Last year, industry experts predicted that 2025 would be the year SaaS data protection stops being optional and becomes a must-have as data volume increases, API strain grows, and practical AI solutions start to win over hype.
Now, as we look ahead to 2026, the view sharpens. The growing complexity across cloud, hybrid, compliance, and threat landscapes forces us to confront three truths: First, protecting cloud data must become non-negotiable; second, AI should be used deliberately to defend, not just to automate; and third, compliance and regulatory pressure are reshaping how and where data lives.
AI Offense Evolves Faster Than Defense — Unless Leaders Demand Transparency
AI-driven attacks will become highly adaptive. By 2026, adversaries will use AI systems that map entire infrastructures in seconds, identify weak links deep in the supply chain, and shift tactics in real time to bypass defenses. Hybrid warfare will amplify this trend as hostile actors blend geopolitical intent with AI-enabled automation at scale.
Defenders will match this only if they adopt AI with intention and transparency. Security teams will use AI to understand exposure, strengthen detection, and model where risk concentrates. But success will depend on knowing how an AI system works, what data it relies on, and how decisions are made.
CISOs will demand clarity, control, and accountability. The organizations that win will be those that use AI to enhance — not replace — human judgment. This means:
Explainable AI systems: Security tools must provide clear reasoning for their recommendations and actions, allowing teams to validate and adjust as needed.
Controlled automation: AI should accelerate human decision-making, not remove humans from critical security choices.
Continuous validation: Regular testing and auditing of AI-driven security measures to ensure they're working as intended and adapting appropriately to new threats.
Transparent data usage: Understanding what data AI systems access and how that information influences security decisions.
Hybrid Infrastructure Is Back — And So Is the Race for Skills
Hybrid environments will grow faster than anyone expected. After years of cloud-first narratives, companies are re-evaluating what belongs where. Political instability, rising sovereignty requirements, and cost pressures are pushing critical workloads back on-premises. Servers, storage systems, and licensed software are seeing a resurgence because organizations want balance, not absolutism.
This shift exposes the growing skills gap. Demand for deep technical expertise in networking, Linux, and systems engineering is accelerating while talent inflow is shrinking. By 2026, this shortage will influence everything from innovation speed to resilience planning.
The Talent Challenge
Organizations face several interconnected challenges:
Legacy expertise shortage: Fewer professionals have deep experience with on-premises systems, creating knowledge gaps as organizations rebalance their infrastructure.
Cloud-native limitations: Many newer IT professionals have primarily cloud experience and need upskilling for hybrid management.
Security complexity: Hybrid environments require security professionals who understand both cloud and traditional infrastructure threat models.
Operational integration: Teams need skills to integrate cloud and on-premises systems seamlessly for backup, recovery, and business continuity.
Quantum and AI Face Reality Checks
Meanwhile, quantum computing and advanced AI will face a public reckoning. The promise of crypto-breaking quantum machines and near-term AGI will give way to more realistic timelines. Investments will continue, but the narrative will mature as enterprises look for practical, defensible value rather than speculative breakthroughs.
This doesn't mean these technologies aren't important — it means organizations will focus on pragmatic applications and incremental improvements rather than revolutionary transformations.
AI Stays Practical in 2026, While Modernization Remains the Real Priority
AI adoption in 2026 will feel familiar. Most enterprises will continue using agentic AI to automate repeatable tasks and augment existing processes, not reinvent them. Only one in five organizations report getting meaningful value from their AI tools at the current time, with key adoption challenges being cost and lack of control mechanisms in context of the desired outcomes.
Autonomous business intelligence will remain niche because the required foundations — including infrastructure — are simply not ready: Data quality, governance maturity, and organizational skills still lag far behind the ambition.
The Real Focus: Modernization
Modernization efforts will remain the primary focus. Companies will keep working through the practical realities and motions to replace platforms like VMware and Citrix, while using SaaS to accelerate outcomes where it makes sense.
