Name: Securing the Future: Scaling AI, Sovereignty, and Resilience in ANZ ITOps
Uploaded: 2026-03-12T02:06:07.114Z
Duration: 50 min 8 s
Description: Securing the Future: Scaling AI, Sovereignty, and Resilience in ANZ ITOps

Transcript for "Securing the Future: Scaling AI, Sovereignty, and Resilience in ANZ ITOps": Hello, everyone, and welcome to today's SolarWinds IDC webcast, securing the future, scaling AI, sovereignty, and resilience in ANZ IT ops. We'll just give it a minute for others to join before we begin. As enterprises across Australia and New Zealand accelerate AI adoption, IT operations teams are navigating increasing pressure to balance innovation with trust, sovereignty and resilience. Today's session will explore how organisations are building intelligent, automated, and secured by design IT ops foundations. Before we begin, just a few housekeeping notes. All participant lines are muted. If you have any questions during this session, please use the Q and A function. If you are unable to address all questions live, our team will follow-up after this session. Now it's my pleasure to introduce our speakers. Joining us first is Raul Tabak, regional sales director for SolarWinds Pacific. Rahul works closely with IT leaders across Australia and New Zealand, helping organizations modernize their observability strategies while maintaining control, compliance and operational resilience in increasingly complex hybrid environments. Will set the stage for today's discussion and introduce our IDC insights. Rahul, over to you. Thank you, Emily. Very excited to be here. And I'm also very delighted to welcome Deepika Giri, Associate Vice President at IDC. She leads research across AI, automation and enterprise IT ops strategy in the region. Deepika brings deep regional expertise in AI and automation trends, and she's been closely tracking how enterprises across Australia and New Zealand are building the next generation of adaptive and resilient IT operations. In today's session, she'll share key findings from IDC's latest research covering AIOps adoption, sovereignty priorities, and how organizations are moving from reactive operations to intelligent automated environments. Deepika, we are looking forward for your insights. Thank you very much, Rahul. A very good afternoon, everyone. I'm Deepika Giri, Associate VP for IDC Asia Pacific, focusing on AI platforms and advisory. Today, we'll explore how enterprise observability is becoming critical for building resilience in the AI era, particularly for organizations across Australia and New Zealand. The question many leaders are asking is, how do we balance growth with operational resilience? Let's dive right into it. We'll cover five critical areas in the next twenty minutes. The business case, why observability matters now. Current challenges, modality check for data and complexity. The observability imperative, moving from reactive to proactive. The autonomous operations, how agentic AI is transforming IT ops, the action framework, your roadmap for implementation. This isn't just about technology. It's about competitive advantage in an AI driven economy. Asia Pacific organizations face a fundamental tension, drive innovation by building resilience. Our research shows CEO priorities are remarkably diverse. 48% prioritize productivity and efficiency, 37% focus on innovation, staying ahead of disruption, 25% emphasize sustainable growth, and 20% prioritize risk mitigation. AI is the catalyst that enables both growth and resilience simultaneously. Organizations that master AI driven observability can pursue multiple objectives at once. But what does this mean in practice? Competitive advantage emerges at the intersection of three capabilities: Productivity and efficiency, streamlined operations through intelligent automation, reducing manual effort, and optimizing resource allocation. Resilience, adaptability in the face of risk with proactive threat detection, and rapid recovery. The synergy of these three elements creates sustainable competitive advantage. It's not about choosing one. It's about excelling at all three. Organizations that balance these dimensions can thrive during uncertainty while capitalizing on emerging technologies. We're at a critical moment. The data tells us a very compelling story. 59% of organizations are investing in AI ops to optimize and modernize their infrastructure. 40% are already using AI agents with over 50% planning adoption within a year. And 48% see IT as a top beneficiary of agentic AI. This isn't experimentation anymore. It's enterprise wide deployment. As AI scales, organizations face increasing complexity, operational risks, and cost pressures. Moving from reactive monitoring to proactive AI powered observability that prevents issues before they occur. But to understand where we're going, we need to face where we are today. Despite uncertainty, organizations are safeguarding three critical areas from budget cuts. Data and AI, building a foundation for intelligent decision making. Automation, essential for scaling without proportionate headcount growth, IT operations, critical infrastructure for business continuity. As per IDC research, about 33% measure AIOps success by reduced incident response time, highlighting how automation drives faster and more efficient operations. Where you protect investments reveals where you see strategic value. And these three directly enable business continuity, competitiveness, and sustainable value in a digital first economy. As enterprises accelerate transformation, they face six mounting challenges. Data overload, 30% cite rising volume and diversity as the biggest barrier, especially in hybrid cloud environments. Hybrid complexity, 38 run hybrid environments, intensifying data silos and demanding smarter DevOps and IT ops strategies. Skill shortages, 43% face DevOps and cloud native skill gaps and amplify demand for AI driven automation and upskilling programs. Security and compliance. 