FlowSight privacy: understanding local AI and work data
By Manuel Castillejo · · 9 min read
Privacy is easier to assess when you follow the data. FlowSight analyzes raw screen context locally; connected features and information you choose to share need their own review.
Start with the information journey
A productivity application can encounter particularly sensitive information. A screen may contain client documents, messages, project names, and details that should never appear in a shared report by accident. Ask what the application observes, where it processes that information, and which destinations can receive it. Local AI answers part of that question. Understanding the whole journey also requires looking at storage, configuration, exports, synchronization, and optional services. The assessment belongs to a specific workflow, not just the product name.
FlowSight's published design analyzes raw screen context on the user's device. Reviewable summaries describe the resulting interpretation, while optional team features use selected information and aggregate signals. This is a starting point to verify against documentation and published implementation. It does not mean every operation is disconnected. An integration, a request to external AI, or a synchronization feature can introduce another recipient. Examine each separately instead of extending the local-analysis description to all possible uses of the product.
Write a simple chain for the feature you intend to use: input, processing, storage, and output. Add who can access each stage and how deletion works. Keep unanswered questions visible for documentation review or contact with the project. This exercise gives a more useful basis for evaluation than a collection of broad security adjectives. It also helps compare configurations: similar-looking functions may handle information differently if one depends on connected services and another is performed on the device.
What on-device processing contributes
Analyzing raw context locally avoids needing to send that context to a central server for the analysis itself. This reduces a particular set of transfers and keeps detailed information close to the person's working environment. For FlowSight, the separation between local detail and selected shared summaries is part of the product approach. Identify the exact information that stays on the device and verify whether the enabled function preserves that separation in your installation. A useful claim should name the operation it describes.
Reducing transfers does not remove every risk. A lost computer, shared operating-system account, outdated software, or report sent to the wrong recipient still matters. Device access controls and storage practices remain part of protection. Do not infer encryption, retention rules, or automatic exclusion of sensitive material merely because analysis is local. Those measures need documentation for the version and function concerned. The environment where the data live can be as important as the absence of a particular network transfer.
Local processing also has operational requirements. Model behavior depends on available resources, compatibility, and configuration. If an analysis is incomplete, do not fill gaps with assumptions or infer a privacy property from its speed. Review what was actually produced and whether an external service participated elsewhere in the workflow. Separating performance and privacy makes both easier to evaluate: a faster or slower response does not establish where information was processed or who could receive it.
Connected services require a separate review
An account, a payment, a request to an external model, and synchronization of a work summary involve different information and purposes. FlowSight's public documentation distinguishes the local agent from optional connected capabilities. When enabling one, identify its input, provider, and applicable terms. Do not assume the data journey of a desktop feature describes every associated service. This separation matters particularly when a user changes plans or starts using a connected capability that was outside the original evaluation.
The published FlowSight README specifically notes that using external AI through MCP can send the requested report data to that AI. This illustrates why each operation needs a destination review. A report originally stored locally can leave the device when another tool is asked to process it. Before enabling that flow, identify the content that could leave and compare it with project or client obligations. A summary still contains information, even when it is shorter and easier to read than its source context.
Prepare two scenarios: local use and use with the connected capabilities you actually need. Record purpose, recipients, and enabled options for each. This prevents an explanation valid for one scenario from being reused to justify the other. Revisit the review when adding an integration, changing model providers, or expanding sharing. The relevant configuration is the one people are using now, not an old screenshot, an introductory description, or a general phrase such as privacy-first.
Reviewing a report before sharing
Begin with purpose and audience. A client may need outcomes, decisions, and blockers rather than an exhaustive list of applications or private conversations. A team may need to understand meeting load without reading the underlying documents. Select the minimum information that answers the actual question. This improves readability and reduces exposure of context that contributes nothing to the recipient's decision. The existence of a detailed summary is not a reason to distribute every part of it.
