Case Study
Cyber Resilience Insights
A data-driven exploration of the critical gaps between executive confidence and actual cyber recovery capabilities. Learn how mature resilience strategies and AI-driven detection can triple your chances of a successful recovery. 99.5% of companies have a strategy, but 57% fail to recover. Discover the data-backed roadmap to a mature, recovery-first resilience strategy.
- Topic
- Security
- Published
- 15 May 2026
Cyberattacks are intensifying, and the gap between "having a plan" and "being ready" is widening. While executive confidence remains high, IT professionals on the front lines report a different reality: over half of organizations fail to recover effectively when it matters most.
The "2026 Cyber Resilience Insights" research highlights how IT leaders can minimize risk and strengthen their ability to adapt. By shifting from a prevention-only mindset to an AI-driven, recovery-centric strategy, your organization can move from a state of vulnerability to one of verifiable excellence.
Key Highlights:
The Overconfidence Trap: 63% of IT professionals believe their leadership overestimates the organization's actual readiness for a cyber event.
Recovery Failure Rate: 57% of organizations failed to recover effectively from their last test or actual cyber incident.
Imbalanced Defense: 78% of professionals report their organization focuses more on preventing attacks than on the critical ability to recover from them.
Detection Blind Spots: Only 30% of businesses have a comprehensive platform for threat detection across primary storage, backup storage, and network infrastructure.
Minimal Impact Recovery: Only 40% of organizations successfully contained and recovered from an attack or drill with minimal business impact.
The Maturity Advantage: Organizations with mature resilience strategies are nearly 3x more likely to recover successfully.
The Testing Edge: 55% of those who simulate cyberattacks monthly or more frequently recovered successfully, compared to only 38% for those who test less often.
AI/ML Impact: Mature organizations are 3.1x more likely to use AI/ML tools for proactive mitigation and response.
Meeting Objectives: Frequent testers are significantly more likely to meet both Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO).
SLA Consistency: Mature strategies make an organization 2x more likely to consistently meet their Service Level Agreements.
Foundation for Excellence: Success requires a three-pillar approach: Secure (BIOS-level), Detect (AI/ML), and Recover (Frequent testing).