753 From outages to AI failures, 2025 revealed exactly where infrastructure struggled and where it held up. AI workloads exposed data pathways that couldn’t keep pace. New sovereignty rulings made jurisdictional control a measurable requirement. Cloud outages highlighted operational and economic concentration risk. And cyberattacks continued to test whether resilience strategies actually worked under real pressure. As organizations prepare for 2026, the focus has shifted from adding more tools to strengthening the core architecture that governs data integrity, mobility, and recovery. Storage now sits at the center of that equation, where security, compliance, and performance converge. This Data Readiness Scorecard distills the past year’s most important signals into two categories: Green lights indicate your data foundation is ready for the year ahead. Red flags show where hidden risks in control, resilience, or governance remain. The goal isn’t to predict the 2026 trends that will shape data infrastructure. It’s to help IT leaders diagnose where their architecture stands today and where reinforcement is needed. Download a copy of the 2026 data readiness scorecard checklist here, and read on as we get into the details. Green lights for 2026 1. AI readiness supported by high-throughput, governed data pipelines Why it matters: AI outcomes are constrained by data pipelines, not model ambition. In 2025, many organizations discovered that GPUs sat idle while storage systems struggled with throughput, metadata concurrency, or small-object access. Others discovered that without a clear data history, AI results couldn’t be trusted or validated. The teams that progressed fastest invested in pipelines designed for scale, concurrency, and governance, not just raw performance. 2025 news signal: AI failures trace back to missing data lineage MIT Technology Review: “AI models fail in unpredictable ways when organizations cannot trace or verify the data used to train them.” You are AI-ready if your environment supports: Fast, predictable data delivery to maximize GPU utilization High metadata concurrency and rapid small-object access Reproducible pipelines with end-to-end lineage Efficient management of billions of object variants Policy-driven mobility across fast, warm, and cold data tiers This is no longer advanced architecture. It is the baseline for reliable AI. 2. Deep immutability from API through architecture Why it matters: AI-accelerated ransomware exposes how quickly attackers adapt. Surface-level immutability is no longer enough. Clean, verifiable recovery requires immutability engineered into the storage architecture, not applied as a configuration toggle. Organizations embracing deeper resilience stayed ahead of both threats and insurance requirements. 2025 news signal: AI-generated ransomware surges Wired Magazine: “AI-assisted malware is now probing APIs and identity systems directly, accelerating the speed and sophistication of attacks.” Your environment is resilient if it includes: API-level retention enforcement System-level immutability that cannot be bypassed Cryptographic integrity or lineage verification Automated clean-restore testing A multi-layer cyber-resilience model aligned with CORE5 principles 3. Sovereignty-driven design with real control Why it matters: 2025 marked a decisive shift: sovereignty became operational, not conceptual. A court ruling involving French cloud provider OVHcloud made it clear that contractual data residency alone does not satisfy jurisdictional requirements (see below). Enterprises now need proof of where data lives, how it moves, and who controls it — especially in Europe, the Middle East, and regulated industries. 2025 news signal: Canada court vs. OVH The Register: “The court ruled that storing data in foreign regions under a Canadian contract still violates residency requirements.” You are sovereignty-ready if you support: Customer-owned encryption keys Region-locked placement aligned to jurisdiction Audit-ready lineage and data movement history Avoidance of hyperscaler concentration risk Compliant data mobility across cloud, on-prem, and edge 4. Cloud-smart resilience that reduces concentration risk Why it matters: Outages and cost volatility exposed the fragility of single-cloud dependence. Resilient organizations now design for independence, ensuring availability and control even when a major provider is disrupted. 2025 news signals: Cloud concentration recognized as a top global business risk Gartner Group: “Gartner ranks cloud concentration among the top five emerging business risks worldwide.” AWS outage exposes single-region fragility BBC News: “Those who had a single point of failure in this Amazon region were susceptible to being taken offline.” Your architecture signals cloud-smart resilience if it supports: Workload mobility across cloud, on-prem, and edge Alternative access paths to critical datasets Recovery operations that remain online during outages Workload placement based on performance, cost, and compliance Governance of security and configuration outside the cloud provider’s control Review Scality’s Cloud-Smart Resilience Checklist for additional details on the five principles listed here. 5. Multiscale, disaggregated architecture that adapts under pressure Why it matters: AI, analytics, and multi-tenant environments stress infrastructure in different ways at the same time. Traditional, tightly coupled systems hit tradeoffs that stall scale. Disaggregated, multiscale architectures enable independent scaling of compute, capacity, metadata, and transactions, making them essential for today’s unpredictable data demands. You can learn more about disaggregated and multiscale storage architecture in this 15 minute primer on the Jon Myer podcast: 2025 news signal: Industry pivots toward disaggregated designs TechTarget “Storage vendors are racing to deliver independently scalable compute and capacity as data growth and AI workloads expose the limitations of traditional designs.” You are ready for unpredictable growth if your architecture: Scales compute, capacity, and metadata independently Maintains predictable performance under mixed workloads Expands elastically across nodes, racks, or sites Handles massive object and bucket counts without degradation Stays operable and efficient at scale Read this Multiscale Architecture primer to understand the dimensions of scaling needed to power modern workloads. Red flags for 2026 1. Vendor lock-in that restricts data movement Why it matters: In 2025, outages and pricing shocks made one thing clear: dependency is risk. When data is trapped in proprietary formats or tightly coupled platforms, organizations lose the ability to respond whether to an outage, a compliance requirement, or a cost spike. Portability is no longer an optimization. It is a prerequisite for resilience. 