Employees are deploying Microsoft 365 Copilot Studio agents faster than governance processes can follow. Each one accumulates its own permissions and tool connections, and in most organizations, nobody is tracking who built what, what it can reach, or whether anyone is still responsible for it. Security teams rarely see the full picture until after an agent is already in use, calling tools and handling data with whatever permissions its creator gave it.
SecureAI is ManageEngine's platform for AI agent security and governance. It connects to Microsoft's environment, reads your agents' identities and permission data from Copilot Studio alongside Microsoft Foundry, and brings new and existing agents into its governance workflows the moment they are discovered. SecureAI assigns owners, reviews access, and applies gateway rules before any agent reaches production.
How SecureAI governs and ensures Microsoft 365 Copilot security
Copilot Studio agents can be created without a formal approval process, which makes it paramount to have a solution that can give you a level of control over the agents. SecureAI gives governance teams visibility into every agent the moment it appears, with the policies to assign ownership and audit what those agents can do.
Copilot Studio agent inventory
Copilot Studio agents are easy to create and easy to lose track of. Teams across the business build them for customer queries, HR intake, IT support, and internal workflows. Each agent carries permissions and tool connections that are hard to keep track of without the right tools. SecureAI adds each one to inventory the moment it appears, giving governance teams a complete view of agents across the environment alongside those from every other connected platform such as Microsoft Foundry and Google Gemini Enteprise Agent Platform.
Each entry shows the agent's name, application and instance, creation date, status, and current owner. Track agents with no assigned owner to close accountability gaps before they become a risk. Identify those with elevated permissions to know where broad access exists. SecureAI's Agent Access Graph lets you track exactly what the agents can reach: roles, permissions, tools, and connected MCP services.
SecureAI reveals Copilot Studio agents that are both unaccountable and are assigned too many permissions, which are typically the highest priority for a governance team to examine.
Governance policies for Copilot Studio agents
SecureAI governs Copilot Studio agents through three policy types: Agent Ownership Policy, Control Policy, and Access Certification Campaigns.
Agent Ownership Policy
The Agent Ownership Policy puts a named person behind every Copilot Studio agent. Policies run in a defined priority order the moment an agent is discovered. It uses conditions such as Agent Name and Role as filters and assigns Entra, Google WorkSpace, and Active Directory users as accountable human agents. Agents that fall outside every configured policy appear under Unmanaged Agents for manual assignment. The result is a defensible record of who owns each agent and how that ownership was determined, which is the foundation for AI agent accountability.
Control Policy
Control Policies extend governance to the tool-call level for Copilot Studio agents connected through SecureAI's MCP Gateways. The gateway sits between an agent and the MCP servers that expose its tools; agents call the gateway URL rather than the backend server directly. The AI Tools (Copilot Studio) report shows which AI tools are active in your Copilot Studio agents, giving teams a concrete starting point for deciding which MCP servers to register.
Access Policy
At registration, each tool is classified as a read or write operation. The Access Policy can allow or block calls against conditions such as Time, Location, or Job Title, with each decision recorded in the Control Policy Audit. The Data Loss Prevention (DLP) Policy type inspects call content for sensitive data such as national ID numbers, card numbers, or custom regular-expression patterns and can redact or block matches.
Access Certification Campaigns
This add scheduled reviews of the access Copilot Studio agents already hold. Approvers re-confirm that access on the schedule you set, an Access Template scopes what each campaign covers, and reviewers approve or revoke items through Access Certification Requests. Access that survives the review has been confirmed by a named person on a fixed cycle.
Other platforms supported by SecureAI
Microsoft Foundry: Govern agents built in Azure AI Foundry, which sit beside Copilot Studio agents in SecureAI's Microsoft reports.
Gemini Enterprise Agent Platform: Check how securely your Gemini AI tenant is configured. Build an AI agent inventory of all discovered agents in Gemini and assign each an accountable human owner.
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.
AI agent governance
Give each agent an accountable owner, manage its entitlements, and run access reviews that certify or revoke what it can reach.
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.