In one notable intrusion investigated by Mandiant, a financially-motivated actor compromised an organization’s cloud infrastructure and deployed an autonomous agent framework. The threat actor used an AI coding chatbot, a prompt, and a set of agent instructions to plan, build, and execute a mass credential harvesting campaign in less than six hours.
We’re also tracking adversaries using AI as an intelligent orchestrator across the entire attack lifecycle. GTIG recently observed a PRC-nexus espionage group experimenting with a tool called CC Switch to cycle across multiple accounts and swap AI models — like Claude, Codex, and Gemini — picking the best model for specific tasks, such as writing exploit scripts and drafting lures. While the underlying hacking tools aren’t new, AI turned what had been a disjointed manual process into a smooth and automated workflow.
While these machine-speed attacks sound daunting, defenders actually hold an asymmetric advantage. Even when armed with autonomous AI, an attacker operates from the outside with limited context — probing in the dark, guessing connections, and hoping a compromised credential leads to a useful asset.
Defenders, on the other hand, possess deep context that attackers don’t have. You know your code, cloud configurations, user identities, deployment realities, and internal architecture better than anyone. When you feed this rich, multi-dimensional internal observability into security models, AI defense becomes inherently faster and more accurate than AI offense.
To take advantage of your deep context, it’s imperative to shift from manual, human-scale incident response to machine-speed security operations. We can no longer rely on human analysts manually triaging endless backlogs of static alerts.
By codifying our frontline threat intelligence directly into these AI models, these autonomous agents can continuously monitor for, investigate, prioritize, and remediate attacks.
How Google is helping defend the ecosystem
As adversaries adopt AI, we have a unique opportunity to disrupt them at the source. As a major security and AI provider, we take this responsibility seriously, using multiple levers to stay ahead.
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Disabling malicious infrastructure. If you use Google tools to facilitate an attack, you lose access to those tools. We proactively disable the projects, accounts, and assets of known bad actors.
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Hardening our AI models and classifiers. We operate a continuous feedback loop for our AI models. By feeding threat intelligence directly back into product development, our models learn to recognize and refuse malicious requests before an attack can even be generated.
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Automating vulnerability hunting and patching also disrupt adversaries. We are moving from manual patching to AI-driven hunting. Tools like CodeMender automatically fix critical vulnerabilities in the code itself.
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Developing advanced defenses and threat models. Our teams at Google DeepMind are building specialized defenses for generative AI — deploying active monitoring across our entire ecosystem to identify misuse in real-time.
Securing the AI era can’t be achieved with the disconnected, manual tools of the past, and you can only defend against an AI-powered threat with an AI-powered defense. To tip the scales back in favor of defenders, we must transition to a continuous, machine-speed model of protection — and at Google, we are committed to building that secure future alongside you.
To learn more about our approach to securing the AI era, please check out our new Mandiant AI Risk and Resilience report.
