AI agents are moving beyond simple chatbots. They can plan tasks, use multiple tools, access business data, and take actions with little or no human involvement. That makes them useful, but it also makes them a new part of your clients’ attack surface.

For MSPs, the risk is multiplied across every tenant, endpoint, identity, and SaaS environment you manage. An agent with excessive permissions, a shared credential, or no audit trail can create a blind spot that is difficult to investigate and even harder to explain to a client after an incident.

The goal isn’t to ban AI. The goal is to secure AI agents by making their use visible, controlled, reviewable, and resilient. Use this checklist to create a repeatable baseline for your own operations and the clients you serve.

• Define approved AI use cases and assign ownership.
• Inventory every AI agent, tool, integration, and machine identity.
• Apply least privilege and unique credentials.
• Protect sensitive data from unsafe prompts, tools, and destinations.
• Vet integrations, plugins, APIs, and agent updates.
• Add human approval to high-impact actions.
• Monitor agent behavior across endpoints, identities, and logs.
• Harden the environment that agents run in.
• Prepare a response plan for agent compromise and data exposure.
• Test the controls and report progress to clients.

An AI agent is any AI-enabled system that can take action toward a larger goal instead of only returning a response to a single prompt. Examples may include:

• Coding copilots, desktop assistants, and local model runtimes
• AI-enabled integrated development environment (IDE) plugins, browser extensions, and command-line tools
• SaaS automation agents that can read, create, modify, or send information
• Help desk, sales, marketing, finance, or document-processing agents
• Security or IT operations agents that can change configurations or run commands
• Multi-agent workflows in which one agent passes data or tasks to another

A tool doesn’t need to be marketed as an “agent” to create agent-like risk. If it can authenticate to a system, access data, call an API, or trigger an action, include it in your review.

AI security starts with ownership. For each client, document what AI is allowed to do, who approved it, and who is responsible for reviewing its results.

Checklist

• Create an AI acceptable-use policy for your MSP and offer a client-ready version.
• Define approved use cases by business function and data sensitivity.
• Name a business owner and technical owner for every production agent.
• Document the systems, data, tools, and actions the agent is expected to use.
• Require a documented review before an agent is introduced, materially changed, or connected to a new system.
• Define which actions always require human approval, like changing security settings, deleting data, approving payments, disabling accounts, or communicating externally.
• Review AI agent roles in onboarding, offboarding, and change-management procedures.

Evidence to retain

Keep the approved use case, owner, data classification, permission scope, change history, and last review date with the client’s security documentation. This turns an informal technology decision into an accountable control.

You can’t secure what you can’t see. Build an inventory that covers endpoint software, browser activity, SaaS applications, OAuth grants, service accounts, API keys, tokens, and integrations.

Checklist

• Search endpoints for AI applications, coding tools, command-line utilities, plugins, extensions, and local runtimes.
• Review Microsoft 365, Google Workspace, and other SaaS tenants for AI-enabled applications and OAuth grants.
• Ask application owners to identify agents created inside business platforms.
• Record each agent’s vendor, version, environment, purpose, owner, authentication method, permissions, data access, connected tools, and last review date.
• Map each agent to the client, tenant, endpoint, user, service account, or workload where it operates.
• Identify agents using shared accounts, generic service accounts, long-lived API keys, or credentials that can’t be attributed to one workload.
• Reconcile the inventory against endpoint, identity, and log data instead of relying only on user surveys.

Evidence to retain

A useful inventory is more than a list of approved vendors. It should help secure shadow AI by showing who or what is using the agent, what the agent can reach, and what would need to be revoked if it were compromised.

Treat an AI agent like a new digital worker, not like a trusted administrator. In other words, apply non-human identity security principles: Give it an attributable identity, scoped permissions, and a reviewable audit trail. Its permissions should match its job, not the maximum access available through an integration.

