Entro Security AGA brings governance and control to enterprise AI agents and access
Entro Security has launched its Agentic Governance & Administration (AGA), a new pillar of the Entro platform designed to help security and identity teams govern AI agents and AI access across enterprise systems. Applied to the new realities of AI-driven access, AGA brings governance back to fundamentals of inventory, ownership, least privilege, auditability, and enforcement as organizations accelerate adoption of AI assistants, agent platforms, and locally running agents.
“Enterprise AI adoption rarely starts with a strategy deck. It starts with a connection,” said Itzik Alvas, CEO of Entro Security. “A developer connects a tool to an LLM, a team installs an AI app in SaaS, or someone authenticates an agent against SharePoint, GitHub, Salesforce, or internal APIs. It works, spreads fast, and then security teams get questions they can’t answer fast enough. Who connected what, to which systems, with what permissions, and using which identities? Our AGA helps teams regain clarity and control as AI access becomes the default.”
AGA extends the proven IGA playbook to a new access surface
At first glance, agentic AI governance looks familiar, with permissions, owners, and access reviews. But agentic AI introduces a new access surface that traditional IAM and Identity Governance and Administration (IGA) tools were not designed to govern effectively. The user is often an AI service or locally running agent. Access paths are powered by NHIs, tokens, service accounts, API keys, and secrets. And blast radius is defined by OAuth scopes, integrations, syncing, and automation, rather than a single human login.
AGA applies the same governance muscle security and IAM teams already use, adapted to a reality where agents can be connected in seconds, operate continuously, and drift quickly as adoption spreads across teams.
How AGA works: Turning AI usage into governable access
AGA builds a structured AI agent profile from three layers:
- Sources: endpoint telemetry, agent foundries, cloud environments where NHIs are used, and MCP servers
- Targets: the enterprise assets and applications an agent touches
- Identities: the human, non-human, or secret identities used to access those targets
From there, AGA delivers two core capabilities:
Shadow AI discovery
Shadow AI is not limited to SaaS apps and LLMs. It includes the full agent footprint across endpoints, agent platforms, and cloud environments. AGA uses EDR integrations to surface AI clients and local agent runtimes on workstations, while Entro integrates natively with agent foundries (including AWS Bedrock and Copilot Studio) and cloud service providers to discover the agents being created and the NHIs they rely on, such as OAuth applications, IAM roles, and service accounts. This provides a single governed view of where an agent runs, what it can access, and which identities power it.
AI agents monitoring and enforcement
Discovery answers what exists. Monitoring and enforcement answers what is happening, and what is allowed. AGA provides MCP activity visibility and policy enforcement so teams can audit and govern agent behavior as it executes, including visibility into tools invoked and connected services, policy controls for sanctioned MCP targets and AI client behaviors, audit trails of allowed and blocked activity, and AI-focused controls to reduce sensitive data and secret exposure.