Like traditional data centers, AI data centers consist of hardware, network, storage, data and software components, which make them a goal of general cyber attack: distributed service (DDOS), ransomware, supply chain, and social engineering Attack. Data centers are also notorious to be sensitive to side-channel attacks-a cyberlack that collects or tries information to influence the processes and execution of a system-because data center hardware, from fans to Central Community Units (CPU), can reveal sensible information about CPU-tier activity, data architecture and use. For example, in July 2025, AMD found four new processors weaknesses that would allow side-channel attacks.
Risk AI face data centers
Compared to traditional people, however, AI data centers face an extended set of dangers due to the difference in hardware, data and purpose.
While large data centers use CPU and graphics processing units (GPUs), AI data centers always use GPU as AI workload requires more calculation power and because JPU allows for parallel operation. Application-specific integrated circuits and field-programable gate arrays (FPGA) are also powerful Hardware This can be easily adapted to calculate and process AI workload. Google invented an asic specific for AI and is asked to learn deeply Tensor processing unitThese more powerful resources are unsafe for side-channel attacks like CPUS: in January 2025, TpuxcractA TPU-specific side-channel attack that exploits data leaks and allows the danger actors to estimate the parameters of the AI model.
