AI security cost squeeze: defenders outspend attackers, must change tactics

The article presents a structural cost crisis in enterprise AI spending, using cybersecurity as the sharpest example of a broader pattern. Uber's budget depletion in four months signals that many organizations underestimated AI operating costs when they built their deployment plans. The real problem, the author argues, is architectural: companies have defaulted to using the same expensive frontier model (a general-purpose AI built for broad reasoning) for every task, including narrow, high-volume work that does not require that capability. This is economically unsustainable at scale.

The cybersecurity domain illustrates the asymmetry starkly. Attackers operate under completely different constraints—they need to find a single vulnerability in a single application and can afford to run cheap, specialized open-source models continuously. Defenders, by contrast, face an inventory problem: they must validate security across their entire application portfolio, every deployment, and every attack vector. Running frontier models for all of that work creates a cost curve that cannot keep pace with an attacker's economics. The author, Ido Geffen (co-founder and CEO of Novee Security), proposes that the only viable solution is to own the full stack—choosing the right model for each task rather than defaulting to the most advanced one, and continuously re-optimizing that choice as new models, costs, and attack techniques emerge. The implication is that companies that treat AI as a single fungible compute resource will lose to those that build adaptive, task-specific architectures.