8 Ask a security leader whether their organization can recover from a cyber incident and you will almost always hear “yes.” Ask how they know, and the answers get softer. That gap — between confidence and demonstrated capability — is the defining story of cyber resilience in 2026. A recent industry survey of more than 900 security leaders across the C-suite and frontline security roles paints a sobering picture. Digital transformation, cloud adoption, and the rapid embedding of AI have pushed the value of enterprise data to new highs, and with it, the attack surface. Data now moves across clouds, applications, AI models, agents, and automated systems faster than most organizations can track. Yet the majority of leaders believe they are ready. The data suggests many are not. For IT and storage decision-makers — the teams responsible for making recovery real when an incident hits — the trends below are worth reading closely. They point to where resilience programs are quietly breaking, and to the capabilities that separate organizations that recover from those that simply hope to. Trend 1: The confidence gap is wider than most leaders admit Ninety percent of security leaders in the 2026 survey said they were “very” to “extremely” confident they could meet their defined recovery time objectives (RTOs). Sixty-nine percent said those RTOs were fully aligned with business continuity goals. On paper, the industry looks ready. Real-world outcomes tell a different story. Among organizations that experienced a cyber incident in the past 12 months, more than 40% reported customer or constituent service disruption and 41% reported financial loss or revenue impact. Thirty-eight percent experienced extended downtime of critical systems. And for organizations hit by ransomware that resulted in operational loss or data encryption, only 28% fully recovered all affected data. Another 29% ended up with data loss, downtime, or business disruption they could not undo. This is not a measurement error. Confidence in recovery is often shaped by the presence of plans and tools — backups, runbooks, insurance policies — rather than by the realism and frequency of testing under real-world conditions. When recovery has not been exercised against a meaningful failure scenario, readiness becomes an assumption. Incidents are where assumptions get corrected, usually at the worst possible time. The practical lesson: an RTO on a slide deck is not the same as an RTO validated in a tabletop, and a tabletop is not the same as a live recovery from immutable, verified data. Leaders who want to close the confidence gap need evidence, not intent. Trend 2: AI adoption is outpacing the ability to secure it The second force widening the gap is AI itself. AI has moved from experimentation into everyday execution — embedded in core processes, customer workflows, developer tooling, and increasingly in agentic systems that act on users’ behalf without direct human oversight in the loop. That momentum is creating a new category of data risk that most organizations have not yet operationalized. The numbers are telling. Forty-three percent of respondents said AI tool adoption is outpacing their ability to secure data and models. Forty-two percent said they have limited visibility into all the AI tools or models used across their organization. Forty percent said their security policies have not yet been updated to include AI-specific risks, such as generative AI. A quarter cited shadow IT and unauthorized AI tool usage as a primary concern. This is not primarily a model security problem. It is a data governance and operational resilience problem. AI systems introduce new data paths across users, applications, APIs, and third-party services. They amplify existing issues like data sprawl, inconsistent access controls, and unclear ownership. When teams cannot see how data is being used, shared, or retained, enforcing policy becomes harder — and recovering cleanly after an incident becomes harder still. The recovery implications deserve particular attention. AI expands the scope of what must be protected: not just files and databases, but the pipelines, feature stores, vector indexes, model artifacts, and retrieval-augmented context that production AI systems depend on. If those assets are not classified, controlled, and backed up with the same discipline as other critical systems, recovery plans built for a pre-AI environment will quietly fail at the moment they are needed. Trend 3: Policy alone does not reduce risk A consistent finding in the survey is that organizations with stronger outcomes treat governance as an execution discipline, not a statement of principle. The clearest indicator is the adoption of enforcement tooling. Forty-eight percent of respondents said data loss prevention (DLP) controls were already in place — and those organizations reported measurably stronger visibility and control as AI usage expanded. Only 39% of organizations with DLP reported limited visibility into AI tools and models, compared with 45% of those without DLP. Thirty-eight percent with DLP said AI adoption was outpacing their security controls, compared with 48% of those without. The difference is not dramatic, but it is directional and consistent. Enforceable controls translate governance into day-to-day behavior by reducing risky data movement and limiting exposure as AI usage expands. A policy that says “do not upload customer data to public AI tools” is only as effective as the control that prevents it when someone tries. The same principle applies at the storage layer. Immutability written into policy is not the same as immutability enforced by the system. Retention rules that depend on administrator discipline are not the same as retention rules the system will refuse to override. Resilience built on intent is fragile; resilience built on enforced controls holds under pressure. Trend 4: Ownership of AI and data risk is still unsettled As AI expands, the question of who owns the risk is becoming urgent — and most organizations have not answered it cleanly. Among organizations that experienced a cybersecurity incident, 38% said AI and data risk governance was owned by the CISO, 27% said the CIO, and only 17% reported a cross-functional governance structure. Clear ownership matters. Concentrated ownership in a single executive, however, tends to create blind spots. AI risk sits at the intersection of cybersecurity, data governance, infrastructure, compliance, and business operations — and no single executive typically has full visibility across all of those domains. Organizations that adopt a cross-functional model, bringing together IT, security, data, and business stakeholders, report stronger alignment between policy, controls, and recovery capability. This shows up in the incident data as well. Among respondents who did not experience a cybersecurity