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ArmorCode finds gap between AI-powered vulnerability discovery and risk reduction
Wednesday, October 07, 2026 — 16:37:48 (UTC)
ArmorCode Study Highlights the Growing Gap Between AI-Powered Vulnerability Discovery and Risk Reduction Survey of 200 security leaders reveals remediation coordination and alert context are emerging as bigger priorities than simply finding vulnerabilities ArmorCode, the leader in Unified Exposure Management, today released Managing Exposure at AI Scale, a new study examining how AI is reshaping vulnerability management. Based on a survey of 200 senior security and technology leaders, the study explores how AI driving unprecedented growth in software creation and vulnerability discovery is widening the gap between what security teams can identify and what they can act on. The findings underscore a fundamental shift for security programs: it’s no longer enough to observe and rank risk. Organizations need continuous, preemptive exposure management that connects discovery to validated action before vulnerabilities become incidents. “AI is changing the exposure equation faster than most security programs can adapt,” said Sachin Kode, head of Security at ArmorCode. “Half of security leaders expect complexity to get worse if they continue with the status quo. Our study shows that finding more vulnerabilities doesn’t reduce risk. Organizations need to continuously connect discovery to action across security silos, and validate that every fix actually worked. That’s how security teams move from simply managing an ever-growing backlog to actually reducing exposure.” AI Expanding the Exposure Gap The study tapped leaders across application, engineering, product, IT, and information security on how AI is changing vulnerability discovery, prioritization, remediation, and the broader security operating model. Respondents represent organizations in the United States, Canada, the United Kingdom, Ireland, the Netherlands, and the Nordic countries, spanning healthcare and life sciences, financial services, manufacturing, technology, and insurance. It reflects a large enterprise perspective: 62% of respondents work at organizations with more than 10,000 employees, and 44% are C-suite executives. Specifically, the research finds: ● AI is increasing the review burden. 40% of respondents say the volume of AI-generated code requiring human review is a significant security challenge. Meanwhile, 44% say their biggest transformation need is a tiered approach to AI-assisted vulnerability discovery that prioritizes findings by urgency. ● Context matters more than volume. 36% say low-context alerts keep them up at night, underscoring that more findings are only useful when security teams have the context to act on them. ● Remediation is overtaking detection as the top priority. 39% say improving remediation coordination between security and development is their leading security goal, ahead of reducing find-to-fix time (33%) and quantifying risk to prioritize remediation (28%). ● Complexity is becoming a risk of its own. 51% of leaders say sticking with their current security approach will only add more complexity. These findings point to a simple conclusion: visibility alone does not reduce risk. Severity is only one part of the equation. Security teams need to break down infrastructure, application, and AI security silos. They need to know whether affected systems are reachable, whether exploitation is realistic, what business functions are at stake, and who owns remediation. Without that context, every new finding adds to the backlog, not to security. Read the complete report at www.armorcode.com/managing-exposure-at-ai-scale
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Source: Company press release. 
Categories: Reports and research