48% see this as an AIOps success driver, demanding AI driven frameworks for trust and regulatory alignment. Rising costs, implementation costs, and maintenance expenses strain IT budgets, slowing modernization efforts. Tool sprawl, 39% of enterprises prioritize reducing tool sprawl for efficient, cost effective transformation. These aren't isolated challenges. They compound each other, and data overload plus skills gap plus tool sprawl creates paralysis. This is why an integrated platform approach matters. The journey from raw data to business value follows four critical stages. Data influx, collect diverse IT and telemetry data. Insight extraction, analyze and surface meaningful patterns. Intelligence generation, turn insights into actionable intelligence. Decision making, drive business value and actions. So what really is the gap here? 46% haven't embraced AI ops due to skills, data quality, and cost concerns. 30% struggle with data volume and diversity. 43% identify AI powered root cause analysis as the most value capability. Most organizations are really stuck at stage one or two and are drowning in data but starved for insights. Looking at the evolution of observability, it has really moved through three critical stages. First is reactive monitoring, passive data collection, alert based responses where most organizations really start their journey. The next is proactive intelligence, predictive insights, and business aligned metrics where leading organizations are currently at the moment. The next is autonomous operations, self managing systems with automated resolution. This is where we're really trying to head towards. This is a strategic shift. Modern observability platforms translate technical signals into business APIs, breaking down silos through deep integration with IT service management and AIOps platforms. AI driven observability has evolved from a technical tool to a strategic business imperative, delivering tangible outcomes, uptime, enhanced user experience, IT efficiency, compliance, and innovation capabilities. So what do the numbers say now? 30% struggle with data. 43% identify AI powered root cause analysis as the most valuable capability, 33% measure success by reduced incident responses, and 39% prioritized platform consolidation to reduce tool sprawl. So what is the platform advantage? Organizations are shifting from fragmented tools to unified platforms with built in observability. And this enables standardized automated incident response across environments, faster remediation through intelligent correlation, optimized operations with predictive maintenance, actionable insights from complex cloud native architectures. As AIOps adoption grows, enterprises favor platform based approaches that unified compute, storage, security, networking, and observability into a single cohesive ecosystem. And this consolidation isn't just cost efficiency. It's operational intelligence. Unified platforms see relationships that fragmented tools miss to observe. Regulatory pressures are reshaping AI deployment across the region. MAS, FSMA, APRA, CPS two thirty, DORA, and other regulations drive compliance by architecture. 31 of organizations have strict sovereignty requirements on all sensitive data for AI workloads. Let's look at the three drivers. Regulatory tightening, compliance is becoming mandatory, not optional. Sovereign AI requirements, in country knowledge bases, data residency, compliance as code. Trust and transparency. Building sovereign AI enhances trust through transparent governance. This brings me to our IDC prediction that by 2028, 80% of Asia 2,000 organizations will prioritize AI sovereignty using non public hosting, open technologies, and regional partners for mission critical applications. When we look at readiness, we see three pillars for compliance. Sovereign AI requirements, data residency and explainable AI capabilities, model governance, and full auditability, automated compliance mapping to regional regulations. The next is explainable AI and model governance. Model cards and audit logs for AI decisions, override mechanisms for human review, full traceability for regional inquiries, and transparent and oversight for compliance processes. The next is operational resilience capabilities. Real time monitoring and automated incident reporting, third party vendor risk management with continuous monitoring, resilience testing, including scenario and continuity validation, audit ready evidence and cross jurisdictional compliance. Meeting regulations like APRA, CPS two thirty, and DORA demands integrated platforms that embed compliance, explainability, and operational resilience into its core architecture. So now we're at a defining moment. Agentic AI is transforming IT ops from automation to true autonomy, self managing systems that reason, decide, and act independently. What do the numbers say? 40% are already deploying AI agents across the region. 48% see IT as a top beneficiary of agentic AI adoption. 