Answering what data can Manus AI access, store, or share starts with permissions, not marketing. Every browser session, integration, file, and tool a task uses widens the data the agent reaches. Vendor terms set the ceiling on retention and sharing. Inline controls set the enforceable limit inside your environment. Evaluate both, then verify the second one continuously.

Last updated: September 2026.

What Data Can Manus AI Access, Store, or Share? Start With the Access Paths

Manus reaches whatever a task’s permissions expose: the content of prompts and uploaded files, the applications reachable from a connected account or an authenticated browser session, and the services a task calls while it runs. Manus presents itself as a general AI agent that plans and executes multi-step work rather than returning a single answer (Manus, 2026). The vendor’s public privacy materials describe processing at the company level. Per-plan retention windows, processing regions, subprocessor lists, and task visibility defaults are contract items to get in writing before approval.

Manus AI’s data surface means every path by which enterprise information reaches the agent, the model behind it, or a system the agent acts on. Access, storage, and sharing are three separate questions with three different owners: the permissions your users grant, the retention and processing terms in the plan you sign, and the controls you keep on your own side of the connection.

Data leaks are now the top generative AI security concern at 34 percent, ahead of advancement of adversarial capabilities at 29 percent, reversing the 2025 ranking (World Economic Forum, 2026). For an autonomous agent, the leak question is not only what a person types. It is what the agent reads, retrieves, and forwards on that person’s behalf.

Evaluate six paths before approving any agent of this kind. Each is a diligence item with a documented answer, not an assumption.

1. Authenticated browser sessions. Test whether the product acts inside an authenticated browser session, and record which applications, records, and actions each approved session exposes to a task, including email, customer relationship management (CRM) records, ticket queues, HR systems, and cloud storage.
2. Connected accounts and integrations. For each integration you intend to allow, get the granted scope in writing, whether access ends with the task or persists, who inside the organization may authorize it, and the revocation path.
3. Uploaded files and attachments. Spreadsheets, contracts, board decks, source code, and system exports carry personally identifiable information (PII), client records, or credentials nobody intended to send. Decide which file types a task may upload, and confirm where those files are processed.
4. Prompt and conversation content. Context accumulates across a long task. A single message looks harmless while the accumulated session carries a full account plan or an incident timeline.
5. Generated artifacts and task records. Reports, code, slides, and any replay or share feature the product offers become a second copy of the underlying data, with visibility settings of their own to confirm.
6. Autonomous execution output. Code run in a sandbox, outbound network calls, tool calls, and third-party services a task touches move data to places nobody reviewed at approval time. Confirm what network egress the execution environment allows.

Where Does Manus Process, Store, and Retain Your Data?

Manus’s public privacy materials describe data handling across the vendor’s product family. The plan you sign is where processing region and retention become commitments. The privacy hub states the scope of processing across Manus AI, Monica AI, and related services under one controlling entity (Manus Privacy Policy, 2026). Confirm which legal entity contracts with you, which services that entity covers, and how the shared scope affects your own data processing agreement.

Manus’s public privacy materials start the review. Approval still needs plan-specific terms for data categories, processing locations, retention, deletion, subprocessors, and support access. Record the retention period and processing region stated for each data type. Put any missing commitment into the agreement before approving sensitive use.

Two items belong on the open list from the start. The Manus help center groups privacy questions including whether personal data is deleted after account deletion and who can see a user’s tasks (Manus Help Center, 2026). An independent transparency assessment of Manus assigns a Grade B based on analysis of the platform’s published privacy policy and terms rather than verified internal handling practices (VerifyWise, 2026). Published documents are the ceiling of what an outside party establishes, which is why the contract and your own runtime records carry the rest of the review.

Mobile use deserves its own line item. A third-party audit of the Manus iOS App Store privacy label reports that the app integrates 10 third-party software development kits (SDKs) spanning advertising, attribution, analytics, social integration, and infrastructure, and declares cross-app tracking through identifiers, with collection of contact information, identifiers, and diagnostics (Blank Spaces, 2026). Decide whether mobile use of the agent is in scope for work data at all, then write that decision into the acceptable use policy.

