I’m thrilled to sit down with Chloe Maraina, a Business Intelligence expert with a deep passion for weaving compelling visual stories through big data analysis. With her sharp expertise in data science and a forward-thinking vision for data management and integration, Chloe has become a leading voice in ensuring AI and cloud infrastructures are not just innovative but also resilient. Today, we’ll dive into the critical intersection of AI adoption and cloud resilience, exploring how organizations can safeguard their operations in an era of rapid technological change, the unique challenges posed by AI-driven systems, and the strategies that ensure stability without stifling progress.
Can you share how the rapid rise of AI adoption—over 75% of organizations using it in at least one function by 2025—has shifted the demands for cloud resilience, and perhaps walk us through a specific instance where this urgency became crystal clear?
Thanks for having me, James. The statistic you mentioned truly reflects the pace at which AI is becoming integral to business operations, and with that comes an unprecedented strain on cloud resilience. I’ve seen firsthand how AI’s dynamic nature—think evolving models and autonomous agents—can expose vulnerabilities in systems that were never designed for such complexity. I recall working with a mid-sized financial services client a couple of years back who rolled out an AI-driven fraud detection system across their hybrid cloud. Within weeks, a misconfigured agent caused a data pipeline glitch that halted real-time alerts for nearly 12 hours. The potential loss of customer trust was palpable in every meeting room discussion, and it underscored the urgent need for resilience tailored to AI’s unpredictability. We tackled this by implementing segmented environments and immutable data backups, ensuring that even if a glitch occurred, we could roll back to a trusted state without losing critical time or data. It was a wake-up call for them—and for me—about how resilience isn’t just a nice-to-have but a business imperative in the AI age.
What lessons should leaders take away from incidents like a rogue AI coding assistant wiping out critical data, and can you walk us through a similar mishap you’ve encountered and the recovery steps you’d advocate for?
That kind of incident is a stark reminder of how AI’s autonomy can spiral into chaos without proper guardrails. The core lesson for leaders is that resilience isn’t just about preventing failures—it’s about containing and recovering from them swiftly. I remember a case with a retail client where an AI tool, meant to optimize inventory forecasts, inadvertently corrupted their primary database by overwriting historical sales data with nonsensical projections. The panic in the team was almost tangible as they faced potential stockouts during a peak sales season. We had to act fast, first isolating the affected systems to stop the bleed, then pulling from an immutable backup we’d set up months prior—thankfully, one that hadn’t been touched by the rogue action. Next, we rebuilt the data integrity in a clean room environment to ensure no corrupted elements slipped back into production, and finally, we retrained the AI model with stricter policy controls. I’d tell leaders to prioritize immutable storage and automated lineage tracking, and to always have a rehearsed recovery playbook ready. It’s not if something will go wrong with AI, but when—and preparation is everything.
How do innovative services like Business Resilience-as-a-Service address the unique challenges of AI workloads, especially around autonomous agentic actions, and can you share a success story that highlights its impact?
Business Resilience-as-a-Service, or BRaaS, is a game-changer because it’s built to tackle AI-specific risks head-on, particularly with agentic actions where AI can make independent decisions that might disrupt systems. It combines deep monitoring, real-time guardrails, and rapid rollback capabilities, which are critical when dealing with AI’s unpredictability. I worked with a healthcare client scaling AI for patient data analysis, and they were grappling with an agentic AI that kept making unauthorized updates to sensitive records—terrifying in a compliance-heavy industry. By leveraging BRaaS, we set up Rubrik Agent Cloud to audit every action in real time and enforce strict boundaries on what the AI could touch. Within a month, we caught and reversed an erroneous update before it impacted patient care, saving the client from potential regulatory headaches. The feedback was overwhelmingly positive; their CTO mentioned feeling a newfound confidence to push AI boundaries knowing there was a safety net. It’s about giving organizations the tools to innovate without the constant fear of a catastrophic misstep.
Of the practical steps for AI resilience, such as inventorying services or automating recovery, which do you see enterprises neglecting most often, and can you share a story where this oversight led to trouble?
I’d say automating recovery workflows is often the most overlooked step, and it’s a costly miss. Many enterprises still rely on manual processes, thinking they can handle disruptions ad hoc, but AI’s speed and scale make that approach untenable. I had a client in the e-commerce space who underestimated this—they hadn’t integrated automated recovery with their incident response systems. During a major sales event, a data pipeline failure tied to their AI recommendation engine brought down personalized offers for thousands of users, and their manual recovery attempt took over 18 hours, costing them significant revenue and customer goodwill. The frustration in their ops team was evident as they scrambled through disjointed tools and outdated scripts. We guided them to prioritize automation by mapping out critical workflows and integrating them with monitoring and identity systems for seamless response. I always stress to teams that automation isn’t just efficiency—it’s survival when AI disruptions hit at scale.
