How to prevent AI agent sprawl before it becomes a security risk

Most IT teams can tell you how many endpoints they manage, but far fewer can say how many AI agents are running across their environment, what credentials each one holds, or who would notice if one stopped being needed. That gap is about to get much wider. Gartner predicts that by 2028, an average global Fortune 500 enterprise will have over 150,000 agents in use, up from fewer than 15 in 2025.

Every one of those agents authenticates somehow, through an OAuth grant, a service account, or an API token someone created and moved on from. When agents get built faster than anyone inventories them, you lose track of who owns what, what each agent can touch, and which ones should have been shut down months ago.

Here’s how to deal with AI agent sprawl before it becomes a security risk.

Why AI agent sprawl becomes a security blind spot

Agent sprawl is the latest version of a problem IT has dealt with for years: unmanaged automation. Scripts, service accounts, and API tokens have always had a habit of appearing faster than anyone documents them, and autonomous agents follow the same pattern.

The critical IT issue is that agents often hold broader access and can act on their own, and building one no longer requires a developer or a change request. An employee can connect a few apps in Zapier, n8n, or Copilot Studio, grant the workflow access to their mailbox or a shared drive, and have an autonomous process running before lunch. None of it shows up in an asset spreadsheet or an annual review, because none of it went through IT with proper asset management practices.

The numbers show how quickly that adds up. Salesforce’s 2026 Connectivity Benchmark found that organizations already use an average of 12 agents, with that number expected to climb 67% within two years. Half of those agents run in isolated silos rather than as part of a connected system. An estimated 27% of APIs are ungoverned, and only 54% of organizations have a centralized AI governance framework with formal oversight of their agents.

» Struggling with governance? Learn how to build an IT governance framework

Accountability is the first thing to break

Once agents outnumber the records describing them, IT loses the ability to answer these basic operational questions about any one of them:

• Who owns it, and who approves changes to it?
• What identity does it authenticate with?
• Which systems and APIs can it reach?
• What data can it read or modify?
• What happens to it when its owner leaves?

Security controls fragment from there. One agent authenticates through OAuth, another through a service account, and a third through a long-lived API key a developer generated for a quick test and then forgot about. Two teams can run agents doing the same job without either knowing the other exists. When an incident does happen, responders lose valuable time just working out which autonomous components were involved before they can contain anything.

Permissions pile up and outlive their purpose

The access problem compounds in two directions:

• The first is privilege creep. Agents often inherit the permissions of whoever built them, or run through a shared admin account because it was the fastest way to get a workflow running. Broad scopes and permanent tokens to systems like Jira, GitHub, or Active Directory get granted once and never revisited. No human validates what the agent does with them, so configuration drift grows silently.
• The second is orphaning. An agent doesn’t stop running when its creator changes roles or leaves the company. Its API keys stay valid, its database access stays open, and its scheduled jobs keep firing or sit dormant until something wakes them up.

According to Dominique Locksley, Linux System Administrator at Adapt IT Holdings Limited:

Nobody left the company in that case. The project ended, and that alone was enough to orphan the agent. Dormant isn’t the same as disabled, and an agent with live credentials but no accountable owner is an access path nobody is watching. Because it was legitimately created, nothing about its activity looks unusual to the systems it touches.

» Here’s how to know if your AI agents are actually autonomous

What a governed agent estate looks like

The answer to agent sprawl isn’t fewer agents for the sake of it, or a ban that pushes builders somewhere IT can’t see. It’s an environment where every agent, however it was built, carries the same baseline that includes:

• Its own identity
• A recorded owner and purpose
• Access scoped to the job
• A place in a central record that the rest of your controls can check against

When those foundations hold, adding another agent means adding something you can account for rather than another unknown. Here are the main pillars of a governed agent estate:

Every agent is a non-human identity

The starting principle is that an agent should be treated like any other non-human identity. That means it never inherits its creator’s permissions and never runs through a shared administrator account just because it was the quickest way to get a workflow running.

Instead, each agent gets its own workload identity, with access granted on a deny-by-default basis and scoped to the specific tools and actions its function requires. Permission to read EC2 inventory doesn’t justify permission to modify instances. Permission to create a Jira ticket doesn’t extend to administering projects or users.

When access is tied to the agent rather than to a person, it can be reviewed, rotated, and revoked on its own terms, and it doesn’t quietly change when someone’s role does.

» Here’s our guide to writing an AI acceptable use policy

Use one registry as the single control point

A registry is only useful if it’s the authoritative record, not a spreadsheet someone updates when they remember. In a governed estate, registration is part of deployment itself. Each record should hold enough to answer the accountability questions from the previous section. That typically means:

• Business purpose, accountable owner, and technical owner
• Environment and deployment location
• Authentication method and the credentials or service identities it uses
• Systems, APIs, and data it can access, with its permission scope and data classification
• External integrations
• Whether it’s assistive (acting only through user interaction) or autonomous (executing actions on its own)
• A review or expiry date

That record becomes the reference point for everything else. Identity logs, credential audits, and network signals can be checked against it, and anything active but missing from the registry stands out as an exception.

