The AI governance mirage: Why 72% of enterprises don’t have the control and security they think they do

Decision makers at 72% of organizations claim to have two or more AI platforms that they identify as their "primary" layer, according to a survey of 40 enterprise companies conducted by VentureBeat last month, revealing real gaps in security and control.

For enterprise management and technical leaders, and especially security leaders, these multiple AI platforms extend the attack surfaces of most enterprises at a time when AI-driven attacks have become increasingly potent.

The multiple platforms — which include offerings from hyperscaler or AI labs like Microsoft Azure, Google, OpenAI or Anthropic, or big application companies like Epic, Workday or ServiceNow — reflect a state of sprawl that has emerged as these big software providers rush to offer their own AI to their enterprise customers.

Those customers, in their own rush to scale AI, are finding they aren’t building a singular strategy — in fact they may be building a collection of contradictions.

The strategic paradox: why leading enterprises are building around their vendors

For example, take the strategic paradox faced by Mass General Brigham (MGB) hospital system, which has 90,000 employees and is the largest employer in Massachusetts. The hospital system last year had to shut down an uncontrolled number of internal proof of concepts that had sprouted up as employees had gotten carried away with AI projects, said CTO Nallan “Sri” Sriraman at the VentureBeat AI Impact event in Boston on March 26, which focused on the challenges of scaling AI.

Instead, the company decided it was better to wait for the software giants it already uses to deliver on their AI roadmaps. Since these companies have so many resources, and were making AI a top priority themselves, it made no sense for MGB to try to build its own AI layer that would be duplicative, he said. "Why are we building it ourselves?" he asked. "Leverage it."

Yet, even then, Sriraman’s team has been forced to build workarounds, where those companies haven’t done enough.

For example, MGB has just completed a “full-scaled” custom build around Microsoft’s Copilot — to get essentially everything offered by that tool — by putting a "skin" around Copilot to handle the safety and data privacy concerns the major model providers haven't yet mastered. Specifically, MGB needed a way for employees to prompt the AI and not have their protected health information (PHI) leaked back to the Copilot LLM provider, OpenAI. The new secure platform, which can support up to 30,000 users, is really the ultimate contradiction: Even though the company has a mandate to leverage the AI provided by the bigger companies, it needs to build around its failures.

The contradiction goes even further. These software vendors used by MGB — which also include Epic, Workday and ServiceNow — are all now building agents for their AI, all operating differently. So MGB has to invest in building a “control plane that coordinates and orchestrates all of these agents,” Sriraman said. “That’s where our investment is going to be.”

He noted that companies like his are “discovering and experimenting as the landscape keeps shifting." The marketplace is "still nascent," he said, which makes decisions difficult.

The "six blind men" problem

Sriraman explained the current vendor landscape with an analogy: "When you ask six blind men to touch an elephant and say, what does this elephant look like?" Sriraman said. "You're gonna get six different answers."

What emerges from the research VentureBeat conducted in the first quarter, along with conversations like the one in Boston, is a situation that we at VentureBeat are calling a “governance mirage.” While many enterprises say they have adequate governance, in reality they haven’t created clear accountability or specific guardrails, evaluations or security processes to ensure that governance.

The data of disconnect: confidence vs. systematic oversight

The research comes from surveys across January, February and March by VentureBeat of enterprise companies with 100 or more employees, with 40 to 70 qualified respondents per topic area — covering agentic orchestration, AI security, RAG and governance. The data lacks statistical significance in many areas and should be treated as directional.

The research on governance found that a majority, or 56%, of respondents said they are “very confident” that they’d detect a misbehaving AI model, suggesting that most decision-makers believe they have sufficient basic governance at their companies.

However, nearly a third of respondents have no systematic mechanism to detect AI misbehavior until it surfaces through users or audits. In a world where telemetry leakage accounts for 34% of GenAI incidents (Wiz), and the global average breach cost has hit $4.4M (IBM 2025 Cost of a Data Breach), finding out after the damage is done is the default for too many companies.

Moreover, 43% of respondents say a central team owns AI governance. That sounds reassuring — until you look at what’s happening everywhere else. Twenty-three percent say governance is unclear or actively contested between teams. Twenty percent say each platform team governs independently. Six percent say no one has formally addressed it. The rest said they were unsure who owned it.

More telling is the barrier data. When asked about the single biggest obstacle to governing AI across platforms, “no single owner or accountable team” ranked second at 29% — just behind vendor opacity. Accountability structure and lack of vendor transparency are the two dominant failure modes, and they compound each other: Without a central owner, no one has the mandate to demand transparency from the vendors.

The day-two bill: managing sprawl, creep, and lock-in

The scaling trap: Red Hat’s warning

Brian Gracely, Senior Director at Red Hat, who also spoke at the VentureBeat Boston event last month, addressed the infrastructure side of this sprawl, warning that many enterprises are falling into a trap of deceptive initial wins.

Gracely noted that the barrier to entry is almost nonexistent at the start, with nearly anyone able to spin up a project using a credit card and an API key. "Day zero is very, very easy," Gracely said. "Day two is when the bill comes due."

Red Hat is positioning its software layer (OpenShift AI) as the necessary buffer to prevent enterprises from getting buried in a single provider's proprietary ecosystem. Gracely’s point is direct: If your control system is built entirely inside one cloud provider’s toolset, you are effectively "renting a cage." The illusion of speed in the early pilot phase often hides a technical debt that becomes obvious the moment you try to move your AI work to a different platform.

Gracely illustrated this with a recent example. A senior leader from Red Hat’s centralized CTO office spent part of her vacation contributing to an open-source agent project called OpenClaw, which became widely popular in the first quarter. Within days of her name appearing as a project maintainer, Red Hat was fielding calls from major New York banks. Their problem was immediate: They realized they already had upwards of 10,000 employees bringing "claws" — agent-based tools — into their infrastructure with zero centralized oversight.

Breaches caused by employees working on these sorts of unapproved technologies are costly. These so-called “shadow AI” incidents cost on average $670K more than standard incidents, according to IBM.

Red Hat’s Gracely noted that while organizations can try to shut down these unapproved ports, they eventually have to figure out how to make them productive and secure — a task that requires a serious investment in an orchestration or platform layer.