What Permissions Does Manus Need, and What Can It Do With Them?

An agent’s permissions are granted once at setup and exercised continuously afterward, so scope matters more than the initial approval. With a chat assistant, access is what a user pastes in. With an agent, access is what the task encounters and what the connected accounts allow. A research task that reads a web page, a shared document, or a search result consumes untrusted content, and that content carries instructions the agent then follows.

Aurascape’s threat research team showed why that question matters. Aura Labs disclosed SilentBridge, a class of zero-click indirect prompt injection flaws in the Manus agent, with three variants sorted by the source of the untrusted content: SilentBridge-Page, SilentBridge-Search, and SilentBridge-Doc, each rated CVSS v3.1 9.8, Critical. Controlled research demonstrated email data theft, secret leakage, remote code execution, and cross-tenant access. The findings were reported on 18 September 2025, acknowledged on 5 October 2025, and mitigations were deployed in November 2025 (Aura Labs, 2026). The lesson generalizes beyond one product. An agent with browsing, file access, and execution rights is a data path that responds to content it did not author.

The research community has named and ranked these risk categories. OWASP places prompt injection (LLM01), sensitive information disclosure (LLM02), and excessive agency (LLM06) among the top risks for applications built on AI models (OWASP, 2025). Excessive agency is the one most buyers underweight, because it is granted at setup and exercised later. Model Context Protocol (MCP) is one common tool-execution pattern, not the whole agent access-control problem: agents also act through browsers, native integrations, direct API calls, and code they write and run.

Ask what the agent accesses, what actions it takes, and which control stops an out-of-policy action before execution. An approval decision should rest on that question, not on a general trust judgment about the vendor. For the wider risk picture around this product, see the companion analysis of Manus AI security and compliance risks.

What Should You Require Before Approving Manus for Enterprise Use?

Require a named owner, a specific artifact, and a recorded status for every open question, then approve against that record rather than a vendor summary. Traceability is where most agent programs are thin: only 28 percent of organizations trace agent actions back to a human sponsor across all environments, and 78 percent have no documented agent-identity policies (Cloud Security Alliance, 2026). If a task pulls client records and writes them to an external service, the audit question is unforgiving: which employee started it, under which account, with which grants. Our related guidance on AI agent identity and access management covers how teams structure that attribution.

Ask the vendor which administrative controls the specific plan tier includes, and treat the answer as evidence rather than an assumption: single sign-on, role-based access inside the console, an allowlist for which integrations a user may connect, and an export path for task and administrative logs. Where a plan tier omits one, that gap becomes a negotiation point or a boundary on what the pilot may touch.

That produces three clean outcomes. Approve when the retention, residency, sharing, and log artifacts are in hand and inline controls are live. Approve with restrictions when the artifacts are partial, scoping the pilot to non-regulated data, sanctioned enterprise accounts, and a short integration list. Hold approval when the data types in play are regulated and the matching terms are still open. Map the resulting evidence to the obligations you already carry, using the approach in our overview of AI compliance frameworks and governance for enterprise AI.

Vendor Terms vs. Runtime Controls: What Each One Establishes

Contracts govern vendor handling. Inline controls govern what users and agents send, receive, and execute during use. A questionnaire records vendor representations. Signed terms establish contractual commitments. Runtime inspection shows what actually moved. All three are necessary, and none substitutes for the others.

How Aurascape Governs Manus Use Inline

Discovery is the first enforcement point, because approval decisions age quickly. Aurascape continuously discovers AI applications, Embedded AI, AI Copilots, coding assistants, agents, and the AI services running on endpoints, then scores each one on risk and capability (Aurascape Discover and Monitor AI, 2026). The endpoint agent uses process and filesystem analysis to find local AI agent activity on laptops and servers. The outcome is a current inventory of who uses which agent, under which account type.

