Observability
We've curated 69 cybersecurity statistics about Observability to help you understand how monitoring and analyzing system performance is crucial for detecting vulnerabilities and improving response strategies in 2025.
Showing 1-20 of 69 results
Roughly a third of organizations pay for redundant or underutilized observability features.
Technology teams spend an average of nearly $2.5 million annually on logging solutions.
Nearly three-quarters of senior technology leaders say AI workloads require a platform-based approach to log management.
More than a quarter of organizations burn engineering cycles just keeping multiple observability tools running across environments.
Organizations use an average of seven different tools to manage logs and telemetry.
96% of technology leaders rate observability as very or extremely important when working with AI-generated code, and 0% rate it as slightly or not important.
78% of engineering teams routinely prompt AI tools to include specific telemetry (logs, traces, metrics) directly into generated code.
About three-fourths of ITOps and engineering leaders identify end-to-end observability as their top investment priority to improve infrastructure resilience.
98% of organizations with the lowest downtime costs confirm end-to-end visibility is very or extremely important for reducing incidents.
33% of IT professionals cite budget constraints as a barrier to fully operationalizing AI in observability
42% of IT professionals cite skills gaps as a barrier to fully operationalizing AI in observability
55% of IT professionals report using too many monitoring and observability tools
64% of IT professionals say unified observability across all layers of the IT stack is very important to their team's success
47% of IT professionals cite security concerns as a barrier to fully operationalizing AI in observability
37% of IT professionals cite employee reluctance or resistance as a barrier to fully operationalizing AI in observability
41% of IT professionals cite complexity of technology as a barrier to fully operationalizing AI in observability
77% of IT professionals cite limited visibility across on-premises and cloud environments
75% of IT professionals say lack of coordination between teams (e.g., network, infrastructure, applications, and database) hinders effective observability
90% of IT professionals express confidence in AI’s ability to improve monitoring and observability operations
100% of IT and security leaders say a unified platform for logs, metrics, and traces would be valuable for their security and DevOps teams.