Check facts and names next. AI-generated summaries can assign a task to the wrong project, misunderstand a screen, or describe partial work as a completed outcome. Compare claims with real deliverables and records. Remove client names, internal links, and personal details when they are unnecessary. If preparing the report using an external template, correct it in the editor before sending. Do not assume that the installed agent provides every editing control a drafting workflow might require.
Finish by opening the actual output and checking the intended recipient. Confirm the time period and content. For a shared link, examine access and revocation behavior in the service being used. Test unfamiliar options with material that is suitable for a trial. The final review belongs to the version that will leave the device: a correct draft does not establish that an export or shared file includes the same selection. Sharing introduces a new information journey that needs its own checks.
Local architecture is one part of data protection
The General Data Protection Regulation requires consideration of purpose, lawful basis, transparency, minimization, and further obligations according to the circumstances. Local architecture can reduce particular transfers, but does not supply a lawful basis or replace informing affected people. The European Commission provides public explanations of GDPR principles, and the regulation is available through EUR-Lex. FlowSight's legal and privacy pages explain the product and its stated practices. An organization's own use still requires an assessment of the actual processing and context.
Employment settings need particular care because of the relationship between employer and worker. A sharing option does not automatically establish a legitimate purpose or make every acceptance valid consent. An organization must define proportionate use, explain access, and involve responsible privacy functions and qualified advice where appropriate. Participation or consultation requirements may also apply. Complete that review before introducing processes that expand visibility into individual work. A technical capability and an appropriate organizational use are separate questions.
Secondary uses matter too. A summary created to help draft an update should not silently become a performance indicator or input to an employment decision. Activity context can omit important responsibilities, accessibility needs, and work performed away from the device. FlowSight positions AI output as advisory information for human review. Preserve that boundary in processes built around the product, and avoid inferring medical conditions, emotional states, or comprehensive judgments about people from partial activity signals.
A checklist for a real trial
Record the installed version, operating system, and enabled capabilities. Check that the installer comes from an official source or release link. Read requested permissions and connect each with the function you expect to use. Start with data and an environment appropriate for a trial, and note what appears in the summary. Avoid claims about other operating systems or versions based on one installation; releases and capabilities can advance independently, and the assessment should remain tied to the case you observed.
Choose a contained workflow, such as drafting an update for a fictional project. Review the detail produced and what would appear in the final report. Then examine only the connected functions needed for the purpose. If sharing is necessary, define content and recipient before testing the flow. If it is not needed, keep it outside the trial. The objective is to understand a useful configuration, not to accumulate features without a question that explains why they belong in the workflow.
Document answers and uncertainties: where summaries are stored, what an export contains, what an integration requests, and which deletion options are available. Verify each point against current documentation and record the date. When behavior differs from the published explanation, report the case with the minimum information necessary to investigate. Do not attach third-party documents or sensitive screen captures by default. A practical incident description should preserve privacy while explaining the version, action, and observable result.
Where to verify FlowSight's claims
The official website maintains security, privacy, GDPR, and AI transparency pages. Mancasvel/FlowSight.AI publishes implementation and instructions for inspection. These sources have complementary roles: product documentation explains intended behavior, while source can support technical review. A complete assessment also considers the installed version and connected-service configuration. Public code alone does not establish that every installation corresponds exactly to that state, nor that all services involved in a wider workflow use the same architecture.
Keep claims within their evidence. You can describe FlowSight's published approach to local screen-context analysis and optional connected capabilities. Do not turn it into a universal guarantee of anonymity, regulatory compliance, or absence of every transfer. Do not infer certifications that are not documented. Precision helps people evaluating the product and those who must explain its limitations before using it with work information. A clear limitation is part of a useful product description, especially where users are selecting a configuration.
A privacy decision should be explainable in plain language: which information is needed, why, where it is processed, who can receive it, and how it is controlled. Apply that explanation to the FlowSight workflow you use, and review reports before sharing them. Update the assessment when capabilities or recipients change. Architecture contributes to protection; purpose, information selection, and the choices made by people complete the journey. Current documentation is the appropriate place to begin checking those details.
Resources and sources
Published by FlowSight. Manuel Castillejo is the creator of the project.