The data backup confidence gap: Are you really recovery ready?

Where the confidence is coming from

Despite a rapidly evolving threat landscape, market research, such as the Unitrends report, shows that business leaders are confident in their data backup strategies.

This confidence does not equate to a lack of data security awareness. Today's business leaders understand the importance of cybersecurity; the proof is in their increasing cybersecurity investment. According to a global study commissioned by Sage and conducted by IDC, small and midsize businesses (SMBs) in particular are increasing cybersecurity investments. However, this investment is not translating into operational readiness and resilience.

Part of the problem might be that many SMBs have loosely defined cybersecurity responsibilities and data protection protocols. In smaller organizations, security is often embedded within the wider IT function. This lack of clear ownership and data security expertise is likely related to poorly defined data backup policies, inconsistent recovery process documentation and nearly non-existent review cycles.

The proof is in the survey responses: Most SMBs surveyed have only baseline data protections in place, and only 36% test their incident response plans. Even in organizations that have established clear data backup and recovery metrics, like recovery point objectives (RPOs) and recovery time objectives (RTOs), there's often a lack of alignment with business resiliency.

According to Veeam's Data Trust and Resilience Report 2026, 90% of surveyed organizations are confident in meeting their defined RTOs, but only 69% say those RTOs align with business continuity goals.

That dissonance is the center of the backup confidence gap: Business and IT leaders are aware of the need for strong data protection, comprehensive data backups, and rapid data recovery, but the reality of their backup and recovery capabilities doesn't match their expectations.

Why backups aren't measuring up

There is no single reason for the data backup confidence gap, but some current trends may be responsible.

As organizations embed AI deeper into core processes, the ability to recover quickly from data disruptions is becoming more important than ever.

For example, organizations are becoming much less tolerant of downtime and data unavailability, especially in the wake of surging AI adoption. AI applications require high levels of data availability and unparalleled access to vast stores of data. As organizations embed AI deeper into core processes, the ability to recover quickly from data disruptions is becoming more important than ever.

In this environment, a traditional data protection strategy won't cut it. Organizations need to keep pace with a rapidly evolving threat landscape and detect, respond and recover from incidents as quickly as possible. But relying on outdated data backup and recovery protocols and tightening RPOs and RTOs isn't enough.

Traditional data protection strategies might provide leaders with a false sense of security, and the need for recovery speed could distract them from focusing on recovery assurance. Older strategies are typically more rigid and focus on hardware-defined protections.

This creates a static data infrastructure that can't adapt to modern data challenges, which can lead to IT teams using a patchwork of data protection tools to cover gaps. However, using multiple tools instead of a cohesive fix can lead to inconsistent security coverage and make it difficult to spot vulnerabilities hiding in blind spots.

As a result, leaders might feel like they are doing the right thing by investing in security, acquiring and using new data protection tools, and setting tighter data recovery metrics, but these efforts aren't making their business more resilient. Instead, the data backup confidence gap is leaving their organization exposed to vulnerabilities and less equipped to recover from disruptions.

How to close the backup confidence gap

To course-correct at a high level, IT leadership must focus on tighter integration of data storage and data protection at the application level rather than defining protections based only on hardware. The modern data landscape is application-centric, so the data protection strategy must shift accordingly to ensure data availability, faster time-to-detection, and quicker recovery rather than just aiming to prevent disruptions.

The following are some best practices and next steps IT leadership can implement to help close the data backup confidence gap and align expectations with reality:

• Assign the same level of importance to backup data as regular data. Backup data might be copies of business data, but that doesn't make it any less important. Backup data must be just as secure and compliant as other business data.
• Continually evolve data policies. Factor in people, processes and technology when designing data policies and define the distinct data protection strategy each application requires. These policies should be updated at least annually or whenever new data sources are integrated.
• Develop flexible backup and recovery strategies. Each data source might require a unique backup method or recovery strategy. Design them accordingly and make sure they work across deployment types, including on-premises, cloud and edge applications.
• Establish realistic RPOs and RTOs. Don't overpromise and underdeliver when it comes to data recovery. Set realistic RPOs and RTOs, test performance against them, and revisit them often to help truly understand how quickly the business can recover from disruptions.
• Implement strict access controls. Permissions must be set comprehensively to ensure those who need to access backup data can do so during an emergency. Combine access controls with multifactor authentication, data encryption and secure data transfer protocols to make sure only designated personnel can access data.
• Lock down the control plane. Create an air gap by isolating data and use immutable storage repositories to lock it down and prevent it from being modified, deleted or encrypted by bad actors.
• Plan specific responses to each threat vector and any disruption. Ransomware, AI-driven social engineering, simple human error, and natural disasters are just a few of the threat vectors to consider. Use deep threat analysis to identify all risks to the organization and plan specific responses to each.
• Have well-defined contingency plans in place. Multilayered protection is the goal. If backup and recovery procedures hit a snag, have alternative plans in place to securely recover data, even from risks that might seem unlikely to occur.
• Automate what you can. Centralizing and automating data protection can help reduce human error and accelerate recovery. Try to avoid using disparate tools and instead work toward unified data management. This can help simplify operations and eliminate hidden vulnerabilities.
• Use advanced (but tested) technologies. Traditional data optimization technologies can help address data growth with methods such as compression and deduplication. However, emerging AI tools can help detect misconfigurations in backup infrastructure and potential compromises and vulnerabilities through anomaly detection.
• Test all backup and recovery processes. Test all backup and recovery processes with regular drills and use sandbox data recovery environments for additional security. Make sure there are procedures in place to validate data being restored to ensure it's not corrupted. Perform tests on an annual or quarterly schedule, depending on how often processes are updated.
• Continually assess and refine data resiliency. Embracing a continuous improvement cycle for backup and recovery can help address emerging threats. Track and review regular reports on backup status and history, recovery objectives, and compliance requirements to ensure backup strategies continually evolve.

Never become complacent. Organizations that build a static backup strategy or rarely update their recovery approach are prone to falling into the data backup confidence gap, where expectations are mismatched with actual ability. This can unnecessarily put the business at risk of data loss, compliance consequences, workflow interruptions and worse.

Jacob Roundy is a freelance writer and editor with more than a decade of experience, specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.