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Yes, a “local” AI agent can still send prompts, file excerpts, screenshots, browser content, or tool results to the cloud. “Local” may describe where an agent runs a tool or accesses files—not where its model runs, where its conversation is coordinated, or what information is sent to a provider. To judge privacy, trace each part of the specific feature and configuration: model inference, orchestration, file and process access, data transfers, execution isolation, and permissions.
What does “local” mean for an AI agent?
“Local” is a description of a particular part of a system, not a complete privacy or security guarantee. A computer may perform a step locally while a provider’s cloud service coordinates the task and runs the model. In another setup, files and processes may stay on infrastructure you control while conversation history and tool results still go to the model provider.
It helps to separate four locations that are often blurred together:
- Model inference: where the computation that generates the model’s response occurs.
- Orchestration: which service holds the conversation, decides what the agent should do next, and routes tool calls.
- Tool execution and file access: where commands run and which local devices, directories, applications, browser sessions, or connected services they can reach.
- Storage and retention: where conversation data, files, tool outputs, and run records are stored, and under what policies.
These parts can be split between a device, a customer-managed worker, and a provider’s cloud. As a result, “runs locally,” “private,” “offline,” and “not used for training” are not interchangeable claims. Each should be checked against the particular data category and system component it describes.
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Can a local AI agent still send my files or prompts to the cloud?
Yes. Local file access or local tool execution does not establish that the model runs on your device or that no context leaves it. Whether information is sent depends on the product’s data path and the feature’s configuration.
OpenAI’s documentation for ChatGPT Work’s Local computer access with Work Cloud describes cloud coordination alongside a connected computer that performs steps requiring local access. Relevant prompts, excerpts, screenshots, browser content, and tool results may be sent to OpenAI services. OpenAI explicitly cautions that local execution does not mean offline or device-only model inference. This describes that specific feature; it should not be generalized to every local agent.
Anthropic’s Claude Platform documentation describes a related distinction in its agent environments. In Anthropic’s managed cloud environment, Anthropic runs the sandbox, and conversation history, tool results, sandbox files and state, skills, and memory stores reach Anthropic. With its self-hosted sandbox option, files and processes stay on the customer’s worker, while conversation history, tool results, skills, and memory stores still reach Anthropic. Self-hosting the execution environment therefore does not by itself make the whole agent offline or keep all agent data on the customer’s systems.
Anthropic sums up the separation this way: “The agent definition is the same whichever you choose. You set the model, prompt, and tools on the agent, not on the environment.” That distinction is useful beyond this one platform: an agent’s tools and execution environment do not, on their own, tell you where its model runs or what its service receives.
How does information move through a local-plus-cloud agent?
A useful way to evaluate a setup is to follow one task from instruction to result. The exact route varies by feature; the sequence below is a map of questions to verify, not a claim that every product handles data identically.
- You give the agent an instruction. Identify where the conversation is held and whether the prompt is sent to a provider or processed by a model on your device.
- The agent decides what context it needs. It may use a file-reading tool, browser session, connected app, or another tool. Check which excerpts, page contents, screenshots, or other outputs are returned to the model or service.
- A tool runs. Establish whether execution happens on your computer, in a provider-managed sandbox, or on a worker you control. A local tool runner describes execution location, not necessarily model location.
- The tool returns a result. Find out whether tool output, files, sandbox state, conversation history, or other context reaches the provider. Results can disclose sensitive information even if the original file stays in place.
- The agent responds or takes another action. Check whether actions require confirmation, what permissions they use, and whether the system records the run. Approval and logging do not determine where the model runs or what data is transmitted.
For each step, ask which system receives the information and what information it receives. A claim that “files stay local” may refer only to the file’s storage location; it does not rule out sending selected excerpts or results for inference.
What can an AI agent access on my computer?
An agent’s access depends on the permissions of the user or service account it runs under, the operating system, the application, and the integrations enabled for it. The word “agent” does not mean every product automatically indexes or can read every file.
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That means an agent may be able to do what its running account can do through an enabled capability, but the actual boundary is product- and configuration-specific. A shared connected account can have broader privileges than an individual user’s account. Check both the computer identity and the account identities behind connected services.
Check the scope before granting access
- Files: Which directories or files can the agent read, create, edit, or delete? Is access limited to selected locations?
- Commands and applications: Can it run shell commands or interact with the desktop? Which capabilities have separate controls?
- Browser sessions: Can it see pages or act in a browser where you are already signed in? What sensitive content is visible there?
- Connected apps: Which accounts and permissions are connected? Are they personal accounts or shared accounts with wider access?
- Network: Can the execution environment contact external systems, and are outbound connections restricted?
- Oversight: Which actions require confirmation, and what information is available in logs or traces?
Do sandboxing and approval prompts make an agent safe?
They address different risks. Sandboxing limits which resources a process can access; approval settings govern when a person must confirm an action. Neither should be treated as a substitute for the other.
VS Code’s official agent trust and safety documentation distinguishes these controls in its sandboxing model: sandboxing restricts filesystem and network access for commands, while approval settings determine whether an action requires confirmation. It also says outbound network access is not blocked by default in that model. Therefore, the presence of a sandbox does not prove that a tool cannot communicate over the network; verify the actual egress rules.
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OpenAI’s sandbox agent guide describes isolated execution environments with files, commands, packages, mounts, ports, snapshots, and controlled access to external systems. It also separates the harness or control plane—which handles model calls, tool routing, approvals, tracing, and run state—from the sandbox execution plane. This is a useful design pattern for understanding the boundaries to inspect, not evidence that every commercial agent separates or restricts those layers in the same way.
What to verify in an execution boundary
- Which filesystem locations are visible, including mounted storage and files created during a run.
