AI Agent Governance: Identity, Access Control, and Audit for Agentic AI

AI agent governance is the set of controls that decide which AI agents may act, on whose behalf, against which tools and data, under which rules, and with what record left behind. In practice it covers five things: agent identity and authentication, authorization and access control, data controls, human approval for sensitive actions, and an audit trail. SealGate enforces all five at an MCP gateway that every agent tool call passes through, so the rules hold regardless of which model or framework the agent runs on.

Why AI agents need their own governance

Identity and access management was built for people, who click through an app, and services, whose code paths are fixed and reviewed. An agent holds a person's credentials but decides what to do next from whatever text is in its context window, including text an attacker wrote. Three properties make it hard to govern with existing controls:

• Non-deterministic behavior. The sequence of tool calls is chosen at runtime by a model. You cannot review it ahead of time the way you review a service's code.
• Broad, inherited access. An agent connected to GitHub, Google Drive, and Slack acts with the union of those permissions, all within one session.
• Injectable instructions. Any document, web page, or email the agent reads can carry a prompt injection that redirects it.

Model-level guardrails, such as a system prompt telling the agent not to share customer data, are requests to the model, and a successful injection overrides them. Governance has to sit outside the model, where a call can be denied whatever the model decided. For MCP-based agents, that is the gateway between the agent and its tools.

The pillars of AI agent governance

1. Agent identity and authentication

AI agent identity answers two questions for every call: which person is this agent acting for, and which agent client is it? Without both, an audit log shows that "the integration" did something, and no policy can treat an intern's coding agent differently from a finance lead's.

SealGate ties each connection to a user:

• Per-user API keys. Every user gets a personal connection URL with their own key in it. Admins can disable a key, which revokes it immediately. Sharing one key across a team collapses every member's activity into one identity, so the docs tell you to connect each person separately.
• OAuth sign-in for hosted clients. Hosted MCP clients can instead sign in to SealGate through OAuth, which issues a token tied to the signed-in user and keeps the key out of the client.
• Agent attribution. Sessions record which client made them (Claude Code, Cursor, ChatGPT and others), so the dashboard can show each user's most-used agent and filter sessions by agent.
• Dashboard sign-in. People sign in to the SealGate dashboard with SAML SSO or Google. Owners can make a domain SSO-only, and sensitive admin actions, such as promoting a user to admin, require a fresh re-authentication.

2. Authorization and access control

AI agent authorization decides what an authenticated agent may do. SealGate layers three mechanisms, all set in Access Control:

• Roles (RBAC). Admins define roles with a priority and assign users to them. Server access can be granted at global, role, or user level, and the most specific level wins.
• Per-tool enablement. Every tool, resource, and prompt a server exposes can be switched on or off, again per role or per user. A support role can read from the ticketing server without seeing its delete tool.
• Access Control Levels. Tools are classified PUBLIC, PRIVATE, or SECRET, and a session records the highest level it has touched. A rule can then keep a session that has read SECRET data away from outbound tools.

On top of those, policy rules written in CEL see the principal (user and roles), the tool and its arguments, and the session's history:

"contractors" in principal.roles &&
resource.server == "github" &&
resource.name == "merge_pull_request"

Attach block to that rule and contractors' agents cannot merge, whatever they are asked to do. Rules can run before a call executes or after it returns, and a new rule can be saved as tentative so it only logs matches until you trust it.

You do not have to write CEL by hand. The admin Chat page lets you describe the rule in plain language ("contractors can't merge pull requests"); the assistant drafts the rule and shows it to you as a change to confirm before anything is saved. Once saved, the rule is a CEL expression evaluated on every call, so enforcement does not depend on a model's judgment. The same gateway governs both remote MCP servers and local ones running on a user's machine.

3. Data controls

Access control decides which tools an agent can reach. Data controls decide what can leave through them.

• Lethal Trifecta tracking. SealGate flags each session once it has read private data, seen untrusted content, or called a tool that communicates externally. Tracking is always on. Enforcement, which blocks or holds the write that would complete all three, is a per-organization switch that is off by default, so teams can watch where it would fire before turning it on.
• PII detection. A pii_detect() function in policy rules checks tool arguments for emails, phone numbers, credit card numbers, and API keys, so a rule can stop an agent from sending them out.
• PII obfuscation (Enterprise). Detected values in tool results are swapped for opaque tokens before they reach the model and restored when passed back to a tool, so workflows still work. See the PII redaction guide.

For the wider picture of stopping sensitive data leaving through agents, see AI data loss prevention and AI guardrails.

4. Human approval for sensitive actions

Some actions should be allowed only with a person's sign-off. A policy rule with the require_approval action pauses the call and sends Approve/Deny buttons to the web dashboard, the desktop app, Slack, Telegram, or an inline card in the agent's own chat. Answering on one channel dismisses the prompt everywhere. If nobody answers before the timeout, the call is denied: approval gates always fail closed. See Managing Approvals.

5. Audit logging and SIEM export

Governance you cannot prove is hard to defend in a review. SealGate records every tool call in a session timeline (tool, user, agent, decision, duration, and risk flags) and keeps a separate audit log of configuration and security changes. Tool-call parameters and results are read in flight to evaluate policy but only stored if an organization opts in.

Events stream to Splunk HEC or any HTTP endpoint, either every call or security events only. They cover tool calls, blocks and flag changes, approval decisions, and admin actions, and Splunk receives them in Common Event Format.

6. Shadow MCP discovery

Governance only covers the agents and servers you know about. A developer can add an MCP server to Claude Code or Cursor in one line, and so can an injected instruction. The SealGate desktop app watches the MCP configuration of supported clients and, with auto-quarantine enabled, disables any new server until an admin approves it. Quarantine runs on the user's machine, so it only covers machines where the desktop app is running.

7. Secrets handling

Agents should never hold upstream credentials. With SealGate, the tokens for GitHub, Postgres, or Notion stay at the gateway, and the agent only ever holds its SealGate credential. Stored credentials are encrypted with AES-256-GCM using keys derived from a secret the user holds; SealGate stores only a hash of it, never the key itself (details).

AI agent governance checklist

A sensible rollout order is identity, then audit in monitoring mode, then access control, then enforcement. Watching real traffic first shows which rules would have fired without breaking working agents.

Passing a CISO review with your AI agent

If you build agents that run inside other companies, the customer's security team will review them before they go live. Their questions tend to be the same:

1. Who is the agent acting as? Show that each call is attributed to a named user in their organization, not a shared service token.
2. What can it touch? Show the list of servers and tools it can reach, and that anything else is disabled.
3. What stops it leaking data? Show the policy rules and data controls, and that they run outside the model.
4. Who approves risky actions? Show which calls need sign-off and that unanswered requests are denied.
5. Where are the logs? Show that events land in their SIEM, in their format.
6. Where do our credentials live? Show that their tokens never sit in the agent's prompt or config.

Building identity, RBAC, audit, and SIEM export into your agent product takes months. Putting SealGate between your agent and the customer's tools gives you those controls out of the box, and gives the customer's IT team a dashboard to set the rules themselves. Enterprises that cannot send traffic to a hosted service can self-host SealGate in their own environment.

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