AI adoption in enterprises never starts with a strategy deck. It starts with a connection.
A developer connects a tool to an LLM. A team installs an AI app in a SaaS platform. Someone authenticates an agent running on their laptop against SharePoint, GitHub, Salesforce or internal APIs. It works, it saves time, and it spreads.
Then IAM and security teams get questions they cannot answer fast enough: who connected what to which enterprise systems, with what permissions, using which identities, and for what purpose?
Enterprise Security for AI Agents & Non-Human Identities
AGA vs. IGA: Why We Built Agentic Governance and Administration
At first glance, these challenges sound like the traditional IAM problems. Permissions, owners, and access reviews. But agentic AI introduces a new access surface that classic IAM and Identity Governance and Administration (IGA) tools were not designed to and can’t effectively govern: agentic AI access. The “user” is often an AI service or locally running agent, the programmatic access path is powered by non-human identities (tokens, service accounts, API keys) and secrets, and the blast radius is defined by scopes, syncing, and automation rather than a single human login or action.
That is why we built Agentic Governance and Administration (AGA) as a core pillar of the Entro platform, to give security and IAM teams a practical way to discover AI connections, understand the identities and permissions behind them, and enforce policy as AI adoption scales.
The Old-New Access Plane: Governing AI Agents
AI agents do not require a brand-new security philosophy or IAM logic. The governance principles are the same ones identity and security teams already use every day: maintain an inventory, know the owner, enforce least privilege, audit changes, and define what is allowed. What changed is the subject being governed.
Agents can be connected in seconds, operate continuously, and their access is often granted through OAuth scopes and integrations powered by NHIs like tokens and service accounts. As adoption spreads across teams, ownership blurs and permissions drift. AGA applies the same proven governance muscle to AI agents, so you can answer, consistently and at scale, which agents exist, what they can reach, and who is accountable.
AGA fits naturally into the way security teams already govern identities and access. You do not need a new playbook, you need the same playbook applied effectively to a new access reality. That means making AI agents governable in the same practical way you govern any other access path: discover them, classify them, understand their permissions, assign ownership, and enforce what is allowed.
How AGA Works
At its core, AGA turns AI access and “usage” into something security and identity teams can actually govern. It does that by building 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 the agent touches
- Identities: the human, non-human or secret used to access those targets
From there, AGA delivers three practical capabilities: (1) AI Agent profiling, (2) Shadow AI discovery, and (3) AI service monitoring and enforcement.
Shadow AI Discovery
Shadow AI is not only SaaS apps and LLMs. It is the full agent footprint across endpoints, agent platforms, and cloud environments. AGA uses EDR integrations to surface AI clients and local agent runtimes on local workstations. Entro integrates natively with agent foundries (AWS Bedrock, Copilot Studio) and cloud service providers to discover the agents being created and the non-human identities they rely on (OAuth apps, IAM roles, service accounts). The result is a single, governed view of each agent: where it runs, where it was created, what it can access, and which identities power it.
In practice, Shadow AI Discovery gives IAM and security teams:
- Inventory: plug-and-play discovery of AI clients and agentic connections to enterprise services
- Identity + permissions context: the permissions/scopes granted, plus the human owner and the NHIs involved
- Attribution + adoption: who connected it, who is using it, and what is spreading across the org
- Risk + misconfig insights: signals like over-privileged agentic access to production, agents targeting many services, broad admin-consented permissions, and unverified external AI apps
- Governance controls: classification (third-party vs homegrown), dormant agent/seat visibility, plus OWASP-aligned insights and mapping
Agentic Intent Monitoring and Enforcement
Discovery tells you what exists. Monitoring and enforcement tells you what is happening, and what is allowed in your organization. AGA’s AI service controls focus on MCP activity visibility and policy enforcement, so teams can audit and govern agent behavior as it executes:
- Monitor MCP activity: visibility into tools invoked, connected services used, and session context
- Interception: detect suspicious and malicious intent patterns and block in real time where enforcement is enabled
- Leakage controls: reduce sensitive data and secret leakage with AI-focused scanning controls
- Policy + audit: define sanctioned MCP targets and AI client behaviors, enforce them consistently, and keep an audit trail of what was allowed, blocked, and why
AGA leverages Entro’s NHI lifecycle. We apply the same operational flow we use for non-human identities and secrets, and extend it to AI agents. The goal is simple: take something that is spreading fast, and make it inventory-able, classifiable, observable, owned, and enforceable.
AGA’s Lifecycle: From Discovery to Remediation
Entro helps organizations govern AI agents throughout their lifecycle:
- Discovery & Inventory: Identify AI agents and connections across your environment and bring them into a centralized inventory.
- Classification: Distinguish third-party vs homegrown, trusted vs unknown, and tag agents by purpose, context, and risk.
- Observability & Posture: Track what agents can access, how they are configured, and where permissions and scopes drift over time.
- Ownership Attribution: Tie each agent to a responsible human owner so governance and remediation are actionable.
- NHIDR: Detect suspicious or risky behavior patterns by correlating agent activity with identity, permissions, and context.
- Remediation: Enforce policy, reduce scope, remove risky connections, and operationalize fixes with auditability.
| Capability | Traditional IGA | AGA |
| Lifecycle control | Lifecycle control (Joiner, Mover, Leaver) | Discovery → Classification → Observability & Posture → Ownership → NHIDR → Remediation (govern AI agents throughout the lifecycle, from first discovery to enforced remediation). |
| Access requests and approvals | Central place to request access (apps, groups, roles). Policy-based routing to approvers (manager, app owner, data owner). Time-bound access (auto-expire), optional risk checks, ticketing integration. | AGA focuses on making AI agents discoverable and governable by mapping what exists and what it can access: agent inventory, permissions/scopes, identities involved, and human owner attribution. Policies define what is allowed via sanctioned MCP/services connections, with auditing. |
| Access reviews (certifications) – Campaigns – “compliance” | Periodic campaigns where managers and app owners review who has access. Goal: remove unnecessary access and produce audit evidence. | AGA supports review through inventory + classification + dedicated campaigns: see which agents exist, which enterprise assets they touch, the permissions/scopes granted, the NHIs powering access, and the human owner. |
| Policy and compliance controls | SoD (Segregation of Duties): prevent toxic combinations like “create vendor” + “approve payment.” Least privilege: detect over-entitlement relative to role and peers. Attestation: prove approvals, exceptions, and reviews happened | AGA policy focuses on agent governance and policy enforcement and auditing, OWASP-aligned insights/mapping, least privilege signals (over-privileged access, broad permissions/scopes, agents targeting many services), sanctioned MCP/services connections, plus leakage controls to reduce sensitive data/secret leakage, with audit trails of what was allowed/blocked and why. |
Closing: Govern AI Agents Before Sprawl Becomes the Default
AI agents are becoming a standard way to access enterprise systems. That is a productivity win, but one that creates a governance gap. Agentic Governance and Administration (AGA) brings AI agents into a governable model security and identity teams already understand: discover what exists, map identities and permissions, assign ownership, monitor activity, and enforce what is allowed, with auditability.
Watch the AGA demo to see how Entro visualizes agent access and helps teams control it in practice, or reach out for a walkthrough of AGA in your environment.