2025 news signal: Microsoft outage raises dependency concerns BBC News: “A widespread outage took Microsoft services offline for hours, disrupting customers around the world and renewing concerns about dependency on a single cloud ecosystem.” You may be exposed to platform risk if your environment shows: Proprietary formats that restrict data portability Applications that require redesign to migrate Recovery paths tied entirely to cloud availability No independent environment for access or failover What to do about it: Adopt open, S3-compatible storage that works across cloud and on-prem Design for mobility with global namespaces, replication, and consistent APIs Separate compute and storage to avoid platform entanglement Maintain at least one fallback environment outside any single provider 2. Unverified recovery SLAs Why it matters: In 2025, many organizations learned that having backups was not the same as being able to recover. Recovery plans that had never been tested under real conditions failed when they were needed most. If recovery cannot be proven, it cannot be trusted. 2025 news signal: Cyber insurers tighten requirements for coverage Reuters: “Insurers are increasing scrutiny of backup and recovery controls and requiring evidence that organizations can restore operations quickly after an attack.” You may be exposed to recovery risk if your environment shows: Backups that are never tested end-to-end Immutability applied only at the bucket or policy level Recovery paths dependent on cloud availability No cryptographic validation of restored data What to do about it Automate clean-restore testing on a regular schedule Implement deep immutability at both API and architectural layers Ensure at least one recovery path is independent of cloud availability Track “time to clean restore” as a measurable resilience metric 3. Data lineage gaps that undermine AI reliability Why it matters: As AI systems move into production, trust in outputs becomes as important as performance. In 2025, multiple failures were traced back not to models, but to missing or incomplete data lineage. Without lineage, AI outcomes cannot be reliably explained, reproduced, or defended. 2025 news signal: AI failures traced back to missing data lineage MIT: “AI models fail in unpredictable ways when organizations cannot trace or verify the data used to train them.” You may be exposed to AI reliability risk if your environment shows: Untracked dataset versions or transformations Shadow corpora created outside formal pipelines No audit trail for training or inference data Manual or inconsistent preprocessing workflows What to do about it: Store all AI training and inference data in lineage-aware object storage Enforce versioning for datasets and logic Automate and document preprocessing workflows Apply governance controls for access, retention, and change history 4. Cloud risk driven by concentration and cost volatility Why it matters: Cloud concentration emerged as a strategic risk in 2025. Organizations dependent on a single provider faced disruption, unpredictable cost exposure, and limited leverage. Resilience now depends on choice and the ability to act on it. 2025 news signal: Cloud concentration ranked among top emerging business risks Gartner Group: “Many organizations are now in a position where they would face severe disruption in the event of the failure of a single provider.” You may be exposed to strategic cloud risk if your environment shows: Reliance on a single hyperscaler for core workloads Limited portability or data mobility No on-prem or alternative recovery option Inability to move workloads during cost or availability events What to do about it Design architectures for portability from the outset Implement hybrid or multi-cloud strategies Separate compute and data tiers to avoid lock-in Ensure data can replicate or move outside a single provider quickly 5. Unbounded data sprawl Why it matters: AI adoption accelerated data creation and duplication in 2025. Without governance, this sprawl expanded attack surfaces, increased compliance risk, and drove uncontrolled storage growth. Data growth without control is exposure and can even mean visibility into exposed data is limited or unknown. 2025 news signal: AI adoption fuels rise of shadow datasets TechRadar: “Uncontrolled copies now lead to compliance failures and cyber incidents.” You may be exposed to governance and security risk if your environment shows: Temporary corpora that persist indefinitely Duplicated datasets with no lifecycle enforcement Sensitive data stored in unapproved locations Storage growth outpacing classification and policy application What to do about it Enforce lifecycle and retention policies Centralize storage for training data, checkpoints, and variants Classify and tag datasets to support governance Track all data copies, including AI-generated derivatives What these signals mean for 2026 The lessons of 2025 point to a clear shift in expectations. Storage becomes the enterprise control point of trust. Integrity must be demonstrable. Organizations will need immutable, lineage-rich, and audit-ready storage that can support verifiable recovery and compliance. Data mobility becomes a strategic advantage. Cloud concentration is a systemic risk. The ability to move data across cloud, on-prem, and edge environments, without penalty or redesign, defines resilience. Backup evolves into clean cyber recovery. Organizations must validate recovery paths continuously. Independence from cloud availability, automated restore validation, and multi-site protection are essential. AI success depends on governed pipelines. Reliable AI outcomes come from reproducible, well-managed data, not larger models. Accountability is the unifying principle. Across cyber, AI, sovereignty, and economics, organizations judged on evidence. As teams plan for what comes next, this is the moment to assess the foundation and reinforce the areas that matter most. Organizations that invest now — deepening immutability, improving data mobility, reinforcing sovereignty, and governing their data pipelines — will enter 2026 with confidence and clarity. Those that wait will be forced to react under pressure. Related content you may like: Blog article: 7 predictions that will shape data infrastructure in 2026 Blog article: The real lesson from the AWS outage — resilience can’t be outsourced Blog article: After AWS and Azure outages, SMBs face a reality check
