AI Agent Governance Guide: Discover, Monitor, Set Policies
As employees and teams build and roll out AI agents across tools to improve efficiency in their day-to-day work, it has now become more important than ever before to look at how those agents are performing.
Since AI agents act autonomously, it is important to understand what kind of actions they are taking, if there are any risks that need attention.
According to a report, 94% of IT leaders are concerned about AI sprawl and only 12% use a centralized platform to manage it.
In this guide, learn what AI agent governance is and how to implement it in your organization.
Key Takeaways:
What is AI Agent Governance and Why is it Important?
AI agent governance is the continuous process of having full visibility over the autonomous activity of AI agents (in cloud environments where they are deployed) and setting up specific governance policies to ensure they do not create security and compliance risks.
Almost all organizations are deploying AI agents, but most of them lack proper visibility into the data they are accessing.
“Considering the kind of agents that users are building in their own products and within their organization, it is important now to look at what kind of actions those agents are taking, if they are accessing any private files or sensitive information. That’s the reason we are prioritizing AI agent governance.” – Pranavi Manthena, Product Manager, CloudFuze.
AI Agent Governance Processes
With a dedicated AI agent governance dashboard, CloudFuze Manage makes it simple for IT teams to get 360° visibility on AI agent activities, monitor usage, spot compliance and security gaps, act on them, and do a whole lot more.
Here are the AI agent governance steps you can follow:
Step 1: Check Overall Health in the AI Agent Dashboard
Open the Agent Governance dashboard. There, you get an overview of the entire AI agent usage and activities with key metrics like:
As you can see in the screenshot attached below, you can also check risk distribution, live activities, user activities, costs, policies, and more.
Step 2: Discover all AI Agents
Move to the “Discovery” tab to discover all AI agents deployed in your organization’s cloud tenant. After all the AI agents are discovered and listed, you can check the risk level of each agent and decide whether to approve or disapprove them.
CloudFuze Manage makes the AI agent discovery more structured with details on the vendor, owner, and the LLM model behind each agent.
Moreover, you can discover AI agents across various platforms, such as Copilot Studio, SharePoint, Azure AI Foundry, Google Workspace, Google Vertex AI, and more.
Step 3: Understand User Conversations in AI Agents
Navigate to the “User Activity” section and click on the “User Chats” option to see how users are communicating with AI agents.
Step 4: Monitor Safety in AI Conversations
You can check the safety of user conversations with AI agents under the “AI Safety” option. It will surface all the sensitive information users have shared with AI agents, such as financial information, PII, passwords, and more.
Step 5: Check User Activity-Based Risks Levels on AI Agents
The “Risk “Management” panel provides a detailed snapshot of the risks associated with AI agent usage. It shows the active and inactive status of each agent along with risk level, risk score, total user chat sessions, etc.
It also lists details on risk factors, such as broad connector scopes, organization-wide consent, sensitive keywords, and more.
Step 6: Analyze Knowledge Files Used for Building AI Agents
When you move to the next panel, “Knowledge & Files,” you can check the knowledge files that users have used to build their AI agents. These reports help you understand whether those knowledge files contain sensitive information.
Step 7: Check App Permissions and File Access of AI Agents
In the “Data Activity” panel, you can check the app permissions and read or write file access that AI agents have across your organization’s cloud tenant.
Upon scrolling down, you can check the files that AI agents have access to and whether or not they have been used and changed.
Step 8: Check Stale AI Agents
Move to the next tab “Stale Agents” to get a detailed report on AI agents that haven’t been used. This report can help you understand whether the agents that have been sitting unused for more than 30 days are creating security risks.
Step 9: Check the Cost of Using AI Agents
Next, the “Cost” panel generates a detailed report on the cost of using AI agents across your organization based on tokens usage. You can check the input and output cost of each agent and the total tokens used.
Step 10: Set AI Agent Governance Policies
In the “Policies” panel, you can set up various governance policies to ensure secure and compliant AI agent deployment and usage across the organization. For example, you can set up a governance policy to create alerts whenever any AI agent uses HTTP connectors or SQL connectors.
Understand More Through a Demo
Interested in diving deeper into all the functionalities of our AI agent governance tool? We are just a request away. Contact today for a free and no-obligation demo and consultation.
Frequently Asked Questions
1. What are the best platforms for AI agent governance in enterprise settings?
With CloudFuze Manage, enterprises and their IT teams can govern AI agents to ensure secure and compliant usage. Since AI agents function autonomously, it is important to have proper governance policies in place that prevent them from accessing unauthorized information.
2. What are the key challenges in governing autonomous AI agents?
Traditional IT governance practices do not work for autonomous AI agents. Governing them requires a comprehensive approach that focuses on creating a well-governed cloud environment for secure AI agent deployment and continuous monitoring for secure and compliant usage.
3. What are best practices for implementing AI agent guardrails
Making data and the overall cloud environment AI ready is one of the most important steps to take before deploying AI agents. This will help you make sure that the agents do not access sensitive data and information after deployment. It is also equally important to continuously monitor and govern them after deployment.
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