incident, 37% reported using cross-functional policy approval committees, compared with 31% of those that did. The delta is modest, but it reinforces a broader point: resilience is a shared capability, not a single team’s responsibility, and governance structures that reflect that tend to perform better when the pressure is on. Trend 5: Four practices separate organizations that recover from those that hope to Despite differences in industry, size, and maturity, the survey points to four practices that consistently correlate with stronger recovery outcomes. The first is expanding ownership of cyber resilience beyond the security function into IT, data, and business leadership. The second is operationalizing AI governance — pairing formal AI risk management and data governance policies with practical controls such as model validation, testing, and drift monitoring. The third is communicating cyber risk to business leaders frequently, with boards and executives briefed monthly or more often, supported by advanced reporting such as cyber risk quantification. The fourth is tracking KPIs that matter — restore and recovery testing (57% of respondents), mean time to recover (56%), time to isolate and contain (42%), and the percentage of fully automated or orchestrated recovery processes (only 23%, a meaningful gap for most organizations). None of these practices is novel. What is notable is how tightly they correlate with outcomes in the data and with anonymized interview accounts. One Fortune 1,000 leader described a resilience discovery that will sound familiar to many: “The board was there at the tail end of a tabletop, and they had a lot of questions. One stood out: ‘Wait, we don’t have the ability to recover?’ At the time, we didn’t.” The fix was an investment in immutable infrastructure the organization could actually recover from. The lesson is that resilience becomes real when leaders can point to what will work, not what should. Trend 6: Budget growth correlates with better outcomes — but only where it is measured Cyber budgets are not rising uniformly. Forty-nine percent of organizations increased cybersecurity budgets year over year; 51% held flat or cut. That split helps explain why resilience outcomes vary so widely — especially when organizations cannot consistently measure readiness or validate recovery. Where budget is growing, it tends to flow into foundational resilience capabilities. Thirty-eight percent of organizations with budget increases prioritized immutable storage, compared with just 11% of those without. Forty-two percent prioritized automated backup, versus 33%. Integrated cyber resilience and business continuity planning is more common in budget-growing organizations as well. The recovery payoff is visible. Organizations with budget growth were far more likely to track RTOs (78% vs. 56%), time to isolate and contain (47% vs. 36%), and the percentage of recovery processes that are fully automated or orchestrated (32% vs. 14%). When ransomware hit, 40% of budget-growing organizations fully recovered their data, compared with just 16% of those without budget growth. Only 32% of budget-growing organizations paid a ransom; 52% of those without budget growth did. Correlation, not causation — but consistent. Measuring readiness and investing in proven capabilities improves outcomes. That has real implications for how storage and backup strategies are built. Immutable, object-based repositories with enforced retention and air-gapped recovery paths are not just a line item. They are the difference between recovering and writing a check to an attacker. Trend 7: Compliance is quietly reshaping resilience architecture Cyberattacks remain the emerging risk most likely to impact data resilience over the next 12 months (36%), but regulatory and compliance mandates are a close second (33%). Data residency and sovereignty now drive the most important factor in data placement decisions for 58% of organizations. As obligations expand — from sector-specific rules to cross-border data transfer frameworks to AI-specific regulation coming online in many jurisdictions — compliance and resilience are converging. Organizations need to demonstrate controls, produce evidence, and report accurately, especially after an incident. That favors architectures where data location, retention, access, and recovery can be proven with precision rather than attested with a checkbox. It also favors sovereign-capable, on-premises or hybrid object storage for workloads where public cloud data residency is either unacceptable or operationally too expensive to maintain at scale. What proven resilience looks like in 2026 Pulling the trends together, the pattern is consistent. Organizations that over-index on confidence are often more exposed, not less. Real resilience is demonstrated through validated recovery, supported by strong data governance, cross-functional ownership, enforceable controls, and storage that behaves the way policy says it should — even when an attacker or an errant AI agent tries to convince it otherwise. For teams rethinking their posture in 2026, three practical priorities stand out. First, close the testing gap. Move from tabletop exercises to live recovery rehearsals against realistic failure modes — including AI-dependent workflows. Second, invest in storage that enforces immutability and retention at the system level, with air-gapped or logically isolated recovery targets, sovereign data placement where the regulation requires it, and the throughput to restore at scale rather than trickle. Third, build cross-functional ownership into the org chart — not just the incident bridge — so AI, data, security, and infrastructure leaders share both the metrics and the accountability. AI is accelerating how data moves, learns, and triggers action across the enterprise. Agentic systems will accelerate it further. Risk rises at the same pace, which makes trusted, recoverable data the quiet foundation for everything else an organization wants to do with AI. The 2026 data makes the takeaway clear: confidence is common, but validated recovery capability is not. Closing that gap is the work. Further reading 2026 tech trends: cyber sovereignty — a forward look at how data sovereignty and residency are reshaping resilience strategy in 2026. Five levels of unbreakable cyber resiliency — a maturity model that pairs cleanly with the four practices covered above. Ransomware backup protection: how to build immutable, recoverable backups — the operational how-to behind “immutability enforced by the system, not by policy.” Ransomware recovery: restore faster with object storage — a practical take on recovery speed and S3 Object Lock, aimed squarely at the confidence-versus-reality gap. Ransomware protection with Scality — analyst-informed overview of ransomware protection architecture for enterprise data.