62% expect autonomous decision making agents to become the defining trend. What does this mean? AI agents enable self healing systems, automated compliance enforcement, intelligent remediation without human intervention. As 59% of organizations recognize, AI agents will shift IT from reactive management to self governing systems. This makes trust and governance essential for success. But how do we get there? This is a four stage progression. We start with reactive management, where we have manual intervention and alert based responses to incidents after they occur. Intelligent automation, AI powered workflows that automate routine tasks and accelerate response times. Proactive intelligence is the next stage. Proactive systems that anticipate issues and prevent disruptions before impact. The next stage is the autonomous self governance, fully independent systems that reason, design, and optimize operations continuously. Let's look at a real world use case. Intelligent monitoring agents predict resource bottlenecks, automatically scale infrastructure, and optimize workload distribution across multi cloud environments at real time without human intervention. We need to know that most Asia Pacific organizations are between stages two and three, and the leaders are pivoting towards stage four. Building resilient IT ops requires three integrated elements, people, process, and technology. Human expertise and continuous upskilling drive trusted AIOps adoption. But the challenge really here is 24% cite inadequate skills development as the greatest risk in 2026. Standardized workflows ensure reliable delivery and rapid incident response. Where do we stand today? 30% have reviewed and automated most IT processes to some degree. Coming to technology, AI driven intelligence enables proactive detection and secure operations. 29% have adopted secure by design principles for unified hybrid multi cloud insights. What are the key outcomes achieved through operational resilience? It is uninterrupted business continuity with zero downtime and seamless user experiences. Future proof agility, rapid adaptation to hybrid multi cloud and AI driven environments. Secure by design, proactive risk management, compliance, and autonomous recovery. Technology alone won't deliver resilience. You need all of these factors working together. About 78% of Asia Pacific organizations have practiced DevOps for over three years now. And now, the focus shifts to intelligent acceleration. Today, 78% practice DevOps for over three years, which has laid a mature foundation. 24% integrate agentic systems across workflows, offering early leadership. Secure first KPIs are shifting towards resilience and AI driven automation. So what is the next frontier? Root cause analysis transforms firefighting into systematic improvement. Container level diagnostics and orchestration insights become super critical. Kubernetes and containerized microservices require unified visibility. AI augmented DevOps transforms IT operations, enhancing efficiency, security, and resilience while enabling faster delivery, continuous monitoring, smarter issue detection, and streamline containerized operations. Here is a strategic shift where 58% of Asia Pacific organizations run true hybrid environments. The evolution is from cloud first to cloud smart. And to do this, we have six strategic pillars. Unified management, single pane visibility and integration across multiple environments, automation and AI, self healing systems and real time agility, data sovereignty, regulatory trust, mobility, and compliance by architecture, resilience and continuity, failover capabilities, and business assurance, edge intelligence, distributed processing, and IoT integration cost optimizations smart spend management and predictive operations cloud repatriation trends 37% cite enhanced security as a key driver for cloud repatriation, with nearly half prioritizing on premises IT automation for daily operations over the next two years. So hybrid isn't really a compromise. It's a strategic choice that balances innovation, compliance, and sovereignty. ANZ is accelerating AI adoption through strong digital trust frameworks, government support, and enterprise led innovation. There are three distinguishing factors here investments targeted, augmented threat intelligence, AI augmented merchandising, and optimization, driven by national AI and cyber strategies. The next is sovereign focus. Enterprises deploy AI across public, private, and hybrid clouds to balance agility, compliance, and sovereignty with federated data platforms. AIOps leadership. 40% of Australian organizations adopt AIOps with platform based stacks, leading the region in autonomous operations maturity. Platform preferences in Australia show that 56% prefer platform based AIOps stacks that unify compute storage, networking, security, and observability, delivering automated incident response with fewer alerts and faster remediation. ANZ's mature technology landscape and focus on sovereign AI position it as a hub for scalable, responsible AI development. Let's look at some of the metrics in Australia. 40% of enterprises adopting AI ops, it's the highest in the region. 41% have already deployed AI agents, which is ahead of APAC average. 