Enterprise AI is rapidly moving from isolated pilots toward broader production use. But in many cases, engineering teams are building applications and deploying autonomous agents faster than security, governance and operational processes can keep up. This has created a fractured environment where leaders may not have a centralized view of which models are being used and how.
To become truly AI-ready, organizations must move from ad-hoc oversight to centralized governance. That means establishing a unified control plane for monitoring, governing and securing AI applications, models, agents, and interactions at scale. “For the first time, we’re seeing CISOs, CIOs and platform leaders come to the same table to discuss how to open up AI to the entire enterprise,” says Rohit Agarwal, senior director of AI Gateway at Palo Alto Networks.
AI sprawl has become an enterprise governance problem
Organizations often think about AI sprawl in terms of the growing number of models, including those that teams adopt without central approval or visibility. But the problem goes beyond the model layer alone: individual teams also build their own applications and agentic workflows on top of them, which creates a fragmented environment that becomes harder to govern as it scales.
“As more and more applications are built on top of these models, and everyone is trying to figure out their own workflows, enterprises are seeing a huge sprawl across teams,” says Agarwal. “You also end up with models that haven’t been certified by security teams, they may be really expensive, or they may not even be suitable for the kinds of use cases you have. That sprawl now needs to be managed.”
Without centralized visibility, leaders struggle to govern and secure AI use effectively. They lose sight of who owns each application, which models teams use, and where costs accumulate. That increases risk across security, compliance and cost control. The lack of oversight also makes it harder to measure and attribute value: although 96% of organizations report that they have yet to see returns from AI1 , many still can’t clearly attribute spending to specific teams, models or use cases in the first place.
Autonomous agents need identity, access and accountability
Unlike passive assistants and chatbots, agentic systems can execute tasks autonomously, significantly elevating the risk if the necessary guardrails aren’t in place. After all, agents perform many of the same tasks that people otherwise might, so it stands to reason that they need the same protections. At the very least, enterprises should treat agents more like privileged actors rather than regular software requests.
What is different is the speed and potential scale at which agentic systems operate. According to Palo Alto Network’s 2026 Identity Security Landscape Report, machine identities now outnumber humans 109 times over, up from 82 just a year ago2 . Much of that increase is down to agentic AI adoption, but that’s not all—agents can also initiate multiple actions in parallel almost instantaneously. Moreover, those actions aren’t always predictable due to the nondeterministic nature of AI.
“With human actors, you could detect problem areas, because humans still move at a certain pace. AI agents, on the other hand, can fire off dozens of different actions all at the same time before you have a chance to stop them,” says Agarwal.
Unmanaged agent identities can create excessive permissions, unclear ownership, and a larger attack surface. Because agents act quickly, controls must be in place to authenticate, authorize and enforce policy both before and during execution. That means not only extending familiar identity and access management principles to AI agents, but also being able to do so within drastically shorter remediation timelines.
An AI gateway turns fragmented activity into governed operations
With a control plane that spans all agentic models, workflows and applications, organizations can address the dual challenge of speed and scale. For instance, an AI gateway monitors traffic in real time to detect and mitigate potentially malicious activities or vulnerabilities. It also applies strict, least-privilege controls to prevent excessive permissions from creating new risks, and it allows organizations to consistently enforce policies, manage credentials, and automatically redact sensitive data across thousands of AI models.
An AI gateway is also an investment in the organization’s future, because it allows developers to adopt approved capabilities without being held back by repetitive manual approval processes. “When a new model comes out, for example, or a new agent goes live in the registry, you can have a set of controls and policies that are automatically applied to them,” says Agarwal.
This unified control plane brings security enforcement, operational governance and cost control under the same solution, rather than requiring enterprises to manage each through separate systems. With centralized logs, consistent policies and cost attribution can improve accountability and give leaders a clearer basis for deciding on what to scale.
As AI agents move from experimentation to production, organizations need a more consistent way to see what they’re doing, control what they can access, and enforce policy in real time. Learn how Prisma AIRS AI Gateway helps centralize governance and security for enterprise AI making it safer and easier to meet tomorrow’s challenges.
