Agentic AI: when models become operators
Why autonomy changes cybercrime
Agentic AI refers to AI systems that can plan tasks, choose actions, use tools, and iterate toward a goal. Instead of generating one response, an agent can run a workflow: search, draft, message, check results, and adjust.
In cybercrime, that matters because it turns persuasion into a process. The attacker does not need one perfect script. They need a system that keeps trying, learns what works, and scales.
What “autonomy” changes
- Speed: experiments run continuously, not when a human is online.
- Personalization: lures can be tailored using publicly available context, then refined based on replies.
- Coordination: multiple agents can split roles, such as research, writing, voice, and logistics.
- Persistence: campaigns become background activity, not one-off bursts.
Where defenders feel it first
Expect pressure on identity and communications: executive impersonation, finance approval scams, helpdesk manipulation, and customer support abuse. When voice cloning, deepfakes, and synthetic profiles are combined with rapid iteration, the target is often your process, not your firewall.
Defensive response
Defending against agentic attackers is less about catching one artifact and more about building systems that do not trust unauthenticated requests. That means strong identity controls, verification loops that are hard to socially engineer, and detection that correlates across channels.