Contractors and agencies can’t always be monitored the same way as employees, especially when they work on devices and accounts you don’t control. Visibility depends on what you require contractually, what happens inside your systems, and what you can verify in the finished work.
With employees, organizations often control at least some combination of the device, identity, network, and company-managed software accounts used for work. Contractors and agencies may work on their own devices and use their own accounts across multiple clients.
That changes what you can see. A contractor working in your GitHub repository or CRM may still generate useful activity data inside your systems, even if they aren’t employees. An agency that completes the work entirely in its own environment gives you little or no direct visibility into how AI was used.
The practical question is what parts of the work environment you control.
If AI use matters to your AI governance or risk requirements, address it before work begins.
CIO.com recommends AI contract terms that cover where and how AI is used, oversight responsibilities, and liability. Common AI data-processing terms can also address whether submitted data may be used for model training, which subprocessors are involved, and what happens to data when the relationship ends.
For a contractor or agency agreement, work with counsel to decide which requirements fit the engagement. Depending on the work, that may include:
A contract doesn't give you real-time visibility, but it sets expectations before the work starts.
When contractors work inside systems you administer, use the data those systems already provide.
For example, a contracted engineer added to a company-owned GitHub repository still generates commit and pull request activity in that repository. The same principle can apply to company-managed SaaS accounts or licensed seats in your own tenant.
Exactly what you can see depends on the platform, account type, permissions, and available logs. Confirm coverage system by system rather than assuming contractor activity will look exactly like employee activity.
This approach has a hard boundary: it only covers work done in systems you control.
If an agency works entirely in its own environment, focus on the quality of the work you receive.
For code, use the same review, testing, durability, and rewrite or revert checks you would apply to other external engineering work. For written or analytical deliverables, check factual accuracy, originality, sourcing, and whether the work meets the agreed requirements.
These checks can tell you whether the deliverable is acceptable.
The right approach depends on how the work gets done:
More employee monitoring won't solve a visibility gap outside the systems you control. Use the controls that fit the relationship.
Sometimes. If the contractor works through accounts, devices, or systems you administer, those systems may provide useful activity data. If the work happens entirely on outside devices and accounts, your monitoring coverage will be much more limited.
That depends on the work and your organization's legal, security, and governance requirements. If AI use affects data handling, confidentiality, quality, or compliance, the agreement is one place to define disclosure and oversight expectations. Have counsel review the specific terms.
Larridin helps organizations connect AI adoption, usage, spend, and outcomes across the enterprise. For contractor and agency work, visibility is strongest when that work happens inside systems and accounts your organization controls.