AI Agent Security Checklist for Teams

AI Agent Security Checklist for Teams

AI agent security is not one setting. It is a chain of boundaries.

A team agent can read messages, call tools, browse websites, run commands, schedule follow-ups, and act through connected accounts. That means the security question is not just “is the model safe?”

The better question is: where does the agent’s authority start and end?

If you are still setting up the agent itself, start with our private AI agent setup guide. This checklist is for the next step: making sure the agent is safe enough for real team workflows.

In Agent Setup reviews, we start with the boundary map before touching tool configuration. The checklist below is the order we use.

1. Define the trust boundary

Before you configure channels or tools, define who the agent serves.

OpenClaw’s security model is direct: one trusted operator boundary per gateway. It is not a hostile multi-tenant security boundary for multiple adversarial users sharing one agent or gateway.¹

That means your first checklist item is not technical. It is organizational:

• Is this a personal operator agent?
• Is this a business-scoped team agent?
• Is everyone using it in the same trust boundary?
• Are personal, company, and client credentials separated?
• Would a mistake by one allowed sender affect everyone else?

If users are in different trust groups, separate the runtime. Use separate gateways, credentials, OS users, machines, or VMs. Do not rely on prompt rules to separate people who do not share the same authority.

OpenClaw’s operator-scope docs make the same point: scopes are control-plane guardrails inside one trusted gateway domain, not hostile multi-tenant isolation.²

2. Lock down sender and channel access

Messaging channels are untrusted input surfaces.

That does not mean every sender is malicious. It means messages arrive as instructions, and an agent with tools may act on them.

Before a team can use the agent, answer these questions:

• Who can DM the bot?
• Who can mention it in a group channel?
• Are DMs paired or allowlisted?
• Are group channels mention-only?
• Does a shared channel route to a minimal-tool agent?
• Can one sender cause actions that affect other users?

The OpenClaw exposure runbook recommends pairing or strict allowlists for DMs, avoiding open access unless every sender is trusted, requiring mentions in groups unless tightly controlled, and routing shared channels to agents with minimal tools and no personal credentials.³

3. Isolate the runtime

An agent that runs directly on a personal laptop inherits too much accidental authority.

Use a dedicated runtime:

• dedicated machine, VM, container, or OS user
• dedicated browser profile
• dedicated messaging bot account
• dedicated API keys
• no personal password-manager session
• no personal cloud account unless intentionally part of the boundary

Docker’s agent security overview frames execution isolation as the first practical security domain. Isolation limits blast radius: if the agent is compromised or misdirected, it cannot reach everything on the host.⁴

For teams, this is not overkill. It is how you make the agent auditable. When the agent acts, you should know which runtime, account, and credentials it used.

4. Minimize tools and tool functions

Tools are permissions.

OpenClaw’s tools docs describe tools as callable actions such as exec, browser, web_search, message, and media generation. The model only sees tools that survive active profile, allow/deny policy, provider restrictions, sandbox state, channel permissions, and plugin availability.⁵

That is the right mental model: every visible tool is a capability the agent may try to use.

OWASP calls the core failure mode “Excessive Agency.” It comes from excessive functionality, excessive permissions, or excessive autonomy.⁶

So do not ask, “which tools are cool?” Ask:

• Does this workflow need browser access?
• Does it need shell access?
• Does it need to send messages or only draft them?
• Does it need write access or read-only access?
• Does this tool include extra functions we do not need?
• What happens if the model calls it at the wrong time?

Agent Setup treats browser, shell, cron, messaging, and node access as separate risk surfaces. A support-summary agent and a coding agent should not have the same tool profile.

5. Give the agent its own identity and credentials

Do not run a team agent through a founder’s personal token unless that is truly the intended authority.

Docker puts it simply:

“Every agent is an identity.”

— Srini Sekaran, Docker⁴

That identity needs scoped credentials.

A secure team setup should use:

• dedicated service accounts where possible
• read-only scopes when the workflow only reads
• short-lived tokens where practical
• separate keys per agent or workflow
• spend limits on model/provider keys
• documented credential owners
• a revocation plan

IBM’s security guide points to the same pattern: least privilege, access controls, sandboxing, continuous logging, auditing, anomaly detection, and lifecycle governance.⁷

The goal is not to eliminate all risk. The goal is to make the agent’s authority narrow enough that mistakes are recoverable.

