The use and reliance on AI is the biggest single growth area in technology. But AI is enormously energy-intensive and requires a new quality of data center.
The demand is fueling rapid growth in AI data center builds. The danger is that those building this new type of data center, at speed, do not readily understand the difference between traditional data centers and AI data centers – and the result is leaving the new AI data centers open to a new scale of risk.
Traditional data centers are primarily data processing warehouses serving a known clientele. AI data centers are more akin to high power data compute factories serving a larger and unknown clientele. Traditional data centers can comprise a series of independent servers, an AI data center must function as a single engine capable of massive parallel processing to handle a much greater computational demand. AI data centers simply cannot be built in the same way as traditional data centers.
Lava Labs has examined and now reports (PDF) on the security needs of AI data centers (The Top 10 Data Center and AI Infrastructure Security Risks) and concludes they are being built faster than they are being secured. Both traditional data centers and new AI data centers carry largely similar risks; but AI changes their exploitability and blast radius: “Systems originally designed for trusted operators are now supporting high-value, multi-tenant workloads from unrelated customers,” it notes.