Checklist

• Give every agent a unique identity and dedicated credential where the platform supports it.
• Don’t allow agents to ride on a human administrator’s account unless there is a documented, temporary exception.
• Start with read-only access and add write or execution rights only when required.
• Separate credentials by client, tenant, environment, and workload.
• Use short-lived tokens, automatic rotation, and secure secret storage where supported.
• Restrict access to only the files, mailboxes, repositories, APIs, and systems needed for the approved use case.
• Use just-in-time elevation or approval gates for administrative actions.
• Review permissions on a regular schedule and after every major workflow change.
• Revoke credentials and remove integrations when an agent is retired or no longer justified.

Evidence to retain

Capture access reviews, token-rotation settings, exception approvals, and revocation records. If you can’t explain why an agent has a permission, treat that permission as a remediation item.

Agents can expose sensitive information through prompts, memory, logs, outputs, third-party APIs, or connected tools. Data protection needs to cover the entire path, not just the AI application itself.

Checklist

• Classify the data each agent is allowed to access, process, retain, or transmit.
• Prohibit sensitive client data, credentials, regulated information, and proprietary material from being sent to unapproved AI services.
• Review vendor terms for data retention, model training, subprocessors, geographic storage, and deletion procedures.
• Prevent agents from copying secrets, credentials, or regulated data into logs, tickets, chat channels, or public repositories.
• Treat content from email, documents, web pages, and user messages as potentially untrusted input.
• Require validation before an agent follows instructions found inside external content.
• Inspect outputs before they are used to change configurations, run commands, send messages, or update client records.
• Define retention and deletion requirements for prompts, outputs, memory, and tool-call history.

Evidence to retain

Keep data-flow diagrams, vendor reviews, approved-data rules, data loss prevention (DLP) or access-control settings, and examples of human review for high-risk outputs.

An agent inherits the risk of every integration it can call. A convenient plugin or marketplace connection can expand access faster than your security review process can keep up.

Checklist

• Maintain an allowlist of approved tools, APIs, plugins, extensions, and agent frameworks.
• Review the permissions and data flows of every integration before enabling it.
• Confirm how the vendor handles vulnerabilities, security updates, incidents, and breach notifications.
• Pin versions or use controlled update channels where practical.
• Re-review an integration when its permissions, provider, model, hosting location, or behavior changes.
• Limit outbound network access and block unnecessary destinations.
• Run agents in isolated or sandboxed environments when they need to execute code or interact with sensitive systems.
• Apply rate limits and transaction limits to reduce the impact of misuse or runaway automation.

Evidence to retain

Record the integration approval, permission review, version history, vendor security documentation, and test results from a pilot or sandbox environment.

Automation is most useful when it removes repetitive work without removing accountability. The higher the potential business impact, the stronger the approval control should be.

Checklist

• Define high-risk actions that agents can’t complete without human approval.
• Require confirmation before changing security configurations, deleting or encrypting data, granting access, moving money, or communicating externally.
• Use dual approval for actions that could affect multiple clients or tenants.
• Display the agent’s identity, requested action, affected systems, and supporting evidence to the approver.
• Prevent an agent from approving its own output or escalating its own permissions.
• Test failure modes, including incorrect instructions, incomplete context, unavailable systems, and repeated execution.
• Document who can approve, deny, pause, or terminate an agent workflow.

Evidence to retain

Store approval records, rejected actions, exception history, and tabletop results. A human-in-the-loop control should be observable, not just described in policy.

AI-related activity doesn’t stay in one console. A suspicious agent action may appear as a process on an endpoint, an unusual sign-in in a cloud tenant, a new OAuth grant, a bulk file access event, or an unexpected outbound connection.

Checklist

• Log agent authentication, tool calls, API requests, data access, configuration changes, and external communications.
• Record the agent identity, human sponsor, endpoint or workload, timestamp, destination, and result of each high-risk action.
• Establish a baseline for normal agent behavior, including systems touched, operating hours, request volume, and data accessed.
• Alert on unusual permissions, new integrations, unfamiliar destinations, unusual data access, and actions outside the approved use case.
• Correlate endpoint, identity, SaaS, and network signals during investigations.
• Retain enough history to reconstruct what happened before, during, and after a suspicious action.
• Route meaningful findings to an owner who can contain the activity, not just to a mailbox full of untriaged alerts.

