Enterprise AI adoption is happening in small, fast steps. A developer connects a tool to an LLM. A team installs an AI app. An agent gets access to internal systems. None of this feels like a major rollout, but over time, it creates a web of connections that is hard to track.
That is the gap
Entro Security is trying to address with its new Agentic Governance & Administration (AGA) capability. The idea is that if AI agents are becoming part of how systems operate, they need to be governed like any other identity. Right now, that is not how most environments work.
The Visibility Problem Behind AI Adoption
Security teams are used to managing access for people and applications. But AI agents do not behave like either. They are created quickly, they run continuously, and they rely on non-human identities like API keys, tokens, and service accounts.
That creates a visibility problem. Teams often cannot answer simple questions about what exists in their environment and what it can access.
Itzik Alvas, CEO and co-founder of Entro Security, told MSSP Alert, “Existing IGA tools govern people and apps. They were not built to govern autonomous agents acting through non-human identities. AI agents can connect fast, operate continuously and gain access through OAuth tokens, API keys and secrets. What breaks down is the ability to answer: what is this agent, what can it reach, which identities are enabling it, who owns it and is that access still appropriate? AGA closes that gap by applying NHI governance principles to AI access.”
The issue is not a lack of governance frameworks. It is that those frameworks were designed for a different kind of user.
Why AI Changes the Access Model
In traditional environments, access is tied to a login or a defined application. With AI agents, access is more fluid. It depends on how the agent connects to systems, what permissions it inherits, and how it interacts across tools.
This means risk is no longer tied to a single account. It spreads across integrations, automation flows, and data access paths. That shift makes it harder to see where exposure actually exists.
Moving Beyond Basic “Shadow AI” Detection
Many vendors are now talking about “shadow AI,” but most tools still focus on detecting unknown applications or usage.
Entro is trying to go a step further by connecting different signals into one view. That includes endpoint activity, cloud behavior, and the identities that enable access.
Alvas explains it this way: “This is a very crowded claim now. What makes AGA different is that Entro correlates three layers most tools still treat separately: endpoint telemetry, cloud behavior and identity. We leverage EDR signals, but also Entro’s core strength in NHIs, which are the actual access points for most agents. That lets us move beyond ‘we found an AI tool you didn't know about’ to how it operates and whether its access can be governed. That is the gap CASBs and EDRs still leave open.”
That distinction matters. Finding an AI tool is one thing. Understanding what it can do and controlling it is another.
From Discovery to Control
AGA focuses on both sides of the problem. It identifies where AI agents exist and maps how they interact with systems. Then it adds visibility into what those agents are doing and whether that activity aligns with policy.
This brings governance closer to how access is actually used, not just how it is assigned.
For security teams, that means less guesswork and more context when reviewing risk.
What This Means for MSSPs
The challenge becomes even more complex for MSSPs managing multiple environments. AI agents are not limited to one organization. They are spreading across client accounts, each with different tools and integrations.
That raises the question of scale.
According to Alvas, “Yes. AGA is part of the Entro platform, not a separate product, so our MSSP customers and partners can use it across the connected environments and accounts they already manage through Entro. We built it to extend the same visibility, governance and control model to AI agents, whether that’s inside one enterprise or across many environments.”
For MSSPs, this points to a growing need to manage AI-driven access as part of their core services, not as an edge case.
AI agents are starting to act like users. They access systems, move data, and trigger actions without direct human involvement. But most governance models still treat them as background processes.
That gap is where risk builds up. What Entro is doing with AGA reflects a broader change in how identity needs to be managed. It is no longer just about people and apps. It now includes agents, integrations, and machine-driven access paths. As AI adoption continues, the ability to see and control those access paths will likely become a baseline requirement, not an advanced feature.