At the same time, compliance and regulatory pressure will intensify. Leaders will need a clear understanding of:
- Sovereignty requirements: Where data can be stored and who can access it
- New operating models: How to manage data across distributed environments
- Talent requirements: The skills needed to bridge "old way" and "new way" approaches
- Compliance frameworks: How to meet evolving regulatory requirements across jurisdictions
Planning for 2030
In 2026, CIOs will be planning for what IT must look like in 2030. The problems they solve today will not be the ones they face next, and there is significant pressure on the IT suite to ensure companies are ready and competitive as the AI transformation gains momentum.
This forward-looking approach requires:
Infrastructure flexibility: Systems that can adapt to changing technology landscapes
Skills development: Investment in training and hiring to bridge capability gaps
Vendor partnerships: Relationships with providers who can support long-term transformation
Resilience planning: Backup and recovery strategies that work across hybrid environments
Compliance Goes Default: NIS2 and DORA Will Reshape Every SaaS RFP
By 2026, compliance expectations will become embedded in nearly every SaaS data protection RFP. Requirements tied to NIS2 and DORA will shift from "requested" to "assumed," especially in finance, energy, healthcare, and the public sector.
Organizations will insist on local digital sovereignty: data stored in-region, zero sub-processors, and guaranteed access even if the original SaaS platform is unavailable.
The Compliance Shift
Because many companies are still in the early stages of meeting these regulations, demand will rise sharply as deadlines tighten. Key compliance requirements will include:
Data sovereignty: Clear documentation of where data is stored and processed, with options for regional storage to meet local requirements.
Access guarantees: Ability to access backed-up data even if the primary SaaS provider experiences an outage or business disruption.
Incident reporting: Systems and processes to detect, report, and respond to security incidents within regulatory timeframes.
Recovery testing: Regular validation of backup and recovery capabilities to ensure RTO and RPO targets can be met.
Vendor transparency: Clear information about sub-processors, data flows, and security measures.
The Role of Local Partners
Local partners will play an essential role. They understand national sovereignty rules, infrastructure constraints, and the operational realities of regulated industries. As a result, the channel will become a core enabler of compliant SaaS adoption, not an afterthought.
Organizations should look for partners who can provide:
- Expert guidance on local regulatory requirements
- Infrastructure that meets sovereignty needs
- Support for compliance documentation and audits
- Rapid response for incident management
- Long-term partnership for evolving compliance needs
Preparing for 2026: Action Steps
To navigate these trends successfully, organizations should:
Evaluate AI security tools critically: Focus on transparency, explainability, and human oversight rather than fully autonomous solutions.
Assess hybrid infrastructure needs: Determine which workloads belong in the cloud versus on-premises based on cost, sovereignty, and operational requirements.
Invest in skills development: Address the talent gap through training programs, strategic hiring, and partnerships with experienced providers.
Prioritize practical AI applications: Focus on proven use cases that augment human capabilities rather than speculative transformations.
Prepare for compliance requirements: Start implementing NIS2 and DORA requirements now rather than waiting for deadlines.
Partner strategically: Build relationships with vendors and partners who understand your regulatory environment and can support long-term transformation.
Test recovery capabilities: Regular validation of backup and recovery processes across hybrid environments.
Looking Forward
The predictions for 2026 aren't about revolutionary change — they're about the hard work of making technology serve business needs while managing real risks. Success will come to organizations that balance innovation with pragmatism, speed with control, and automation with accountability.
As we move into this next phase of digital transformation, the winners won't be those with the newest technology or the biggest AI investments. They'll be the organizations that thoughtfully integrate new capabilities while maintaining robust security, compliance, and resilience practices.
The future belongs to those who can navigate complexity without compromising fundamentals — and that starts with honest assessment of where we are and where we need to go.
This article was inspired by insights from Keepit Blog on data protection trends and predictions for 2026.