Why is validating recovery in isolated clean rooms so vital for AI systems compared to traditional IT, and could you guide us through a scenario where this made a pivotal difference?
Validating recovery in clean rooms is critical for AI systems because, unlike traditional IT, AI involves intricate dependencies across models, data, and configurations that can hide corruption or bias until it’s too late. In traditional setups, you might restore a server and call it a day, but with AI, a single flawed dataset or misaligned model parameter can derail everything post-recovery. I worked with a logistics company using AI for route optimization, and after a cyber incident, they thought a simple backup restore would suffice. Without clean room validation, they nearly reintroduced corrupted training data into production, which would’ve sent trucks on wildly inefficient routes—imagine the chaos and cost. We set up an isolated environment mirroring their production scale, ran extensive tests on the restored models, validated data integrity down to the smallest feature store, and only then gave the green light for go-live. The process took an extra day, but the relief on their faces when operations resumed flawlessly was worth every minute. It’s about ensuring trust in the recovery, not just checking a box.
How do you help leaders make sense of resilience scorecards for AI, with metrics like detection speed and recovery performance, and can you share a case where these insights drove meaningful change?
Resilience scorecards are invaluable because they turn abstract concepts into tangible metrics leaders can act on—think detection speed or recovery performance by workload tier. My role is to translate these numbers into business impact, showing how a slow detection time might mean lost revenue or eroded trust. I had a tech client whose scorecard revealed their detection speed for AI pipeline failures was lagging at over 4 hours, far below industry benchmarks. This wasn’t just a number—it meant potential customer churn during outages, and the tension in their boardroom discussions was thick as they grappled with the implications. We used this insight to prioritize observability tools that extended beyond basic infrastructure to cover model endpoints and data flows, cutting detection time down to under an hour within three months. I guide leaders to treat scorecards as a living dashboard, regularly reviewing metrics with cross-functional teams to spot trends and allocate resources where gaps hurt most. It’s about making resilience a shared language, not just an IT concern.
Resilience for AI isn’t just about system restoration but ensuring continuity of outcomes—how do you strike a balance between pushing innovation and maintaining stability, and can you share a project where this balance was tested?
Balancing innovation and stability is at the heart of scaling AI, and it starts with embedding resilience as a foundation, not an afterthought. It’s about creating guardrails that let teams experiment while protecting core operations. I worked on a project with a media company launching an AI-driven content personalization engine, and the pressure to innovate was intense—they wanted to be first to market. But midway through, a model update caused inconsistent recommendations, risking user engagement, and I could sense the team’s anxiety as they feared scrapping months of work. We maintained balance by segmenting their environments, allowing safe experimentation in non-production zones while shielding live systems, and we set clear recovery objectives tied to customer outcomes, not just uptime. Regular end-to-end drills ensured we could rollback without disrupting users, and when we launched, customer feedback was overwhelmingly positive, with no hiccups. The strategy is to align resilience with business goals—innovation thrives when teams trust they won’t break what matters most.
As AI adoption surges, embedding resilience into cloud architecture is crucial—what’s the biggest hurdle organizations face in making this shift, and can you detail how you’ve helped a client overcome it?
The biggest hurdle is often a cultural mindset—many organizations still view resilience as a reactive fix rather than a proactive design principle, and that’s tough to unlearn. They’ve built cloud architectures for older compute patterns, not AI’s sprawling dependencies, and retrofitting resilience feels daunting. I supported a manufacturing client who struggled with this; their legacy cloud setup couldn’t handle AI-driven predictive maintenance workloads, and frequent outages were denting operational efficiency. The skepticism from their leadership was almost a physical barrier—change felt risky. We started with a resilience assessment to map their AI services and dependencies, highlighting where fragility threatened outcomes. Then, we redesigned critical components with immutable storage and automated recovery baked in, piloting on a small scale to prove value before full rollout. Over six months, we reduced downtime incidents by half, and their team’s confidence grew with every successful recovery drill. It’s about starting small, showing measurable wins, and building a narrative that resilience enables, not hinders, their AI ambitions.
Looking ahead, what’s your forecast for the future of AI resilience in cloud environments as adoption continues to accelerate?
I believe we’re on the cusp of a seismic shift where resilience will become as integral to cloud architecture as security is today. As AI adoption accelerates, I foresee organizations moving toward fully automated, predictive resilience systems that don’t just react to failures but anticipate them using AI itself—think anomaly detection on steroids. The challenge will be balancing this sophistication with accessibility, ensuring smaller enterprises aren’t left behind. We’ll likely see broader adoption of services like BRaaS, with platforms evolving to offer even tighter integration across hybrid environments. I’m excited, but also cautious, because the stakes will only get higher as AI embeds deeper into critical functions. My hope is that resilience becomes a competitive differentiator, pushing everyone to prioritize trust and continuity alongside innovation.
