1M Tokens: It’s Not the Size, It’s How You Use It
Architecture Over Raw Volume I saw a tweet last week from someone who was genuinely…
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.
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 inference.” This oversight, he explains, “leads to underestimated costs for continuous monitoring systems, real-time threat analysis and rapid patching mechanisms.”
Another critical blind spot, according to Steffen Schreier, SVP of product and portfolio at Telesign, is “the assumption that third-party models are thoroughly vetted and inherently safe to deploy.”
He warned that in reality, “these models often haven’t been evaluated against an organization’s specific threat landscape or compliance needs,” which can lead to harmful or non-compliant outputs that erode brand trust. Schreier told VentureBeat that “inference-time vulnerabilities — like prompt injection, output manipulation or context leakage — can be exploited by attackers to produce harmful, biased or non-compliant outputs. This poses serious risks, especially in regulated industries, and can quickly erode brand trust…”
Read Full Source: VentureBeat
By Louis Columbus
