Security & Privacy Overview · Last Updated: 2 September 2026
We believe the people who store their health data with us deserve to know exactly what we do with it, how we protect it, and where we currently fall short. Methos is now in open beta — this page describes the product as it actually runs today, including the parts that are still missing, and is updated with every release.
Release status: Open Beta. The service is publicly available, free of charge, with a limited feature set. The stable release is the milestone at which subscriptions (paid features) are introduced; it may bring updated Terms of Service, Privacy Policy, and Data Processing Consent, which you will confirm again. Commitments on this page phrased as "before the stable release" refer to that milestone.
Beta data migration: data stored during the Open Beta will be migrated to the stable release.
Methos is a personal data vault and MCP server. It is not a health application, a medical application, or a diagnostic tool. Its sole purpose is to provide a secure, encrypted, self-sovereign store for your personal data — including health and biometric data — and to expose that data back to you through an open, standardised protocol (Model Context Protocol), so you can query and use your own data within any AI agent or tool of your choosing.
Agent access is authorised with standard OAuth 2.1 flows — authorization-code with PKCE (your agent registers itself and you approve its access on a Methos consent screen) and client-credentials for server-to-server API keys. Access is scoped per data category and revocable at any time (see section 3).
Methos itself performs no health analysis, gives no health advice, and makes no clinical assessments. All interpretation of data is the responsibility of you and the external tools or agents you choose to connect.
What Methos does
What Methos never does
What we collect
Health and biometric data is a special category under GDPR Art. 9 and is treated accordingly. We process only what you explicitly provide or authorise:
Within the service, each ingested item is additionally assigned a fine-grained data category (e.g. blood samples, genetic data, lifestyle, medical history, wearable data, body composition, nutrition, mental health, medications, supplements, allergies, personal care, reproductive health, vaccinations, family history, sleep, stress, plus an admin-reviewed "other" bucket). This classification is technical — it drives the ingestion pipeline, MCP access-key scoping, and audit logging — and does not define separate consent scopes (see "Consent model" below). When you upload data of a kind the category set does not yet cover, the item is held under an AI-proposed label pending administrative review: the administrator sees only the proposed category label, never the item's content, and either maps the item to an existing category or accepts it under the proposed label.
Legal basis
Only data you explicitly upload or authorise for import is stored. It is used solely to provide the service to you. No cross-user profiling is performed on individual health data; anonymised aggregate metrics may be derived for internal service quality monitoring.
Encryption model
Honestly: this is not end-to-end encryption. Methos manages the keys — you don't hold them. That means the platform can technically decrypt your data. This is a deliberate design decision: server-side processing (vectorisation of your documents, retrieval by your MCP agent) has to work without you being online, and none of it would be possible with keys only you hold. In the current phase, Vault admin access — and with it the technical ability to decrypt — is held by a single person, the founder (see section 6). We would rather tell you this plainly than imply a zero-knowledge model we don't have.
The guardrails: Vault is the sole authority for key material, accessed under least-privilege policies. User identity exists only as a signed request context created once at the trust boundary — it is never taken from a request's payload, so a request can never act as another user. Every cryptographic operation and every data access is written to an audit log.
Data locality & hosting
All user data is stored and processed on a self-hosted Kubernetes cluster at the company's premises in Brno, Czech Republic — within the European Union. No data is transferred outside the EU in the normal course of operations. AI inference runs in EU Azure regions (see section 4). The cluster currently runs on two nodes, with a third prepared but kept powered off — partial hardware redundancy, not full high availability. This arrangement is temporary by design: the target setup (multi-node or managed EU hosting) will be decided based on how the service grows during the Open Beta, and any future hosting provider will be contractually bound to EU data locality.
Sub-processors
No personal data is shared with third parties for commercial or analytical purposes. The following processors receive data under a Data Processing Agreement — this is the complete list (infrastructure is self-hosted, so no hosting provider is involved):
| Processor | Role | Data received |
|---|---|---|
| Microsoft Azure AI Foundry | All AI model inference (categorisation, normalisation/anonymisation, embedding, ancillary non-health uses). EU regions only (Sweden); no training on customer data. | Document content during ingestion; normalised, de-identified text only for embedding |
| Paddle | Payment processing and subscription billing as Merchant of Record (card data, VAT, invoicing). Keeps Methos out of PCI-DSS cardholder-data scope. | Billing details only: name, email, payment/tax information. No health, biometric, or vault data. |
| SparkPost | Transactional email delivery (login magic links, service notifications). EU sending region only. | Email address, email content, and delivery metadata only. No health, biometric, or vault data. |
Data retention
Deleting your account always deletes all of your data. Audit logs (processing records, consent records, access logs) are retained independently, for the period required by law. A tiered retention model for long-lived data (daily resolution for 3 months, monthly aggregates to 1 year, yearly aggregates beyond) is designed but not yet implemented, and the handling of stored data when a paid feature is cancelled — as distinct from closing the account — will be defined before paid features are introduced.
Your GDPR rights
Access (Art. 15)
Implemented
Erasure (Art. 17)
Implemented — delete account
Consent withdrawal
Implemented — delete account
Portability (Art. 20)
Planned — export in scope for MVP
Rectification (Art. 16)
Planned
Restriction (Art. 18)
Planned
Objection (Art. 21)
Applicable
Consent model
Two distinct credential types exist, for two distinct access paths: you signing in, and your AI agent reading your data without a human session.
