This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency and ROI at Scale.” Read more from this special issue.
AI’s promise is undeniable, but so are its blindsiding security costs at the inference layer. New attacks targeting AI’s operational side are quietly inflating budgets, jeopardizing regulatory compliance and eroding customer trust, all of which threaten the return on investment (ROI) and total cost of ownership of enterprise AI deployments.
AI has captivated the enterprise with its potential for game-changing insights and efficiency gains. Yet, as organizations rush to operationalize their models, a sobering reality is emerging: The inference stage, where AI translates investment into real-time business value, is under siege. This critical juncture is driving up the total cost of ownership (TCO) in ways that initial business cases failed to predict.
Security executives and CFOs who greenlit AI projects for their transformative upside are now grappling with the hidden expenses of defending these systems. Adversaries have discovered that inference is where AI “comes alive” for a business, and it’s precisely where they can inflict the most damage. The result is a cascade of cost inflation: Breach containment can exceed $5 million per incident in regulated sectors, compliance retrofits run into the hundreds of thousands and trust failures can trigger stock hits or contract cancellations that decimate projected AI ROI. Without cost containment at inference, AI becomes an ungovernable budget wildcard.
The unseen battlefield: AI inference and exploding TCO
AI inference is rapidly becoming the “next insider risk,” Cristian Rodriguez, field CTO for the Americas at CrowdStrike, told the audience at RSAC 2025.
Other technology leaders echo this perspective and see a common blind spot in enterprise strategy. Vineet Arora, CTO at WinWire, notes that many organizations “focus intensely on securing the infrastructure around AI while inadvertently sidelining infer …