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Cyber Resilience & Data Trust: Data Trust and Resilience Report 2026 – Veeam

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3 weeks ago
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Introduction

Organizations today face a paradox. Digital transformation, cloud adoption, and AI have increased the value of enterprise data, but they have also expanded the attack surface around it. Data now moves across clouds, applications, AI models, agents, and automated systems faster than most organizations can track, and the tools built to protect that data are struggling to keep pace.

Contents
IntroductionYou Will LearnStrategic Insight: Confidence Is Common, Validated Recovery Capability Is NotKey ChallengesGetting StartedWho Should Read This Report?Download the ReportOh hi there 👋It’s nice to meet you.Sign up to receive awesome content in your inbox, every week.

At the same time, most organizations believe they are prepared. Confidence in cyber recovery runs high across the C-suite and frontline security roles, yet actual incident outcomes tell a far less reassuring story. When ransomware and other cyber incidents strike, a significant share of organizations end up with real customer disruption, financial loss, and extended downtime, regardless of how confident they felt going in.

This shift marks a move away from equating resilience with having backups, policies, or insurance in place, toward a more demanding standard: resilience proven through testing, validated recovery, and enforced controls rather than assumed readiness. As AI becomes further embedded in business operations, the stakes only rise, introducing new data flows, new attack surfaces, and new governance questions that many organizations haven’t yet resolved.

This report explores the gap between recovery confidence and demonstrated recovery capability, based on a survey of more than 900 security leaders. It examines what separates organizations with genuinely stronger recovery outcomes from those simply hoping their plans will hold.

You Will Learn

  • Why high confidence in meeting recovery time objectives often doesn’t match real incident outcomes
  • How ransomware recovery rates reveal the gap between preparedness and proven capability
  • Why AI adoption is outpacing organizations’ ability to secure and govern the data behind it
  • How data loss prevention and enforceable controls change recovery outcomes compared to policy alone
  • Why concentrating AI governance in a single executive role can create blind spots
  • What four practices consistently correlate with stronger recovery outcomes
  • How executive alignment and reporting frequency influence both budget and recovery performance
  • Which KPIs cyber insurers and business leaders increasingly expect organizations to track
  • How cybersecurity budget growth connects to lower ransom payments and higher data recovery rates
  • Why compliance and regulatory mandates are becoming as central to resilience as the cyberattacks themselves

Strategic Insight: Confidence Is Common, Validated Recovery Capability Is Not

The central finding of this research is a persistent mismatch between how prepared organizations believe they are and what actually happens when an incident occurs. The vast majority of security leaders report high confidence in meeting their recovery time objectives, but among organizations that experienced a cyber incident in the past year, a substantial share still reported customer disruption or financial loss. Ransomware outcomes are harder still, with a majority of affected organizations ending up with meaningful data loss, downtime, or business disruption.

This matters because it reframes resilience as something that must be demonstrated, not assumed. In interviews, many organizations equated resilience with simply having backups, policies, or insurance in place. Real resilience requires more: clear visibility into where data lives and how it’s used, enforced controls that reduce exposure in practice, and recovery capability that’s proven through testing rather than taken on faith.

1. AI Adoption Is Outpacing Governance
AI is moving from experimentation into everyday execution across core business processes, and that momentum is expanding data risk well beyond model security alone. A significant share of security leaders say AI tool adoption is outpacing their ability to secure the underlying data and models, and many report limited visibility into which AI tools are even in use across their organization.

2. Policy Alone Doesn’t Reduce Risk
Organizations need governance that extends beyond written policy into enforceable controls. Those with data loss prevention tooling already in place report measurably stronger visibility and control as AI usage expands, compared to organizations relying on policy intent alone.

3. Ownership Is Often Concentrated, Not Shared
Responsibility for AI and data risk governance is rarely distributed across leadership teams. It’s typically assigned to a single executive, most often the CISO or CIO, even though AI risk sits at the intersection of cybersecurity, data governance, infrastructure, compliance, and business operations, areas no single role usually has full visibility into.

Key Challenges

While the research points toward clear paths to stronger resilience, it also surfaces real barriers organizations are working through:

  • A persistent gap between recovery confidence and validated, tested recovery performance
  • Limited visibility into AI tools, models, and the new data paths they introduce
  • Security policies that haven’t yet been updated to address AI-specific risks such as generative AI use
  • Shadow IT and unauthorized AI tool usage that expand exposure outside formal oversight
  • Fragmented AI governance ownership that creates blind spots across cybersecurity, data, and business functions
  • Inconsistent cybersecurity budget growth, with roughly half of organizations holding budgets flat or reducing them year over year

Getting Started

Organizations looking to close the gap between recovery confidence and recovery capability should begin by:

  • Testing and validating recovery processes under realistic conditions rather than relying on assumed readiness
  • Extending AI and data risk ownership beyond a single executive into a cross-functional governance model
  • Backing security policy with enforceable controls, such as data loss prevention tooling
  • Increasing the frequency and depth of cyber risk reporting to the board and C-suite
  • Tracking recovery-focused KPIs such as recovery time objectives, time to isolate and contain, and the percentage of recovery processes that are fully automated

Who Should Read This Report?

This report is designed for leaders responsible for cybersecurity, data governance, and organizational resilience, including:

  • CISOs, CIOs, and other C-suite security and technology executives
  • Risk management and data governance leaders
  • IT infrastructure and security operations teams
  • Business continuity and compliance leaders navigating AI-related regulatory requirements

It is especially valuable for organizations integrating AI into core business operations who need to understand where their governance and recovery capabilities may not match their actual level of risk.

Download the Report

Download the Data Trust and Resilience Report 2026 from Veeam to see how more than 900 security leaders assess their recovery readiness, where the gap between confidence and demonstrated capability shows up most, and which four practices consistently separate organizations with stronger recovery outcomes from the rest.

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