AI is everywhere in security operations now. Most teams are investing in it, experimenting with it, or at least trying to figure out where it fits. The real challenge isn’t whether to adopt AI—it’s how to apply it without making things worse.
At the Rapid7 Global Cybersecurity Summit, a session called The AI Dilemma: Automating Defense Without Surrendering Judgment tackled five assumptions that don’t hold up in practice.
Myth 1: AI will replace analysts
Not happening. AI reduces repetitive work and surfaces context so analysts can focus on decisions that need judgment. The role is evolving, not disappearing. Accountability still sits with people.
Myth 2: More automation means better security
Depends where you put it. Teams see the most value in enrichment, summarization, and triage—high-volume data tasks. High-impact actions like containment or config changes still need human oversight.
Myth 3: Speed beats transparency
Wrong. As AI adoption grows, trust matters more. Analysts need to understand how a conclusion was reached before acting on it, especially under pressure. Explainability builds confidence.
Myth 4: AI is just about efficiency
Efficiency is part of it, sure. But AI also connects signals across fragmented environments, reduces cognitive load, and supports more consistent decisions. It changes how teams investigate by surfacing patterns that are hard to spot manually.
Myth 5: Attackers benefit more from AI
Both sides are learning fast. What matters for defenders is how they integrate AI into existing workflows rather than treating it as a standalone gadget.
The session makes a clear case: AI delivers most value on high-volume, context-heavy tasks. Analysts stay central for interpreting intent and deciding how to respond. That balance is what lets teams scale without losing confidence.
