Report reveals AI adoption surge from single digits to 50%+ as cybersecurity teams confront an execution gap at scale
New research shows cybersecurity teams are identifying far more risk than they can realistically remediate. 40 of the world’s largest corporations, representing a combined $708 billion in annual revenue, are found to operate an average of seven security scanning tools and process 67.3 million security findings per year, according to the Seemplicity 2026 Exposure Action Report.
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“At this level of scale, the problem isn’t a lack of data. It’s making sense of it fast enough to act.” – Daniel “Bogo” Bogomolny, principal exposure management specialist for Seemplicity
Remediation capacity has failed to keep pace, resulting in a widening gap between what security teams can detect and what they can actually fix. The new report shows that exposure management has entered a new phase driven by execution pressure, not visibility gaps.
Exposure volume has grown nearly 40 percent year-over-year, while the risks themselves remain familiar. Topping the list are cloud misconfigurations, container privilege issues, and known CVEs. What has changed is “scale.” Continuous scanning and broader coverage now generate volumes that overwhelm traditional prioritization and remediation workflows.
“Security teams aren’t overwhelmed because they don’t know what their risks are,” said Daniel “Bogo” Bogomolny, principal exposure management specialist for Seemplicity. “They’re overwhelmed because execution doesn’t scale as fast as detection. Exposure management success is defined by how efficiently teams can prioritize and remediate, not by how many findings they generate.”
AI is rapidly becoming central to exposure management at scale. In 2025, more than half of organizations using the Seemplicity exposure management platform enabled AI-powered capabilities, up from single-digit adoption at the start of the year. AI has become embedded in day-to-day security workflows, helping teams augment human judgment to interpret massive volumes of data and act faster with greater confidence.
“At this level of scale the problem isn’t a lack of data. It’s making sense of it fast enough to act,” added Bogo. “AI is becoming a critical tool for adding context, helping teams cut through noise and focus on the fixes that actually reduce risk.”
The report concludes that exposure management maturity is now measured by outcomes, not activity. Organizations that operationalize remediation through clear ownership, repeatable workflows, and AI-assisted decision-making are the ones able to reduce risk at scale.
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