Evidence to retain

Maintain sample logs, alert logic, investigation timelines, escalation records, and client-ready reports. Your goal is to answer: what happened, which identity or agent acted, what data or systems were touched, and what changed afterward.

AI agents operate on the same endpoints, identities, and cloud environments that attackers target. Basic hardening reduces the number of ways an agent can be misused or turned into an intrusion path.

Checklist

• Patch operating systems, browsers, AI applications, plugins, and supporting libraries.
• Remove unused software, extensions, runtimes, service accounts, and integrations.
• Restrict script execution and administrative tools to approved business needs.
• Enforce strong authentication and phishing-resistant controls for users and administrators.
• Review risky OAuth applications, mailbox rules, forwarding rules, and delegated access.
• Monitor for exposed ports, unsecured credentials, persistence mechanisms, and suspicious process behavior on endpoints.
• Separate development, testing, and production environments.
• Use network segmentation and egress controls to limit lateral movement and unauthorized data transfer.
• Apply secure configuration baselines to identities, endpoints, browsers, and SaaS platforms.

Evidence to retain

Keep configuration assessments, patch reports, application inventories, identity reviews, and remediation records. The best AI governance program still depends on a secure foundation.

Assume an agent can be compromised, manipulated, misconfigured, or used outside its intended purpose. A response plan should be simple enough to follow during an after-hours incident.

Checklist

• Define the steps to pause or disable the agent.
• Revoke active sessions, OAuth grants, API keys, tokens, and delegated permissions.
• Rotate any secrets the agent could access.
• Isolate affected endpoints, workloads, or accounts when appropriate.
• Preserve logs, prompts, tool-call history, configuration changes, and relevant source content.
• Identify which client tenants, users, systems, and data may have been affected.
• Check for persistence, mailbox manipulation, new accounts, lateral movement, and additional integrations.
• Notify the client using an agreed escalation path and explain business impact in plain language.
• Rebuild or reauthorize the agent only after its permissions, code, integrations, and data flows have been reviewed.
• Conduct a post-incident review and update the policy, inventory, and detection logic.

Evidence to retain

Test the playbook through a tabletop exercise or controlled simulation. Measure how quickly your team can identify the agent, revoke access, scope the blast radius, and communicate next steps.

AI security isn’t a one-time assessment. Agents, permissions, integrations, models, and business use cases change quickly, so build recurring reviews into your managed service.

Checklist

• Review each agent at onboarding, at least quarterly, and whenever its owner, permissions, data, or integrations change.
• Retire agents that have no active owner or documented business purpose.
• Track open findings by severity, client, owner, due date, and remediation status.
• Report practical measures like inventory coverage, stale access removed, high-risk permissions reduced, and response exercises completed.
• Use quarterly business reviews to explain risk reduction, not just tool activity.
• Re-evaluate vendors and integrations annually, including security posture, support, incident transparency, data handling, and roadmap.
• Pilot significant changes before applying them across the full client base.

Evidence to retain

Build a reusable client report with the current inventory, highest-risk findings, completed actions, outstanding decisions, and next review date. This helps demonstrate value while protecting your team from repetitive, one-off reporting work.

AI agents can help MSPs and their clients work faster, but speed without accountability creates a new path to compromise. The strongest programs treat agents like digital workers; they have a defined purpose, a unique identity, limited access, monitored behavior, and a clear offboarding process.

Use this checklist to improve visibility, reduce unnecessary access, and build a response process your team can repeat across clients. It also gives you a practical foundation for MSP cybersecurity best practices as AI adoption grows. Then evaluate the managed endpoint, identity, and log coverage you need to keep those controls working as the environment changes.

Explore Huntress solutions for MSPs or start a free trial to see how managed security and 24/7 SOC support can extend your team.