Consolidation leads to fewer privileged paths

Specialized point agents aren’t a problem in themselves. The risk depends on how their access overlaps:

• Point agents work when scopes stay separate: A read-only infrastructure monitoring agent, a documentation assistant, and an endpoint diagnostic agent can each sit inside their own permission boundary without much complexity.
• They become a problem when access overlaps: Ten teams running ten agents against the same systems means ten service identities, ten sets of tokens, and ten approaches to approval and logging. That multiplies the places where a control can fail.

It makes sense to consolidate IT tools when separate agents start duplicating the same privileged capabilities, identities, and integrations against the same endpoints and identity platforms. Routing that common work through one governed agent cuts the number of privileged execution paths you have to secure and audit.

End-user IT support is a natural place to start, because it’s where teams most often build overlapping bots for the same routine requests. Robin by Atera is an AI technician that remediates Tier-1 and complex Tier-2 technical incidents end to end by autonomously taking real actions on devices, servers, mainframes, and networks, without needing a technician in the loop. It’s also an agent you need to govern, which makes it a useful example of consolidation done right: one identity, one owner, and one place in the registry. Robin by Atera is an AI technician that remediates Tier-1 and complex Tier-2 technical incidents end to end by autonomously taking real actions on devices, servers, mainframes, and networks, without needing a technician in the loop. It’s also an agent you need to govern, which makes it a useful example of consolidation done right: one identity, one owner, and one place in the registry.

• One agent across channels: Robin works across channels including email, the customer portal, Slack, and Microsoft Teams.
• End-to-end handling: It verifies the user, takes approved actions on the device, network, or cloud, and hands anything outside its scope off to a technician with full context and a summary of the actions it has taken.
• Boundaries your team sets: Playbooks structure its workflows, including approval steps, and Cloud Actions define which external tools it can act on.
• Governed like any other agent: It belongs in the registry with an owner, a defined scope, and a review date, but it’s one identity to govern instead of several.

Consolidation has limits. Some workloads justify dedicated agents, or no autonomous execution at all, including:

• Highly privileged security operations
• Sensitive financial systems
• Environments with regulatory separation requirements

Isolation is worth keeping when it creates a deliberate security boundary rather than another unmanaged integration.

» Don’t miss our guide to AI in cybersecurity

How to discover, scope, and retire every agent

A governed estate is built by finding the agents already running, bringing each one under a registry and a scoped identity, and closing them down deliberately when their purpose ends. These steps follow that lifecycle, from the first discovery sweep to the moment an agent’s last credential is revoked.

Step 1: Discover agents by correlating signals

No single data source will reveal every autonomous agent, particularly ones built outside the approved IT and AI agent stack. That means discovery should work as a correlation exercise across identity, network, and credential activity. Follow these steps:

1. Review identity logs first: In Microsoft environments, check Entra audit logs for newly created enterprise applications and service principals, OAuth consent grants, and changes to application permissions or credentials.
2. Check service-principal sign-ins for non-interactive authentication: Service-principal sign-in logs show applications authenticating with certificates or client secrets, along with the service principal, source IP, and resource accessed. Flag anything that doesn’t map to an approved workload. Confirm these logs are enabled and retained first, since some log types need specific licensing or diagnostic settings.
3. Look for recurring egress to automation and AI services: Proxy, firewall, DNS, secure web gateway, and endpoint telemetry can show repeated outbound connections to services like Zapier, n8n, AI APIs, or unfamiliar SaaS endpoints. Scheduled workflows tend to create regular traffic patterns even when the workflow itself was never documented.
4. Audit non-human credentials: Review API keys, OAuth applications, personal access tokens, service accounts, and application secrets across platforms like Microsoft 365, cloud services, internal APIs, and GitHub. Every credential should have an identifiable owner, defined permissions, creation and expiry dates, last-used data where available, and a legitimate workload behind it.
5. Correlate the findings: An unknown OAuth application, recurring traffic to an automation platform, and a newly active API token can each look harmless on their own. Together, they usually point to an autonomous workflow that skipped the approval process, and that combination should trigger an investigation immediately.

Step 2: Register agents and approve them by risk

Registration has to be lightweight enough that teams don’t route around it, but mandatory before an agent receives production credentials. Approval then scales with what the agent can do. An internal agent summarizing non-sensitive documentation doesn’t need the same scrutiny as an autonomous agent that can reset passwords, modify cloud resources, create users, or access customer data.