6. Decide what memory may retain

Memory is useful because agents need context. Memory is risky because context can contain sensitive data.

Your checklist should distinguish:

• conversation history
• long-term memory
• uploaded files
• browser/session state
• tool outputs
• credentials and secrets

Secrets should not live in prompts, memories, screenshots, or copied logs. Sensitive files should not be mounted into runtimes unless the workflow needs them. If memory is enabled, define what can be stored, how long it lasts, who can query it, and how to delete it.

NIST’s AI Risk Management Framework is broader than agent deployment, but its core framing applies: AI risk management should be part of design, development, use, and evaluation, not a one-time afterthought.⁸

7. Require confirmation for high-impact actions

The more impact an action has, the less autonomy the agent should have.

Examples that should usually require confirmation:

• deleting files
• sending external emails
• posting to public channels
• changing production config
• spending money
• rotating credentials
• granting access
• merging or deploying code

NSA/CISA guidance for AI in operational technology is aimed at higher-risk environments than ordinary team chat, but the principle is useful: incorporate a human-in-the-loop for critical decisions and fail-safe mechanisms for worst-case scenarios.⁹

For ordinary team agents, that can be simple:

• drafts before sends
• approvals before destructive commands
• dry runs before changes
• rate limits for repeated actions
• clear rollback commands

8. Log tool calls, not just final answers

If an agent produces a strange result, the final answer is not enough.

You need to know:

• which tools it called
• in what order
• with what parameters
• what outputs came back
• what credentials or accounts were involved
• whether any approval was requested or granted

Docker’s overview makes this point in production terms: agent logging needs to capture the decision chain, not just request and response.⁴

For OpenClaw deployments, run audits and review logs before widening access:

openclaw security audit
openclaw security audit --deep

The OpenClaw security docs say the audit checks common footguns including inbound access, tool blast radius, network exposure, browser control exposure, file permissions, plugin policy, and runtime expectation drift.¹

9. Validate gateway exposure before opening access

Before exposing the gateway beyond a local/private context, write down:

• who can reach it
• how they authenticate
• which agents they can trigger
• which tools those agents can use
• what credentials are available
• how to roll back exposure

OpenClaw’s exposure runbook says to use the narrowest exposure pattern that satisfies the workflow and to run baseline checks before opening access.³

A good pre-flight looks like this:

openclaw doctor
openclaw security audit
openclaw security audit --deep
openclaw health

Then test both paths:

• authorized sender succeeds
• unauthorized sender is denied

If you only test the success path, you have not tested security.

10. Keep a rollback plan

A rollback plan is part of the security setup.

At minimum, know how to:

• disable DMs or group access
• remove broad allowlists
• return gateway access to loopback-only
• deny host exec
• disable elevated tools
• rotate gateway tokens
• rotate affected integration credentials
• review recent tool calls and config changes

The exposure runbook includes rollback steps for overexposed gateways, including disabling channel DMs, denying exec, disabling elevated tools, rotating credentials, and re-running a deep audit.³

Do not wait for an incident to figure that out.

AI agent security checklist

Before a team uses the agent for real work, verify this list:

• The trust boundary is explicit.
• Mixed-trust users have separate runtimes.
• DMs are paired or allowlisted.
• Group access requires mentions or strict channel rules.
• The runtime is dedicated.
• Browser profiles and cloud accounts are separated.
• Tools are enabled one workflow at a time.
• Shell/browser/node/cron access is treated as high risk.
• Agent credentials are dedicated and scoped.
• Secrets are not stored in prompts or memory.
• High-impact actions require confirmation.
• Tool calls are logged with parameters and results.
• openclaw security audit has been run.
• Unauthorized sender tests have been performed.
• Gateway exposure has a rollback plan.

Agent Setup can implement this checklist for teams that want a private OpenClaw agent in real workflows without blurring personal, team, and client authority. The checklist is not bureaucracy. It is how you make the agent useful without making its authority invisible.

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