N-able, Inc. (NYSE: NABL), a global cybersecurity company delivering end-to-end business resilience, today released “The AI Fog Report: When the Signal Becomes the Noise,” an independent global research report examining how small and medium size enterprises are scaling to drive business continuity during this new era of Frontier AI.

The survey of 595 cybersecurity decision-makers and practitioners across the United States and United Kingdom finds the security industry at an inflection point. As organizations rapidly adopt AI-powered security technologies, increasingly capable Frontier AI models are transforming both cyber defense and attacker capabilities.

The research reveals three critical challenges: AI is amplifying complexity within already complex security environments, security teams are struggling to separate context from noise to identify what matters most, and AI adoption is outpacing organizational trust and governance. Together, these trends are reshaping how organizations manage cyber risk and highlighting the need for greater context, confidence, and control.

“Security teams are entering the AI era from an already difficult operating position,” said Brendan Griffin, Director of Threat Research at N-able. “The promise of AI has never been greater, but simply adding more AI doesn’t automatically produce better security outcomes. Organizations need AI that reduces operational complexity, delivers meaningful context, and helps defenders make confident decisions faster.”

AI is amplifying complexity inside already complex security environments

  • 86.1% of respondents report security alert volumes increased over the past 12 months, including 24.2% who say alert volume increased by more than 50%.

  • Among organizations experiencing increased alert volume, respondents most often attribute growth to adding more detection tools (40.2%), followed by AI enabling attackers to launch attacks faster (26.2%), while 20.1% say multiple factors are contributing simultaneously.

  • 84.5% have encountered a confirmed or suspected AI-assisted attack during the past year, illustrating how AI is accelerating the pace and scale of modern threats.

  • 54.8% say alert noise interferes with incident response daily or several times each week, while only 8.7% report that alert noise rarely or never affects operations.

The findings suggest organizations are reaching a point of diminishing returns from layering AI on their existing stacks. As adoption continues to grow, reducing operational friction and helping defenders focus on the decisions that matter most should be the core focus of AI security solutions.

Security teams face a signal problem in addition to a volume problem

  • The leading sources of low-quality signal are duplicate alerts from overlapping tools (50.4%) and AI-generated alerts lacking sufficient context (48.9%), indicating organizations increasingly struggle with interpretation rather than detection alone.

  • 56.0% rank faster detection-to-containment as AI’s most valuable operational outcome.

  • Respondents’ desired outcomes consistently emphasize better prioritization over more telemetry, reinforcing that signal quality, not alert quantity, is becoming the industry’s defining challenge.

  • When asked what AI should optimize for, the largest share (37.8%) say both broader detection coverage and better signal precision are equally important, while 32.6% prioritize broader detection and 29.6% prioritize fewer, higher-confidence alerts.

AI adoption is accelerating faster than organizational trust

  • 81.0% have meaningfully increased AI-powered security tool adoption during the past year, with another 16.5% planning to increase adoption over the next 12 months.

  • Just 24.5% fully trust autonomous AI remediation, highlighting a significant gap between adoption and confidence.

  • Yet 87.1% report making at least minor security policy exceptions to enable AI tools. Additionally, only 51.9% maintain a comprehensive inventory of AI tools operating across their environments.

  • Before trusting autonomous AI, 62.7% of respondents require full explainability. 59.8% require there to be built-in human override.

  • Only 41.0% are very confident their existing security stack can keep pace with AI without a significant overhaul.

Boyd Smith, Founder and President, TechKnowledgey said, ” For many non-customer-facing roles, we’re embracing AI for its potential to transform how we operate. AI creates the most value when it helps our teams cut through complexity and focus on the threats that matter most, freeing them up to focus on people instead of running processes.”

The full findings of “When the Signal Becomes the Noise: The AI Fog Report,” are available here: https://www.n-able.com/resources/ai-fog-report-2026.

About N-able

N-able protects businesses from evolving cyberthreats. Our AI powered cybersecurity platform delivers business resilience to more than 500,000 organizations worldwide, leveraging advanced end-to-end capabilities, simplified workflows, market leading integrations, and flexible deployment options to improve efficiency and drive critical security outcomes. Our partner-first approach pairs our technology with experts, training, and peer-led events that empower customers to be secure, resilient, and successful. n-able.com

© 2026 N-able Solutions ULC and N-able Technologies Ltd. All rights reserved.

The N-able trademarks, service marks, and logos are the exclusive property of N-able Solutions ULC and N-able Technologies Ltd. All other trademarks are the property of their respective owners.

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. These statements are based on management’s current expectations and are subject to risks and uncertainties. Actual results may differ materially. Readers are referred to the Company’s filings with the Securities and Exchange Commission, including its most recent Annual Report on Form 10-K and Quarterly Reports on Form 10-Q, for a discussion of risks that could cause actual results to differ materially from those expressed in any forward-looking statement.

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