Google's Gemini Enterprise Agent Platform makes it easy for organizations to create AI agents for coding, customer service, finance, HR, and other internal operations. This ease of creation has led to dedicated AI agents becoming the emerging set of identities. Every new AI agent brings its own identity, its own permissions, and its own access to data and systems. In most organizations, nobody owns the process of tracking any of it because AI agents were never treated like users, until now.
SecureAI is an AI governance and security platform that connects to AI platforms like Gemini Enterprise Agent Platform, syncing AI agent identities and permission data and controlling their assignment and ownership. When an agent is created, modified, or discovered in a connected Gemini Enterprise instance, SecureAI brings it into the relevant governance and risk workflows automatically.
Governance for AI agents in Gemini Enterprise Agent Platform
Governing your Gemini Enterprise AI agents start with knowing that an AI agent exists, what it can do, and deciding who is accountable for the AI agent. SecureAI handles these through two connected capabilities:
1. AI agent inventory
Any employee can spin up a Gemini Enterprise agent, not just IT or designated admins. Unlike traditional identities, the sheer volume of AI agents created leaves organizations with agents that have no clear owner, purpose, or accountability for what they can access. SecureAI automatically brings every Gemini Enterprise agent into inventory the moment it exists, providing a complete roster of agents, as well as high-privileged agents with elevated IAM roles or unusually broad access.
Each agent entry captures its name, instance, underlying model where exposed, current human owner, and an access graph showing connected users, servers, files, models, and other objects, including permissions, guardrails, accessible documents and tools, and what else the agent can control.
Teams can compare Unmanaged Agents with the High Privileged Agents list and the IAM Roles report to find agents that lack clear ownership and also hold broad permissions. This gives reviewers a practical way to decide where to investigate first and immediately prioritize the agents that are both unaccountable and highly privileged.
2. AI governance policies
SecureAI governs a Gemini Enterprise agent's whole lifecycle through three policy types: Ownership Policy, Access Policy, and MCP Control Policy.
Ownership Policy resolves the question of AI agent accountability through a set of rules that run in a defined priority order the moment a new agent is discovered. Each policy can be run against the current inventory and individual human owners can be assigned for different types of AI agents instantly without any manual intervention once the policies are configured. Every action is then written to an audit-friendly assignment history, giving governance teams a defensible record of not just who owns an agent today, but how that ownership was determined.
Access Policy governs what a Gemini Enterprise AI agent can view or interact with, letting governance teams define standard access protocols once and apply them consistently across AI agents rather than granting permissions ad hoc per agent.
MCP Control Policy extends governance to the tool-call level for Gemini Enterprise AI agents connected through SecureAI's MCP gateway. In practice, this looks like named policies. For example, a "filesystem-access-policy" that allows a filesystem.read_file call, or a "Block untrusted MCP clients" policy that blocks a jira.add_comment call — enforced in real time as AI agents attempt to access the tool via the MCP servers.
Gemini Enterprise AI security
Security picks up to maintain stability after governance sets things in motion. Once an AI agent has an owner and a defined access boundary, SecureAI continuously checks whether these configurations actually hold, through two capabilities:
1. AI Risk Assessment
SecureAI turns "Is our Gemini Enterprise deployment secure?" into a continuously updated score you can check at any time, with insights on which configuration is affected and how you can secure it.
Each assessment check, called a risk indicator, has a severity of Low, Medium, High, or Critical and a weight based on its importance. SecureAI also calculates exposure from the percentage of relevant configurations that fail the check. These inputs produce a Risk Score from 0 to 100, with higher values representing greater risk. The assessment covers administrative controls across data governance, identity and access management, monitoring, network access, and third-party integrations.
You can reduce the number of attack paths available to an attacker during an incident by reducing unnecessary admin accounts, OAuth grants, device connections, and data pathways. Verizon's 2026 Data Breach Investigations Report states that cloud misconfiguration accounts for 14% of global breaches, compared with 9% in 2024.
2. AI agent reports
SecureAI packages this governance and security data into reports scoped specifically to Gemini Enterprise Agent Platform.
Agent-level reports help identify recently created agents, modified agents, unmanaged agents, and high-privileged agents. This gives governance teams a way to monitor change over time, in addition to the current state.
Permission-focused reports that help identify an agent's IAM roles and agent denied policies give security teams the access-control detail needed to validate least-privilege.
SecureAI's reports also be used to check whether the AI governance process worked over a given period. A reviewer can compare the Recently Created Agents report with Agent Denied Policies and Agent IAM Roles to verify that newly discovered agents received an owner and were evaluated against the relevant IAM and access-denial policies.
Other platforms supported by SecureAI
Microsoft Copilot Studio: Audit agents created in Copilot Studio, assign owners where they're missing, and put the access those agents hold through regular review.
Microsoft Foundry: Govern agents built in Azure AI Foundry, which sit beside Copilot Studio agents in SecureAI's Microsoft reports.
Claude AI security: Review the configuration of your Claude tenant against security best practices.
GitHub Copilot security: Assess how securely GitHub Copilot is set up across your environment.
Other features of SecureAI
AI agent discovery
Find the agents built in Copilot Studio, AI Foundry, and Vertex AI, along with the models and MCP servers they use, in one inventory.
AI agent security
Map what each agent can reach, from its identity and models to its tools, knowledge, memory, guardrails, and IAM roles, and reduce access it doesn't need.
MCP gateway
Route agent traffic to MCP servers through one gateway, where policies allow, block, or redact requests based on identity, tool, and content.
AI-SPM
Score Anthropic Claude, Google Gemini, and GitHub Copilot accounts against controls for API keys, identity, data, network, monitoring, and third-party integrations.
AI agent accountability
Assign an owner to each newly discovered agent automatically using ownership policies, so a named person answers for what each agent does.
Agentic AI compliance
Back AI audits with ownership records, access certification history, and control policy audit reports from the MCP gateway.