2025 exposed that the security assumptions teams had historically relied on no longer hold up under the real-world pressure of evolving attack automation and scale. Attack timelines collapsed as impersonation scaled across email, voice, and messaging, turning trust in “what one saw or heard” itself into a potential liability. AI did not change what attackers target, but it changed how quickly they adapt, the quality and believability of those attacks, and how little margin for error defenders now have.
The lessons from 2025 are already shaping 2026. They reveal where defenses broke under pressure and where security strategies must evolve next.
The following insights reflect what security trends we saw in real environments and how those trends are expected to impact 2026.
Cybersecurity lessons learned from 2025
2025 accelerated known attack paths rather than introducing unfamiliar ones. The same weaknesses that caused breaches in previous years were exploited again, just faster and on a greater scale. AI compressed timelines across the attack lifecycle, leaving defenders with less room to discover and recover. Attackers moved quicker, adapted faster, and capitalized on gaps that many organizations still had not closed.
Here are the hard-earned takeaways from 2025 that apply, all the more so, in 2026.
Get back to basics to reduce the attack surface
Ransomware reached an all-time high last year, with more than 2,200 victims posted in Q4 alone, the largest number recorded in a single quarter. While AI increased the speed and scale of attacks, most successful breaches still followed familiar paths. Unpatched systems, exposed services, weak identity controls, and excessive access continued to account for most successful intrusions.
Existing entry points became more dangerous as automation reduced the time needed to exploit them. AI became a force multiplier for everyone.
Threat actors used AI to scale phishing, accelerate vulnerability discovery, and lower the skill barrier through ransomware-as-a-service models. Attacks that once required deep expertise could now be launched with speed, volume, and alarming consistency.
Defenders learned the same lesson. AI is no longer optional on the security side, it is required to keep up.
Not all AI is created equal
Generic large language models are useful, when applied to security detection, decoration, and attribution workflows, which is the meat and drink of Tier 1 and oftentimes Tier 2 triage.
Narrow, well-defined workflows paired with purpose-built models delivered higher task accuracy and consistency. When trained for specific tasks and tied to clear outcomes, they human accuracy in areas such as alert triage, correlation, and prioritization.
Response is not the only goal, business continuity is
From the business perspective, success shows up as uptime, recovery speed, and operational continuity, not alert volume.
This is where many security strategies broke down. Incident response and disaster recovery backup planning often lived in separate silos, creating gaps during real incidents. When ransomware hit, teams reacted but struggled to restore operations quickly.
The lesson was clear. Security must move beyond response and toward resolution. Incident response and business continuity and disaster recovery (BCDR) planning must work together, with playbooks that prioritize restoring critical systems first.
Security only succeeds when the business can continue operating.
“Trust but verify” no longer works
The lesson from 2025 was uncomfortable but necessary. Trust is no longer a default state. It is something that must be continuously earned and validated.
AI-powered impersonation made email, chat, phone calls, documents, and even video unreliable. Insider threats expanded beyond malicious employees to include fake workers, compromised vendors, and manipulated third-parties.
The old model of trust followed by verification failed under these conditions. Verification now has to come first.
Looking forward: 2026 cybersecurity predictions
The lessons from 2025 point to clear shifts already taking shape. In 2026, these trends will accelerate. They change where risk concentrates, how defenses are built, and which service providers are best positioned to succeed.
Prediction #1: AI-driven attacks will target identity first
Over the past year, trust itself became the primary attack surface. AI made impersonation easier to scale and harder to detect. Phishing became more convincing. Voice and video stopped being reliable. Credentials, sessions, and tokens were targeted more often than endpoints in a traditional software stack.
Attackers learned that identity bypasses many traditional defenses. If they can authenticate, they do not need to exploit a vulnerability.
In 2026, this trend accelerates. Identity access management, multi-factor authentication, and continuous verification move from supporting controls to front-line defenses. Static credentials and one-time verification will not hold up against AI-driven impersonation and session abuse.
Prediction #2: Agentic AI will reshape the modern SOC
Analysts are overwhelmed by alert volume, attack velocity, and expanding scope. Burnout is a structural problem caused by traditional security operations center (SOC) designs that rely too heavily on manual triage and reactive workflows.
Agentic AI changes that model. By automating alert triage, correlation, enrichment, and routine response actions, agentic capabilities reduce noise and restore focus. Human analysts are no longer consumed by volume. They are applied where judgment and expertise matter most.
We’ll likely see reduced reliance on Tier 1 staffing, as initial detection triage and contextualization within the customer environment are increasingly handled by agentic capabilities embedded across both SecOps and IT ops. Importantly, “reduced” does not mean “eliminated.”
Tier 2 and Tier 3 expertise become the core of SOC value. Investment shifts away from managing alerts and toward developing analysts who can hunt threats, investigate complex incidents, and drive proactive outcomes.
In 2026, SOC effectiveness will depend less on size and more on whether operations scale without exhausting analysts.
Prediction #3: Shadow AI will become a major enterprise risk
In 2026, the greatest AI risk will come from rapid adoption outpacing governance.
AI capabilities are being embedded directly into operating systems, productivity platforms, and business applications. Features are enabled by default. Access expands deeper into files, emails, and workflows to increase usefulness. Guardrails often lag behind speed to market.
This creates a growing visibility gap. Organizations struggle to understand where AI is active, what data it can access, and how its outputs are being used. Even well-intentioned teams can introduce risk simply by enabling new capabilities without fully understanding their reach.
In response, organizations will be forced to treat AI like any other core technology risk. Governance, access controls, monitoring, and defined usage boundaries will become standard. Frameworks such as the ISO 42001 series will move from optional reference points to practical starting lines.
Ignoring shadow AI will become as risky as ignoring shadow IT once was, just faster and harder to unwind.
Prediction #4: Small MSPs will become more competitive
AI will allow smaller managed service providers (MSPs) to punch above their weight class.
AI and automation will absorb large portions of Tier 1 and Tier 2 work, from alert triage to routine remediation. That allows smaller teams to deliver consistent outcomes without hiring at the same pace as growth.
The advantage will shift to MSPs that apply AI to well-defined processes and measurable outcomes. Those who automate chaos will struggle. Those who automate discipline will thrive. The gap between large and small providers will narrow. Execution, not headcount, will be the differentiator.
Conclusion
The organizations that perform well in 2026 will focus on disciplined integration across security, IT operations, and business continuity rather than constant tool churn. They will focus on outcomes, not alerts; resolution, not reaction.
That level of integration only works when data is shared, correlated, and actionable across the entire environment. Siloed tools fragment visibility, which delays decisions at a point where delay directly increases impact in a threat landscape that moves at machine speed.
The ConnectWise Platform is built on a shared data layer designed to unify security, IT operations, and recovery workflows. By connecting signals, context, and actions across tools and teams, we help MSPs and IT organizations reduce complexity, operate at scale, and respond with confidence. Not just by detecting threats faster, but by resolving incidents and keeping businesses running when it matters most.