- Whether the agent can access credentials, ports, or external services from the environment.
- Whether network access is restricted by default or explicitly configured.
- Whether the harness that routes model calls and tool results is separate from the compute sandbox.
- What actions require confirmation and whether administrator controls can enforce those settings.
A confirmation prompt can prevent an unreviewed action when it appears and is followed, but it does not erase access the agent already has. Conversely, a filesystem boundary does not automatically govern actions performed through a connected account or a network connection. Treat the two controls as complementary.
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How can connected websites and apps create security risks?
A connected account can expose content that is private to you, and content from a website or app can try to manipulate the agent. OpenAI warns that an agent connected to websites or apps may encounter untrusted instructions that attempt to redirect it into harmful actions—for example, retrieving a code from email and sending it elsewhere.
This is a prompt-injection risk: the agent may encounter instructions embedded in content it is asked to read, even though that content is not a trustworthy instruction from you. Safeguards documented by OpenAI include confirmations for high-impact actions, refusal behavior, monitoring, and supervised “watch mode” on certain sites. OpenAI says these measures do not eliminate all risk.
Reduce exposure by enabling only the apps needed for a task, considering what is visible in logged-in sites, giving specific instructions rather than vague ones, and monitoring sensitive workflows. Avoid granting an agent broad account access for a task that needs only a narrow capability. Keep a person in the loop when the work could expose confidential information or take consequential actions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I compare a local agent with a cloud assistant?
Compare the actual data path and controls rather than treating “local” and “cloud” as two complete security profiles. A local tool runner can connect to cloud inference; a cloud assistant may have defined permissions and approval controls. The available documentation does not establish a universal security winner.
| Configuration or example | What is established | What it does not establish |
|---|---|---|
| ChatGPT Work Local computer access with Work Cloud | OpenAI describes cloud coordination and a connected computer executing steps that require local access. Relevant prompts, excerpts, screenshots, browser content, and tool results may be sent to OpenAI services. | Local execution does not establish offline operation or device-only model inference. This feature description does not establish how other agents work. |
| Anthropic-managed agent sandbox | Anthropic runs the sandbox; conversation history, tool results, sandbox files and state, skills, and memory stores reach Anthropic. | The documentation cited here does not establish a universal retention period or policy for every product, plan, or organization. |
| Anthropic self-hosted sandbox | Files and processes stay on the customer’s worker, while conversation history, tool results, skills, and memory stores still reach Anthropic. | Self-hosted execution does not establish that all data stays with the customer or that the model inference is offline. |
| Other local or cloud assistant configurations | The product’s own documentation and settings are needed to establish inference location, orchestration, access, transfers, restrictions, approvals, and retention. | No single behavior, default, or security outcome can be inferred from the labels “local” or “cloud” alone. |
For any product you are considering, check the documentation for the exact feature and configuration rather than relying on a general privacy page or a label. Confirm which data classes are covered: prompts, conversation history, file contents or excerpts, screenshots, browser content, tool results, and stored files may be handled differently. Also verify whether organizational controls apply to that feature, and review terms for retention, residency, and audit visibility rather than assuming a policy for one service or plan covers another.
What should I do before using an agent with sensitive files?
Start with the smallest task and permissions that can accomplish it. Then confirm the data path, execution boundary, and approval behavior before connecting accounts or exposing confidential material.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Choose a low-sensitivity test task. Avoid beginning with confidential files, a broadly privileged account, or a browser session containing sensitive information.
- Read feature-specific documentation. Look for where inference and orchestration occur, what information may be sent, and which device or service executes tools.
- Limit access. Grant only the required directory, capability, and connected account. Prefer a narrower account scope over a shared account with wider privileges when possible.
- Inspect isolation and network settings. Check filesystem boundaries, mounts, credentials, and outbound network controls. Do not infer that a sandbox blocks network access.
- Set confirmation requirements. Require review for high-impact actions, and understand which actions can run without a prompt.
- Observe the run. Monitor what content the agent reads and returns, and check available traces or logs for tool activity.
- Review data handling separately. Verify retention, residency, and governance terms for the specific feature, account, and organization settings you use.
If you cannot establish where model calls go or what context is transmitted, do not treat the setup as offline or device-only. Either withhold sensitive data or choose a configuration whose documented boundaries meet your needs.
What does “private,” “offline,” or “not used for training” actually tell me?
Each phrase answers a different question, and none should be treated as a shortcut for all the others. “Offline” concerns network dependence; “local” may refer only to execution or file access; “not used for training” concerns a provider’s use of data, not necessarily whether that data is transmitted or stored.
Evaluate the claim by naming the data, component, and policy it covers. For example, ask whether it applies to prompts, tool outputs, screenshots, or files; whether it applies to the model, execution worker, or orchestration service; and whether it covers the exact feature and organizational configuration. A privacy statement without those boundaries is not enough to conclude that information stays on your device.
Policies, defaults, and availability can change. Check the current documentation for the assistant and feature you actually use, and avoid extending a claim about one plan or capability to another.
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Frequently Asked Questions
Does “not used to train models” mean my files never leave my computer?
No. A provider can receive information to run a service even when a separate policy says it is not used for model training. Check the feature’s data-transfer and retention terms independently of its training policy.
Can I use a connected work account without exposing everything in it?
That depends on the integration’s permission scope and the account connected, not just the agent’s local permissions. Ask an administrator to review the actual app permissions and account scope before connecting a shared or broadly privileged account.
Who can review what an agent did during a run?
That depends on the product’s logging and tracing features and your organization’s settings; the cited documentation does not establish a common audit view across products. Before a sensitive workflow, confirm who can access run records and what those records contain.
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