Rob Gurzeev on Why AI Has Changed Cybersecurity’s Biggest Blind Spot

This is a medium-term demand tailwind for exposure-management and continuous-monitoring vendors, not an immediate earnings catalyst. The mechanism is simple: AI increases asset sprawl faster than security teams can inventory it, so budgets should migrate away from reactive tools toward platforms that continuously map external attack surface, identity drift, and cloud misconfigurations. The likely winners are the large platforms with multiple attach points and high switching costs; the smaller, point-solution names may get more logo interest but less durable pricing power.

The second-order loser set is broader than traditional cybersecurity: manual pentest shops, legacy vuln-scanning vendors, and services-heavy consultancies face margin pressure as AI automates parts of discovery and triage. In software, the risk is that AI-enabled application velocity expands exposed endpoints faster than security controls, which can raise breach/insurance costs and slow procurement in verticals with weak governance. That said, this is mostly a 1-3 month budget-cycle story unless a headline breach forces a spend re-prioritization.

The contrarian view is that the market already prices "AI security" as a must-own theme, so the incremental alpha may be overstated. The key question is whether AI creates net-new security dollars or just reallocates existing spend from bundled suites into a few narrative winners; if net retention and module attach rates do not inflect, multiple expansion can fade. What would falsify the thesis is evidence that AI consolidation actually reduces exposed assets faster than it creates them, or that security budgets decelerate despite a rising incident rate.