Higher-risk capabilities should trigger:

• A security review
• Least-privilege validation
• Credential approval
• Logging requirements
• A clear decision on where human authorization stays in the loop

The most common failure is a registry that exists on paper but isn’t enforced, which happens a lot with service accounts and automation. An inventory may technically exist, but if the entries have no meaningful owner, then credentials stay active indefinitely and nobody verifies whether the documented permissions still match what the workload actually uses. An AI agent registry implemented that way just becomes another stale asset list.

Keeping the registry current means reconciling it against the discovery signals above on an ongoing basis. That reconciliation checks existence and ownership, not behavior.

Step 3: Scope access down to the operation

Least privilege starts with removing long-lived credentials wherever the platform supports workload identities and temporary credentials.

Temporary credentials alone aren’t just-in-time (JIT) access. If an agent can keep requesting fresh credentials carrying the same privileges, those privileges are effectively standing access. True JIT authorization adds an enforcement sequence for privileged operations that looks something like this:

• The agent requests a specific privileged operation
• Policy, and any required human approval, is evaluated outside the model
• Narrowly scoped authorization is granted for that operation only, for a limited duration
• The operation executes
• The authorization expires
• The request, approval, credential use, action, and result are recorded so you’re ready to audit your AI agent activity

The model can request privilege, but it should never be able to grant privilege to itself.

Scoping also has to reach below the identity layer. Permission to call a tool shouldn’t mean freedom to pass it arbitrary commands or parameters. The execution layer should expose predefined operations with validated parameters, allowlisted resources, and explicit authorization boundaries. A remediation agent might be allowed to restart an approved service on an approved host, for example, without ever receiving unrestricted PowerShell, Bash, or admin API access.

That separation is also the strongest defense against prompt injection. User input, retrieved documents, websites, tickets, and emails are untrusted data, and instructions arriving through them must never become authorization.

Step 4: Retire agents nobody owns

Ownership needs testing. Whenever an employee leaves or changes roles (or a project ends), every agent, service account, API credential, scheduled workflow, and application registration tied to that person or project should enter an ownership review. If no new accountable owner can be identified, treat the workload as orphaned.

Agents living on endpoints won’t always surface through identity logs alone. Remote script execution through Atera’s RMM platform lets you run a check across selected devices or device groups, enumerating scheduled tasks (Get-ScheduledTask) and services (Get-Service) to find locally running agents and workflows that no registry entry accounts for. Run equivalent checks on Linux endpoints with systemctl list-timers and crontab -l. , and check /etc/cron.d/ and other users’ crontabs for a complete sweep.

Once an orphan is confirmed, retire it in this order:

• Check dependencies first: Establish whether any other application, script, or workload shares the agent’s identity or credentials before disabling anything.
• Separate shared credentials: Migrate any surviving workload to its own identity, or rotate the shared credential deliberately, so retiring one agent doesn’t cause an unrelated outage.
• Disable scheduled execution: Turn off every trigger that can start the agent, including scheduled tasks, cron jobs, systemd timers, and workflow triggers in platforms like Zapier or n8n. Doing this before revoking access is reversible, so if something unexpected breaks, you can re-enable it while you trace the dependency rather than scrambling to restore revoked credentials.
• Revoke the agent’s access: Remove its API tokens, OAuth grants, certificates, secrets, service accounts, and database permissions.
• Preserve what audits require: Archive the configuration, logs, and code before deleting anything.
• Review retained data: Handle any data the agent collected according to company policy.
• Archive or remove the code: Either hand it to an accountable owner or remove it securely.
• Confirm it’s gone quiet: Check identity and network telemetry to verify the agent has stopped communicating with corporate systems.

Step 5: Build failure boundaries around a central agent

Consolidating common work into one governed agent reduces privileged paths, but it also concentrates risk. A vulnerability, excessive permission, or compromised integration in a central agent can affect more systems than a tightly scoped point agent would, and an outage can interrupt several workflows at once.

Here’s how you plan for that from the start:

• Isolate capabilities: Make sure individual integrations or execution capabilities can be disabled without taking down unrelated functions.
• Document manual fallbacks: Every critical operation the agent performs needs a written manual procedure your team can follow.
• Keep independent emergency access: Maintain recovery access to endpoints, identities, and infrastructure that doesn’t depend on the agent itself.
• Test the isolation scenario: Confirm administrators can still recover systems through controlled alternative paths if the agent has to be taken offline during an incident.

Fewer agents, fewer blind spots

AI agent sprawl builds quietly. One unregistered workflow, one forgotten token, or one departed owner at a time, and nobody can say with confidence what has access to what. The fix is to treat each agent as a non-human identity with an owner, a defined scope, and an end date, and to keep the number of privileged identities as small as the work allows.

That last part is where consolidation pays off. Instead of every team standing up its own bot for routine IT requests, Robin handles end-user support across channels as a single governed agent, acting autonomously within the guardrails your team defines. For IT teams and MSPs building toward Autonomous IT, that’s a cleaner starting point than chasing sprawl after the fact.

» Learn more in our guide to preventing shadow AI or try Atera for free