The ascent of autonomous attacks and the race to contain them

Cyber risk is now a board room issue, and we have seen clear examples of this in the UK.

The 2025 Jaguar Land Rover attack left the carmaker with a £485m loss, swallowing up the £398m profit it had generated just 12 months before.

Production lines were halted for more than a month as the company shut down parts of its network, showing how quickly a cyber incident can affect business performance, operational continuity and the wider supply chain.

Now, businesses are facing a fresh type of threat made possible by AI – the autonomous attack.

Attackers can already automate parts of target research, initial access and malware development, with any manual effort shrinking rapidly.

Simultaneously, the trust layer people rely on is eroding with the spread of AI-generated content and deepfakes.

It’s a race to tackle the autonomous attack, but how do organizations formulate an effective response? AI in a cyber-attacker’s armory AI-driven automated technologies are strengthening a cyber-attacker’s armory.

Prior to leveraging AI tools , bad actors often had to commit time and resources to researching a target company before planning an attack.

Timing was critical, and a perpetrator had to manually coordinate and initiate an attack at a specific time and could simply forget.

AI doesn’t - and the rise of attack-as-a-service tools is making it possible to successfully breach organizations quickly and accurately.

Guardrails are starting to be put up around established generative AI tools, such as ChatGPT and Claude, in an effort to prevent this kind of misuse.

But hackers are finding workarounds.

Rather than relying on readily available large language models (LLMs), they are deploying their own small language models (SLMs) on local devices, often on something as basic as a Raspberry Pi computer.

From there, they can escalate attacks while hiding in the shadows.