The AI Proxy sits on the intelligence channel between the user or agent and the model. It decrypts supported TLS-protected AI traffic inline and decodes the exchange in both directions, covering prompts and responses, files, code, account type, intent and mode, connectors, and tool calls (Aurascape Platform, 2026). It uses the full conversation as context rather than a single prompt, with Intentions describing application-specific capabilities such as upload, browse, generate code, agent mode, or invoke a tool. The outcome is policy that permits research while restricting upload of client records, for the same user, in the same session.

The Zero-Bypass MCP Gateway governs the tool-execution channel between the agent and the servers it calls (Aurascape Secure Agentic AI, 2026). Within governed MCP workflows, the Gateway marks approved tool calls. The AI Proxy checks for that mark and blocks unmarked calls before the tool executes them. Direction-aware classification inspects tool call requests and results, which makes chained activity visible: reading records through one connection and writing them out through another are two ordinary actions that combine into an exfiltration path.

Policy can allow, coach, notify, redact, redirect, block, capture, or require tenant based on the interaction and the configured rule. Enforcement happens before governed data or tool execution crosses the policy boundary. Enterprise-tenant enforcement keeps sanctioned work inside the agreement you negotiated, so the retention and processing terms you reviewed are the terms that apply. On inspected traffic and under configured policy, threat prevention detects prompt injection carried in tool results, tool poisoning, malicious URLs, and unsafe files, the control class that matches the SilentBridge findings above.

Evidence closes the loop. For governed MCP workflows, interaction records connect the user, application, server, tool, detected data, and policy decision, and Auri gives security, IT, compliance, legal, and business owners role-based access to that record in natural language. In one Aurascape deployment at a Fortune 500 financial services firm, 15,000+ developers were secured across 8 targeted AI tools, with source code and client data governed inside the interaction and enterprise account access enforced (Aurascape, 2026). That is the shape of a defensible approval. The agent stays available, and the data question has a verifiable answer.

Frequently Asked Questions

Can Manus AI access my logged-in accounts and browser sessions?

Test whether Manus acts inside authenticated browser sessions, and record which applications, records, and actions each approved session exposes to a task. Decide which integrations may be connected, by whom, and with what scope before rolling out beyond a controlled pilot.

Does Manus AI store the files and prompts it processes?

Confirm separate retention periods for prompts, files, task history, generated artifacts, and execution logs in the plan terms. Where a period is not stated for a data type, add it to the agreement before approving that data type for use.

Where does Manus process and store enterprise data?

Treat the processing region as an open contract item unless the plan terms name it for each data type. The published privacy materials establish scope at the company level, so require written residency and subprocessor commitments if data localization applies to your obligations.

Can Manus AI share enterprise data with third parties?

Request the subprocessor list, the default visibility of a task, share-link behavior, and vendor support access rules, and treat each as a separate answer. The mobile client carries its own analytics and attribution considerations reported in the Blank Spaces privacy label audit, so scope mobile use deliberately.

Is Manus AI appropriate for regulated data such as PHI, PCI, or client records?

Hold regulated data out of scope until the agreement, the retention and residency terms, and your own inline controls together support it. Regulated programs need per-interaction evidence, not a vendor attestation alone. See our guide to AI compliance frameworks for banks and investment firms.

What audit logs should we require before approving Manus AI?

Require records that tie every agent action back to the person who started it, held for a defined period and exportable. That means user identity, account type, application, tools invoked, parameters, detected data categories, and the policy decision applied.

How do we stop employees from using personal Manus accounts for work?

Detect the account type inline and enforce the enterprise tenant at the moment of use. Aurascape distinguishes enterprise tenants from personal accounts in the decoded interaction, then coaches a user toward the sanctioned account or redacts and blocks when sensitive data is in flight.

Does buying an enterprise plan answer the data question on its own?

No. A plan defines vendor commitments. Inline controls decide what leaves your environment under those commitments. Evaluate both, and measure the runtime side continuously.

Aurascape adds runtime evidence to the Manus contract review, with account attribution, tool-level records, and policy decisions for decoded interactions. Teams approve the agent with real limits in place and keep the evidence a privacy or audit review asks for. See it against your own Manus use case in a live walkthrough.

See how Aurascape governs what AI agents can access, store, and share →