a decision. They will never trust it no matter how accurate it's, so we use three pillars to maintain the transparency. The decision tracing, which means that every automate automated action is logged in full details, including the input signals, the confidence scores, and the logic or the model that is used. This enables SREs to trace any incident postmortem and understand exactly what the system saw and why it acted that way. Next is explainable ai. So we have implemented visual tools that break down complex decisions into digestible human readable formats. This might include a heat map of signal anomalies, a decision tree visualization, or an incident replay that shows how the event unfolded. These tools built trust and they also help identify areas to refine the ai. And third human AI collaboration interfaces. So we don't want AI in a black box. Our dashboard will surface every autonomous action, explain its relational rational, and allow one click override or adjustments. This creates a partnership between automation and human judgment. The system handles routine noise and the SREs focus on strategy and edge cases. Ultimately, our goal is to create AI that's just not powerful, but understandable and accountable. Now let's talk about implementation roadmap. So let's walk through the roadmap of rolling out autonomous incident response in a safe and scalable way safe. So this is a blueprint that we have successfully used in enterprise environments. So the first phase is the assessment phase. So here we start by cataloging incident types and current response workflows. So look for patterns where, you know high volume, low risk events that could be safely automated. Next, monitoring enhancements. So before we automate anything, we need visibility. So this phase will focus on improving telemetry. Adding richer signals, reducing noise, and tagging data for better context. Then passive learning mode. So deploy AI systems in observation only mode. They watch incidents, they make predictions and generate recommendations, but they don't take any actions yet. This phase is critical for trust building. Teams can compare the system's suggested responses against actual outcomes, and start to validate the value of the AI supervised AU automation. So once the confidence is high, begin allowing the system to take actions, but only with human approval. This is like supervised driving for ai. SRA still have a hand on the wheel. And then last step is full autonomy. So as a system earns trust, expands its expand its au autonomy for, understood incidents. So the goal is not to automate everything at once, but to scale up and safely and progressively. So each phase is designed to manage risk while building confidence and capability. So let's talk about key takeaways and next steps and wrap up with that. So one, speed is critical. So in complex systems, every second counts and autonomous systems operate at machine time scales containing incidents before human could even react. And that's how we prevent outages and protect customers. SM start small and scale fast. So you need, you don't need to boil the ocean. Start with narrow, understood use cases. Maybe just automate responses to disc space alerts. And as the system proves itself, expand to broader incident types. And then human AI partnership. So this isn't, remember, this isn't about replacing sre, this is about elevating them. Removing repetitive firefighting so that they can focus on architecture, reliability, and innovation. And the AI becomes a force multiplier. Measure everything like, from false positive rates to time to resolution Metrics are essential for demonstrating that ROI and guiding continuous improvements. If you can't measure, you can't trust it, you can't improve it. So measurement is very important. Next steps identify high volume incidents. Deploy AI in observation mode, build trust before granting autonomy and focus on transparency and audit. Auditability autonomous incident response is in the future. It's the present and it's how we scale SRE to meet the complexity of systems. To wrap up. I wanna say autonomous instant response isn't just a tech upgrade, it's a strategic shift in how we protect, scale and operate our systems. It's about moving from reactive firefighting to proactive resilience. And with AI on our side, we no longer are limited by human speed or scale. So if there's one message that I want you to take. With you today. It is that smart. Start small, start safely, but start now. The future of SRE is here and it's autonomous. Thank you very much for your time. I'm very happy to take questions or chat more, so feel free to reach out to me and once again, thank you a lot. Thanks a lot for your time today.