51% see IT as primary beneficiary, which is a clear focus on use case. Key capabilities that are being deployed in Australia include auto remediation, self healing systems that resolve issues without human intervention, root cause analysis, AI powered diagnostics that identify problems faster, predictive prevention, proactive issue detection before business impact, real time observability, unified visibility across hybrid environments. Australian enterprises leverage AI agents to enhance resilience, efficiency, and agility across IT ops. Self healing systems transform operations into adaptive, autonomous functions that drive digital first growth and minimize risk. This positions Australia as a regional leader in autonomous operations and provides a blueprint for other markets. Now we come to the essential governance. For business leadership, embrace autonomous intelligence. Leverage AI driven observability to gain unified visibility and accelerate root cause analysis across hybrid environments. Invest in people. Develop a strategic approach that prioritizes continuous upskilling and a culture of adaptability. Skilled agile teams form the foundation of resilience, turn data into advantage, harness AI driven analytics to transform data overload into actionable intelligence, automate workflows, and ensure strong data governance. For IT leadership, it's important to build resilient operations. Adoption of AI driven platforms with built in governance and region specific compliance is important. Prioritize explainable automation to maintain trust. Next is to accelerate intelligent operations to focus on scaling securely and embedding AI across observability, security, and container orchestration platforms like Kubernetes. Optimization of hybrid architectures by adopting cloud smart strategies, integrating AIOps, FinOps, DevSecOps, and edge intelligence to build autonomous ecosystems. The future of ITOps is not just automated, It's autonomous. Organizations that embrace AI driven observability today will lead their industries tomorrow. The question isn't whether to adopt AI driven observability. It's how quickly you can implement it while your competitors are still considering it. And that brings me to the end of today's presentation. I'm happy to take any questions here or offline. Thank you very much. Welcome back, everyone. Hope you have gathered some valuable insights from the presentation early from Deepika regarding the trends around operational resilience and the challenges and how observability powered by AI and automation will empower IT teams on the new resilience mandate. Now, this next segment, we will have Deepika back again, where I will have a conversation around AI, agentic systems and observability from a regional and industry perspective. We will also look at the implementation consideration, whether governance, people, technology, or processes. So let's get started. Welcome back, Deepika. Thank you, Rahul. So, Deepika, we are at a turning point. By 2026, operational resilience has evolved from a best practice to a rigid legal mandate across APAC, from DORA in Europe to APRA CPS two thirty in Australia. Yet a resilience gap persists. You know, nearly half of the IT leaders report unexpected outages despite rating their own resilience as high. Now looking at 2026 landscape and beyond, where does IDC see as the biggest disconnect between a firm's perceived resilience and its actual ability to recover? How does the shift from passive monitoring to autonomous observability close this gap? Yeah, that's interesting. If we look at 2026, the core issue is that complexity has outpaced human capacity. Many boards and CIOs feel resilient because they tick boxes on backup, Doctor, and monitoring. But regulators are asking a harder question. Can you detect, contain, and recover from disruption at real time across hybrid, multi cloud, and SaaS estates? And that is where the gaps really shows up. IDC data from Asia Pacific shows that rising data volume, tool sprawl, and skill shortages, they're the top barriers to modern IT ops. Nearly a third of organizations cite data overload as their biggest challenge, yet almost 40% still operate with fragmented monitoring silos rather than a unified view. In that environment, it's very easy to rate your maturity high on a slide, but still be surprised by an outage that originates in an obscure microservice or third party dependency. The shift from passive monitoring to autonomous observability is fundamentally about closing this gap. Instead of waiting for an alert and starting root cause analysis, AI driven platforms continuously correlate telemetry across applications, infrastructure, networks, and databases. They detect weak signals and often remediate before customers notice. For regulators, that means moving from point in time evidence to continuous proof of resilience. For CIOs, it means fewer blind spots and faster, more predictable recovery when disruption occurs? Look, absolutely. I hear this all the time from CIOs across ANZ and APJ. They have the backups. They have the monitoring tools in place. However, I think there is still this lingering anxiety about the unknown unknowns here. And that's exactly why we built a unified observability platform to solve that specific anxiety as it correlates everything in real time. Because at the end of the day, we want to give the leaders and their regulators absolute certainty, not just a bunch of green lights on the dashboard. You know? Moving on, Deepika, one question I had in my mind is that the IDC report predicts that by 2028, 75% of SOC alerts in APJ will be triggered by AI agents. And as we introduce these digital teammates into mission critical workflows, what are the primary hurdles for trust? How critical is the human in group governance model? Yeah. I think IDC sees Agentic AI emerging as a digital teammate in operations, especially in high volume environments like SOX, where a large share of alerts will be triaged by AI agents in the next few years. But the biggest hurdle really is not the technology. It is trust. Teams will rely on agents only if they can understand why a specific action is being recommended by the agent and whether and how it aligns with policy, risk