Taming the 120 Quadrillion Token Era: Veraify Merges AI FinOps and Security

SUNNYVALE, CA – October 27, 2026 – Tokens are the new currency of the global digital economy. As enterprises rush to integrate artificial intelligence into every facet of their operations, the fundamental unit of compute has shifted from the server hour to the AI token. It is a microscopic transaction, but at enterprise scale, it is reshaping corporate balance sheets. According to a recent Goldman Sachs research report, global token consumption is projected to multiply 24-fold between 2026 and 2030, reaching a staggering 120 quadrillion tokens per month. This hyper-consumption is driven not by simple chatbots, but by the rise of 'agentic AI'—autonomous systems that continuously monitor, reason, and execute tasks in iterative loops.

For Chief Financial Officers, this explosion in consumption has created a massive blind spot. AI adoption has outpaced the financial controls necessary to govern it, leading to routine budget overruns and the proliferation of unsanctioned tools.

Today, Veraify, an AI-native security platform created by Cloudbrink, announced a major move to close this gap. The Sunnyvale-based firm has launched comprehensive Token Management capabilities, designed to give enterprises granular visibility into token usage and costs. By merging AI security guardrails with FinOps cost attribution in a single control point, the platform aims to help executives track expenditures across departments, users, and individual models, including the vast, unmonitored expanse of 'Shadow AI.'

The CFO's AI Blindspot: Unmasking the Cost of Enterprise Shadow AI

The mechanics of corporate profit are currently being undermined by a lack of unit economics in AI deployments. Industry surveys indicate that over 80 percent of employees now utilize unapproved AI tools to augment their daily workflows. In some enterprise environments, analysts have tracked over 600 distinct generative AI applications operating outside the purview of IT procurement.

This Shadow AI is not merely a policy violation; it is a growing financial liability. Recent financial index reports reveal that 83 percent of finance leaders have seen consumption-based AI costs exceed their budget expectations, with more than a third blowing past their budgets by over 25 percent.

“We are seeing organizations routinely burn through their annual AI budgets in a matter of months, entirely blind to which departments or specific models are driving the spend,” noted one enterprise FinOps analyst.

The financial risk extends beyond API billing. Unmanaged AI usage creates substantial vulnerabilities regarding data leakage and compliance violations. When employees feed sensitive corporate data or proprietary source code into unsanctioned models, the resulting breaches carry a steep penalty. Recent data breach reports indicate that organizations with high levels of unauthorized AI deployment incur a 'Shadow AI surcharge'—costing approximately $670,000 more per incident than those with strict AI governance.

The new Token Management capability addresses this directly by segregating corporate-approved AI service bills from Shadow AI token usage. It monitors diverse form factors, including web browsers, native applications, plugins, IDEs, and APIs. For the first time, functional leaders and CFOs can view a unified report that identifies exactly where unapproved AI costs are originating, allowing them to either shut down risky behavior or formalize high-ROI grassroots AI initiatives.

Single-Pass Governance: Bridging the Gap Between Security and Tokenomics

The challenge of securing AI while simultaneously counting its costs is fundamentally a problem of network physics. Traditional cybersecurity architectures rely on multi-pass inspection engines. Traffic is decrypted, scanned for malware, re-encrypted, and then perhaps decrypted again for Data Loss Prevention (DLP) or web filtering.

In the era of real-time AI generation, this multi-pass approach introduces unacceptable latency. Defending against sophisticated threats like prompt injection—where malicious instructions are embedded in user input to manipulate an LLM—requires real-time, prompt-level inspection. Adding token metering on top of this process threatens to degrade the performance of AI services to the point of user frustration.

Veraify circumvents this friction through a single-pass processing architecture. Built on its parent company's Zero Trust framework, the system decrypts, decodes, and classifies network traffic once. In that single pass, it applies all necessary security guardrails—such as prompt injection defense and data protection—while simultaneously counting and attributing token consumption.

This is made possible by leveraging dynamic, software-defined Points of Presence that spin up close to the user, ensuring ultra-low latency. By moving the policy enforcement and token metering to the user edge, the platform eliminates the need to backhaul traffic to centralized choke points, preserving the high-performance connectivity required by modern AI applications.

Navigating the Crowded AI Observability Market

The launch positions the company at the intersection of a rapidly evolving and highly fragmented market. Currently, the AI cost management landscape is divided into distinct silos.

On one side are developer-first observability platforms that provide excellent tracing and prompt management but lack enterprise-grade network security controls. On the other side are model-risk and governance platforms built primarily for data scientists to monitor model drift and bias, rather than real-time financial tracking. Meanwhile, traditional cloud FinOps tools are scrambling to adapt their infrastructure-focused dashboards to the realities of API token consumption.

What differentiates this new offering is the integration of security and FinOps at the network edge. Rather than relying on developers to manually instrument their code with cost-tracking SDKs, or waiting for end-of-month cloud provider bills, the platform intercepts and analyzes the traffic organically. This eliminates the blind spots created by third-party plugins or employee workarounds, providing a holistic view of enterprise AI consumption that spans both sanctioned infrastructure and Shadow AI.

Preparing Enterprise Infrastructure for Model Hyper-Consumption

The transition from the experimental phase of generative AI to the industrial scale of agentic AI requires a fundamental shift in how organizations manage their digital infrastructure. The Goldman Sachs projection of 120 quadrillion tokens per month by 2030 highlights a coming era of hyper-consumption.

Unlike human users who generate tokens at the speed of typing and reading, AI agents operate at machine speed. They execute tasks autonomously, often utilizing iterative reasoning loops. Poorly designed agents that constantly retry failed actions or reload massive context windows at every step can generate catastrophic architectural waste, burning through millions of tokens in minutes.

In this environment, granular unit economics are no longer optional. Enterprises must be able to tie the cost of token consumption directly to business outcomes, separating transformative AI investments from runaway algorithmic waste.

“AI Tokenomics is now a board-level issue. Every enterprise is wrestling with it,” said Prakash Mana, CEO of Cloudbrink. “AI has reached an inflection point, and executives are being asked to make adoption decisions without a clear view of the return. Visibility into token consumption is the foundation for measuring the ROI of AI. You can’t manage AI ROI if you can’t see your tokens.”

As the global economy increasingly relies on automated intelligence, the mechanics of power will favor those who can accurately measure and control their algorithmic supply chains. The integration of robust security guardrails with precise financial attribution represents the maturation of the AI industry. Governance has moved from a set of theoretical ethical principles to a hard-nosed discipline of dollars and cents, ensuring that the promise of artificial intelligence does not become an unmanageable liability.