In a nutshell: Resilient enterprises are characterized by complete IT transparency, which makes AI-driven security analyses more reliable and reduces downtime costs.
Organizations with complete visibility into their IT systems handle security incidents more efficiently and incur lower downtime costs. This is shown by a joint analysis from Splunk and Cisco, whose findings are central to the application of AI in incident response.
Splunk’s latest “Cost of Downtime Report” estimates annual costs of unplanned IT outages for Global 2000 enterprises at approximately $600 billion. A striking difference emerges between resilient organizations and the rest of the market: almost all companies with the lowest downtime costs report having complete visibility into their systems, applications, and data sources.
Modern IT environments continuously generate large volumes of data from applications, cloud services, networks, and endpoints. However, the real problem is not a lack of data, but its distribution across different platforms. Security teams must laboriously consolidate relevant signals from various sources, which delays rapid response to incidents. With each additional security solution, complexity increases further.
Artificial intelligence can assist IT teams by prioritizing alerts, filtering false alarms, and automatically enriching security-relevant events. However, its accuracy depends directly on data quality: according to another Splunk report, 82 percent of surveyed IT executives report that a structured data management strategy improved the accuracy of their AI applications. Incomplete or unlinked information, by contrast, leads to incorrect threat assessments or missed incidents.
Splunk recommends five fundamentals for a robust data strategy: first, consistent, current, and cleaned data instead of large volumes of unstructured information; second, elimination of data silos for unified visibility into cloud, on-premises, and third-party systems; third, processing of machine data (logs, metrics, events) for early anomaly detection through specialized analytical models; fourth, IT infrastructure that scales with growing data volumes, for example through edge intelligence for early filtering of irrelevant data; fifth, consistent governance with clear access rights and traceable data lineage.
Source: www.it-daily.net · Published July 23, 2026
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