By Nicole Reineke, Senior Distinguished Product Manager and Director of AI Strategy, N-able
For years, small and midsize businesses (SMBs) relied on the comfortable assumption that attackers were focused on the big enterprises, global brands, and household names. If an SMB was compromised, it was usually framed as incidental, the byproduct of a broader campaign aimed elsewhere.
In 2026, that story no longer holds.
AI has fundamentally reshaped the economics and mechanics of cybercrime. Rather than merely accelerating attacks, AI is also changing the scale of cybercrime, including who gets targeted and how much damage can occur before humans can react. Where threat actors once carefully researched and selected targets, AI-driven automation has sped up the process of crawling the public internet, harvesting vulnerabilities and deploying exploit code and malware across millions of endpoints in mere minutes. Attacks once required enough human effort that SMBs simply weren’t worth the trouble. There wasn’t a high enough return on investment for attackers to bother. Now that most attacks are automated, unfortunately we are seeing them happen across a geometrically increased number of targets.
In many cases, attackers don’t even care who they’re hitting. AI enables broad “open-net” attacks that sweep up any organization with an online presence. If a vulnerability exists, it will be found and exploited at scale.
This reality puts SMBs squarely in jeopardy. They aren’t being targeted because they’re suddenly more strategically valuable; they’re being targeted because it is less expensive to perform the “open-net” attacks. Unfortunately, SMBs also tend to be more structurally exposed and have fewer dedicated security resources, less mature detection capabilities, and far narrower windows to identify and recover from an incident once it begins.
According to N-able’s 2025 Threat Report, detected threats against SMBs surged from roughly 48,749 in June 2024 to more than 13.3 million by June 2025, a nearly 273% increase in 12 months, reflecting attackers’ shift toward easier, scalable targets.
Yet many SMBs are still relying on resilience models designed for a slower threat era. Models that were developed at a point in time when the AI capabilities that exist today were considered inconceivable, and as such were structured around reactivity instead of continuous, automated defense and recovery.
These legacy approaches simply can’t keep up. It’s time for SMBs to adopt modern resilience practices, equipped for the new era of AI-enabled bad actors.
In practice, SMBs need to protect themselves across the entire attack lifecycle. This means from prevention, through response, and finally, recovery.
So, what does modern business resilience look like in practice?
- Automated detection and response. Automation-driven attacks require automation-backed defense. AI-enhanced monitoring can triage and prioritize alerts, dramatically reducing noise and enabling security teams to focus on true risk. Platforms embedding machine learning can handle up to 90% of alerts through automated triage, enabling faster investigation and response, all without proportionally scaling headcount.
- Layered endpoint and workload protection. This helps minimize the attack surface. Modern resilience starts with deployable endpoint defenses, vulnerability scanning, and unified remote management that extends security across workstations and servers, providing a level of visibility that was once only affordable for larger enterprises.
- Unified visibility across security operations. Disparate telemetry creates blind spots, and attackers exploit them. Consolidating endpoint, network, and identity data into a single operational view allows teams to detect patterns, identify lateral movement, and respond before minor anomalies escalate into full-scale breaches.
- Proactive cyber assurance and risk assessments. Beyond tools, SMBs can build resilience by identifying and remediating vulnerabilities before an incident occurs. Continuous risk assessments, penetration tests, and configuration audits offer a structured way to shore up defenses and keep pace with evolving threats.
- Reliable backups and recoverability testing. Backups alone aren’t enough; they must be validated as well. Modern platforms must leverage AI-driven recovery testing to automate boot checks and verify restore integrity, helping businesses avoid false confidence in backups that turn out to be compromised or incomplete when they’re most needed.
These capabilities, when combined, form the backbone of a business resilience strategy that matches the speed and scale of modern AI-driven threats.
AI has permanently changed the rules of engagement. Attacks will continue to grow faster, broader, and less discriminating. For SMBs, the question becomes whether their detection, response, and recovery processes can operate at the same machine speed as the threats they face.
Organizations that continue to rely on legacy, human-paced security models will find themselves increasingly exposed. Those that rethink resilience as always-on capability will be far better positioned to survive and grow in a threat landscape that shows no signs of slowing down in 2026 or beyond.
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ABOUT THE AUTHOR
Nicole Reineke is a technology executive and AI strategist currently serving as a Distinguished Product Manager and Director of AI Strategy at N-able. She previously served as Senior Vice President of Innovation at Iron Mountain and Senior Distinguished Engineer at Dell Technologies, and brings more than 25 years of experience leading high-tech ventures and driving enterprise innovation. In addition to holding dozens of granted patents, she teaches AI and innovation at Georgetown University and co-authors publications focused on breakthrough success and applied AI strategy.