of appetite, and regulations. From our research, the building blocks of trust are explainability, governance, and clear accountability. Explainability means surfacing the evidence behind a recommendation, the correlated metrics, the logs, the traces, and not just a black box score. Governance means having model oversight, audit trails, and the ability to tune or constrain agent behavior to reflect local regulations such as MAS, XME, or APRA CPS two thirty. And accountability means humans still own the outcome, particularly in regulated industries. That is why human in the loop remains critical in the near term. We do see a progression where agents handle more of the low risk triage and orchestration autonomously, while higher impact changes are supervised, reviewed, and approved by engineers. Over time, as organizations mature, they model governance and gain confidence through real world performance. The envelope on autonomy can expand, but always anchored in transparent, auditable decision making. Absolutely. I think trust is the whole ballgame here, right? If your engineers do not trust the AI, they will simply not use it, no matter how advanced it is. That's the core philosophy behind our AI by design approach at SolarWinds. We're not just building black boxes, we are building true digital teammates. So when our system flags an issue or suggests a remediation, it shows it's working. It gives you the logs, the correlated metrics, and the exact reasoning behind the recommendation. That's amazing. Right? So I think it's about augmenting human intelligence, not replacing it, which is the key point we need to understand here. Moving on to ANZ regions, Ipika. In 2026, operational resilience in ANZ has shifted from a best practice to a rigid legal requirement. We discussed in your presentation we are now in the era of APRA CPS two thirty, which replaces several older standards with a unified focus on protecting critical operation services that simply cannot fail without impacting customers of financial stability. How does the shift from passive monitoring to autonomous observability help ANZ leaders close the gap and satisfy APRA's demand? Sure. In ANZ, regulations like APRA, CPS two thirty crystallize what we call critical operations, services that simply cannot fail without systemic impact. To meet that bar, you need more uptime dashboards. You need end to end observability tied to business services, with AI doing continuous impact analysis and automated response. Autonomous observability, detecting degradation early, isolating root cause across hybrid environments, and triggering preapproved playbooks that preserve service continuity. That is exactly the kind of evidence supervisors are now asking for. In both ANZ, the pattern is to unify observability at the platform and process level, but deploy it in ways that respect local residency, sectoral rules, and sovereignty mandates. That combination of autonomous AI driven operations with sovereign aligned deployment is where we see the most investment momentum over the next few years. That's an amazing point, Deepika. And look, this is a massive priority for us locally as well. APRA CPS two thirty has completely changed the conversations around the data sovereignty here, right? So to support our customers through this, we made a significant investment to host our SaaS platform right here in Sydney, and that means our ANZ customers get world class AI capabilities. However, they never have to worry about their sensitive data leaving the shows, which is huge. Right? So I completely acknowledge that point, and we are kind of committed to the E And Z region in terms of whatever investment is required to be aligned to that. Moving on, Tatika, for banking and financial services and critical infrastructures like airports and mining, digital resilience is now a safety and business essentials. What role does agentic AI play in automating complex tasks like risk quantification or predictive maintenance in these high stakes sectors? Look, in financial services and critical infrastructure, downtime is no longer an IT problem. It's business, it's safety, regulatory risks, and so on. These sectors are dealing with highly interconnected systems from real time payments rails to OTIT convergence in airports, energy, mining, where traditional rule based automation struggles to keep up. Agentic AI really changes the game by specializing in complex operational tasks. In FSI, for example, we see AI agents assisting with continuous risk quantification, correlating transaction anomalies, infrastructure health, and cyber signals to flag exposures before they materialize as incidents. In critical infrastructure, agents can monitor sensor data and IT telemetry, detect early signs of equipment failures and stress, and orchestrate predictive maintenance activities that reduce unplanned outages. What makes this powerful is the closed loop between observability and action. Agents can use rich, real time telemetry to propose or execute remediations, document every step for audit, and feed the results back into the models to improve future decisions. For regulators and boards, that means more consistent execution of resilient playbooks. For operations teams, it's less firefighting and more control, predictable operations in environments where the cost of failure is extremely high. I completely agree, Deepika. In these sectors, think downtime is not just lost revenue. It's it's it's genuinely a safety risk. Right? So I think there has been immense pressure on almost all software companies to, you know, rush AI features to the market since