According to the report, there has been a 1,200% increase in phishing attacks since generative AI became popular, and there is an increasing dependence on outside parties for defensive AI capabilities This article explores use ai risk. . However, the majority of organizations are still in the early phases of methodically integrating AI into training, tooling, and governance.

In terms of visibility, risk prioritization, and policy enforcement in particular, the State of Network Security 2026 describes how AI is transitioning from experimentation to practice. These conclusions result in five useful actions. ## Step 1: Use AI for Risk Mapping and Hybrid Visibility The responses from this year indicate a change: AI investments are shifting from incident response and real-time alerts to AI-powered visibility and risk prioritization across hybrid networks. Regular security reviews should include AI results, with explicit approval from risk and compliance stakeholders.

Create AI workflows where high-impact actions are approved or overridden by humans. For recommendations produced by AI, record decision thresholds and escalation procedures. Regular security reviews should include AI results, with clear approval from risk and compliance stakeholders.

This guarantees that critical judgment is enhanced rather than replaced by AI.