What cybersecurity leaders need to consider: Given these expanded threats, cybersecurity leaders and decision makers must closely scrutinize whether their AI data center operators are implementing corporate policies that require technical measures to secure AI data centers across all layers of security, including hardware, data, and geopolitical. Examples of such policies include: closely inspecting hardware being deployed in AI data centers to lower the supply chain security risks, deploying faraday cages or shield chambers with compute resources to mitigate side-channel attacks, executing continuous AI audits and monitoring to identify backdoors and vulnerabilities in models and defend against AI self-exfiltration, and investing into hiring processes to ensure that foreign threat actors are not infiltrating the AI data centers.Additionally, before deploying AI tools, cybersecurity leaders need to understand where the data center hosting the AI workload is located and map the supply chain for the AI data center. This information should be used to assess whether the geographical location and supply chain creates heightened security risks due to potential state-sponsored activity or surveillance.Especially when government oversight for securing AI data centers is lacking with the release of President Trump’s new AI Action Plan, the responsibility for corporate cybersecurity leaders to review and assess AI data centers’ corporate and technical policies grows.
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