shared GPT came into being. Right? However, we intentionally took our time. We actually spoke with many customers, partners, industry partners like you. We gathered some real feedback. And when we were ready, I think last year at SolarWinds Day, we proudly announced our own AI agent and expanded AI capabilities. We built it to function as a highly reliable teammate in observability, incident response, and service management. I think it'll empower IT professionals to resolve incidents much faster by just, you know, using plain language, what people are used to in terms of generative AI. You can literally ask questions about system health, compare metrics, or just launch a multistep workflow just by typing a natural command as you would talk to any of your AI friends. So AI for sure completely simplifies observability management by allowing our customers to configure and manage their environments directly through that agentic interface. So the SolarWinds AI agent is more than a feature. It's actually the foundation of the new way of working for our customers. Deepika, moving on. We can't ignore the talent shortage we have always in the market. Nearly 70% of the IT professionals say constant firefighting impacts their job satisfaction. How do agentic systems fundamentally change the day in the life of an engineer? Yeah, Rahul. Across Asia Pacific, IDC hears a consistent story, chronic skill shortages and burnout in IT ops and security teams. Engineers spend a disproportionate amount of time on low value work, triaging noisy alerts, jumping between tools, and manually piecing together incidents, which erodes job satisfactions and leaves very little space for strategic innovation. Agentic systems fundamentally reshape the day in life of an engineer by taking ownership of that first response layer. AI agents can aggregate signals from multiple observability tools, group related alerts, run initial diagnostics, and even execute well governed runbooks so that when a human picks up an incident, they see a synthesized picture probable root cause, business impact, and recommended actions. And here, the impact is twofold. First, it reduces cognitive overload, which is crucial for retaining scarce talent and maintaining performance under pressure. Secondly, it allows engineers to spend more time on higher order work, improving architectures, refining resilient scenarios, and collaborating with business on new digital initiatives. In IDC's view, the organizations that use agentic AI to elevate, not replace, human expertise will be the ones that win the war for talent and build more sustainable operations. I also think burnout is incredibly real right now. Teams are just drowning in alerts. Really have to stop treating highly skilled engineers like alarm monitors. By letting our AI handle the first response and gather all the diagnostics, we are literally giving people back their evenings and weekends back. It is amazing how it shifts the entire team culture from just keeping the lights on to actually driving the business forward. So, yeah, thanks for that insight. Moving on to the final segment, Deepika, if you had to give one piece of advice to a CTO in E and Z starting this journey today, what would that be? Well, if I had to distill it into one piece of advice, it would be to start with a unified data and observability foundation and then layer autonomy and governance on top of it. Many organizations we see try to jump straight to AI or agentic AI use cases. But if your telemetry is fragmented across tools and teams, the best of the models in the world will not fix the underlying blind spots. The practical roadmap we see working in Asia Pacific has three specific steps. First, consolidate around a platform that can ingest and correlate data across infrastructure applications, networks, and business services, including hybrid and multi cloud. Secondly, embed governance from day one. Define which actions can be fully automated, which required approvals, and how you will evidence compliance with emerging regulations, like CPS two thirty, DORA, Mass FSMA, and so on. Thirdly, invest in your people, upskill teams on AI literacy, SRE practices, and cross functional collaboration so they can partner effectively with these new digital teammates. And down this way, autonomous operational resilience is not a big bang transformation. It is a series of incremental steps, each one expanding the scope of observability, automation, and trust that over time move the organization from fragile, reactive operations to a more adaptive, self governing state. That is absolutely brilliant advice, Deepika. I mean, you cannot build a smart house on a broken foundation, right? Getting your data unified is step one, and I think we are here to partner with organizations across the region to make exactly that happen. Well, thank you so much for sharing your amazing insights today, Deepika, and more importantly, you to everyone who joined us on this one. Please reach out to the local SolarWinds team if you want to continue the conversation with us. Back to you, Emily. Thanks Rahul. Thanks Deepika. Thank you everyone for joining us today. As AI adoption accelerates across ANZ, resilience, sovereignty and trust are becoming defining capabilities for modern IT operations. We hope today's session has provided practical insight into how AI powered observability can help organizations scale securely while maintaining control and compliance. If you would like to continue the conversation, please reach out to our team. Thank you again, and we look forward to welcoming you to our next SolarWinds webcast.