Building resilience for AI workloads in the cloud

In 2025, more than 75% of organizations have reported using AI in at least one business function, according to McKinsey’s latest Global Survey on AI.

AI has moved from pilots to production and now powers decisions, customer experiences, and compliance processes, raising the stakes for resilience. Outages, data corruption, or misconfigured agents can interrupt critical workflows, erode customer trust, and trigger regulatory scrutiny. Cloud platforms have become the backbone for AI workloads, offering elasticity and scale, yet many resilience programs were designed for older compute patterns.

But as AI adoption accelerates, cloud environments have evolved from simple compute and storage layers to sprawling ecosystems of data pipelines, model registries, orchestration tools, and agentic processes. The complexity demands resilience strategies that go beyond traditional recovery, ensuring rapid restoration of operations.

Why AI changes the resilience equation

AI amplifies the challenge of resilience. Data and infrastructure sprawl across hybrid and multi-cloud estates creates intricate dependency chains. Models evolve continuously, and autonomous agents can trigger unintended changes that ripple through systems. Traditional backup cannot guarantee a safe recovery point for these dynamic interactions.

Resilience begins with clear segmentation of environments, robust identity controls, and immutable copies of critical data. Observability must extend beyond virtual machines to include pipelines, model endpoints, and orchestration layers. Recovery should be validated in isolated environments to prevent hidden contamination from re-entering production. Automation is essential to reduce recovery time and ensure consistency across regions and providers. What organizations need is resilience that combines immutable backups, automated lineage tracking, and clean rollback to ensure that recovery is fast, accurate, and trusted.

A recent example highlights how an AI coding assistant at a tech firm went rogue and wiped out the production database of SaaStr, a startup, during a code freeze. The AI not only deleted critical data but also generated fake users and fabricated reports, making it difficult to identify a clean recovery point. The rogue AI action underscores how autonomous AI actions can cause cascading failures and why organizations need advanced resilience strategies.

Cognizant and Rubrik: A partnership for AI resilience

Cognizant and Rubrik deliver Business Resilience-as-a-Service (BRaaS), an offering for organizations scaling AI in the cloud. BRaaS leverages Cognizant’s global delivery capabilities and cloud infrastructure expertise, alongside Rubrik’s advanced cyber resilience platform. Together, they help address the need for AI workloads to have resilience controls that address the full lifecycle.

Rubrik Agent Cloud is designed to monitor and audit agentic actions, enforce real-time guardrails for agentic changes, fine-tune agents for accuracy, and undo agent mistakes. Built on the Rubrik Platform that uniquely combines data, identity, and application contexts, Rubrik Agent Cloud gives customers security, accuracy, and efficiency as they transform their organizations into AI enterprises.

Comprehensive controls over data, orchestration, and recovery can further an organization’s confidence in AI. Cognizant’s Neuro^® AI platform features multi-agent orchestration with embedded policy guardrails operating across protected data estates.

Together, these capabilities support safe experimentation while shielding core business operations from risk. Cognizant and Rubrik aim to protect the foundation for the agentic AI era, where trusted data and rapid recovery are essential — helping organizations gain the confidence to innovate with AI, knowing they can quickly and safely undo any destructive agent actions and maintain business resilience.

Practical guidance for enterprise teams

Leaders can strengthen AI resilience with eight practical steps:

1. Inventory AI services and dependencies across models, pipelines, data sources, vector stores, orchestration tools, and consuming applications.
2. Tier AI workloads and set recovery time and point objectives that match customer and regulatory expectations. Include model registries, feature stores, and prompt libraries in scope.
3. Protect trusted data with immutable storage and frequent, policy-driven snapshots. Guard gold datasets and production feature stores as crown jewels.
4. Validate recovery in isolation using clean rooms that mirror production scale. Confirm that models, data, and configurations work together before go-live.
5. Automate recovery workflows and integrate with incident response, service management, monitoring, and identity systems for coordinated action.
6. Harden identity and access with zero trust principles, short-lived credentials, and strong separation of duties for AI platform operations.
7. Run end-to-end exercises that include technology, security, data, and business owners. Rehearse cutover, rollback, and communications. Close gaps with time-bound plans.
8. Track a resilience scorecard for AI, including detection speed, isolation time, recovery performance by tier, validation frequency, and control drift.

By following these steps, organizations move beyond reactive recovery to embed resilience into AI operations. Proactive planning, rigorous validation, and continuous measurement ensure that innovation does not come at the expense of stability or trust. With the right safeguards in place, enterprises can scale AI confidently, knowing they are prepared to withstand disruptions and protect both business value and customer trust.

Leadership driven by insights and outcomes

Resilience is about continuity of outcomes, not only restoration of systems. When AI services remain trustworthy during a disruption, customers stay served, regulators see control, and teams can resume work without guesswork. Predictable recovery also builds confidence to scale AI programs. Leaders can allocate budgets more efficiently when recovery targets and costs are clear. Measurable progress shows up as faster mean time to recover and fewer failed cutbacks.

Conclusion: Innovate with confidence

AI adoption will continue to accelerate. Organizations that embed resilience into cloud architecture and operating models will move fast and with fewer surprises. Cognizant and Rubrik provide the platform, delivery scale, and service model to make that shift attainable. The goal is simple: keep data trusted, restore services cleanly, and validate outcomes before going live. With this foundation, AI becomes a growth engine that leaders can scale with confidence.

Take the next step towards resilient AI innovation. Contact Cognizant to assess your current posture, explore tailored Rubrik solutions, and discover how to safely scale your AI initiatives on a foundation of resilience and trust. To schedule your resilience assessment, get in touch at BusinessResilience@cognizant.com or click here to learn more.

About Sriramkumar Kumaresan

Sriram Kumaresan leads the Global Cloud, Infrastructure and Security practice atCognizant, overseeing approximately 35,000 professionals. With over 25 years of experience, he excels in building and scaling businesses from strategy to execution. Sriram is responsible for driving market share (strategy, GTM and growth) and mindshare (offering, partner strategy and market positioning) through strategic approaches, customer centricity and the deep technical expertise inCognizant’s Cloud, Infrastructure and Security business. Beyond his professional achievements, he is also a mentor and advocate for diversity in tech, aiming to inspire future IT leaders.