You: no passwords, ever
Your agent: standard OAuth, scoped, instantly revocable
.well-known metadata and registers itself dynamically — which grants nothing on its own. Access is bound on a Methos consent screen where you pick exactly one profile and the data-category scopes to grant (nothing is preselected; an empty grant is refused). Only PKCE S256 is accepted; your browser session is never shared with the agent.Security events logged: successful and failed authentication attempts, license validation failures, authorisation failures, data access, modification, and deletion operations, root-key operations, and every encryption and decryption event. Each entry records the actor (user, AI agent, or system), data category, timestamp, and operation type.
Responsible disclosure: there is no bug bounty programme yet. If you discover a vulnerability, please email [email protected]. Reports are reviewed manually; there is no guaranteed response time at this stage, and responsible disclosers are acknowledged publicly with their consent.
Methos is not an AI health application. Inside the data pipeline, AI is used for exactly three technical functions, and nothing else. None of them interprets health data, gives advice, or makes decisions with clinical, legal, or medical consequence.
| Function | Description | Used |
|---|---|---|
| Data categorisation | When you submit data, an AI model reads the content and determines a category (e.g. blood test, DNA file, training plan, wearable export, lifestyle note). The category gates the rest of the pipeline. Content classified as suspicious is quarantined and processed no further. For a normalisable category, the original content is encrypted and persisted before normalisation proceeds. | Yes |
| Normalisation & anonymisation | For applicable document categories, an AI model transforms the raw document into normalised, structured text and strips direct identifiers (PII). You review and confirm or correct this normalised content before anything is embedded — only normalised, de-identified content you have approved proceeds to the embedding step. | Yes |
| Embedding / vectorisation | Once confirmed, the normalised text is split into deterministic chunks and each chunk is converted into a vector representation stored in your personal vector index (which holds vectors and isolation identifiers only — no readable text; see section 2), enabling semantic search across your own data. The text itself stays envelope-encrypted in the database. | Yes |
| Health interpretation | Assessing whether a metric or result is normal, good, or bad | Never |
| Medical recommendations | Generating clinical guidance, diagnoses, or treatment suggestions | Never |
| Automated health decisions | Any automated decision with clinical, legal, or medical consequence | Never |
The MCP server exposes data retrieval tools only. Retrieval is vector similarity search over your own vault — it returns stored records, not AI-generated answers. All interpretation, analysis, and decision-making is performed by you or by external AI agents you have independently chosen to connect. Methos has no visibility into or control over what those agents do with the data they retrieve.
Human-in-the-loop at ingestion
The AI output at steps 2–3 is a categorisation and normalisation proposal only. It carries no health interpretation and has no effect on the retrievable representation until you review and approve it at step 4.
DNA / genetic data takes a different path. It does not go through this AI pipeline at all — it is neither normalised nor embedded into the vector store, but stored separately and exposed through dedicated MCP tool(s). DNA ingestion is currently disabled; when enabled, the initial scope is limited to short consumer reports (e.g. 23andMe, Ancestry), with full sequencing reports (VCF) deferred to a later phase.
Ancillary AI use (no health data). Two further uses exist that never touch stored user data: a lightweight model normalises a free-text interface-language preference to an ISO code (the only input is the short string you typed), and an in-product help assistant serves short, page-specific help text from a fixed server-side knowledge base, translated into your language. The help assistant is not an autonomous agent — it has no conversation memory, performs no reasoning over personal data, executes no actions on your behalf, and has no access to the database or any user data.
Models used
| Function | Model | Access |
|---|---|---|
| Categorisation | Claude Haiku | Azure AI Foundry |
| Normalisation and anonymisation | Claude Sonnet | Azure AI Foundry |
| Embedding / vectorisation | text-embedding-3-large | Azure AI Foundry |
| Language normalisation (no health data) | Claude Haiku | Azure AI Foundry |
| Help translation (no health data) | Claude Haiku | Azure AI Foundry |
Model versions are pinned at deployment and reviewed periodically. No model receives personal data beyond the specific content required for the operation in progress.
Where AI runs: All AI inference is performed through Microsoft Azure AI Foundry, under Microsoft's Data Processing Agreement (incorporating EU Standard Contractual Clauses). Models are deployed only in EU Azure regions (Sweden), so inference stays within the European Union. Categorisation and normalisation models receive the document being ingested for the duration of that operation; only normalised, de-identified content is sent to the embedding model.
No training on your data: Your content is never used to train, fine-tune, or improve any AI model — neither by us nor by the model provider under the Azure AI Foundry Data Processing Agreement.
Transparency & human oversight: You are always informed when a feature is powered by AI and when your data is being AI-processed. AI output is assistive only — you must review and explicitly approve the normalised result before anything is embedded, so a human always stays in control.
Methos is a data storage and retrieval infrastructure. It does not have a medical purpose and does not fall within the scope of medical software regulations — it performs no diagnostic, monitoring, or clinical function. Health data stored in Methos is treated identically to any other user document; the system has no domain-specific knowledge of its medical meaning.
| Framework | Status |
|---|---|
| GDPR (EU 2016/679) | Fully applicable — primary data protection regulation |
| EU AI Act | Applicable — system assessed as minimal-risk category |
| EHDS — European Health Data Space (EU 2025/327) | Not currently applicable — Methos is not an EHR system, claims no EHR interoperability (so the voluntary wellness-app label/registration regime does not apply), and is excluded as a microenterprise from secondary-use data-holder obligations. Re-assessed if any of those change |
| Medical software / MDR (EU, US) | Not applicable — no medical purpose, no diagnostic, monitoring, or clinical function |
| CCPA (California) | Applicable to California users; compliance not yet assessed — legal review planned |
| HIPAA (USA) | Not currently applicable — no clinical or covered entity relationships |
EU AI Act assessment: The AI functionality in Methos (document categorisation, normalisation, and embedding) falls in the minimal-risk category. It performs no high-risk function as defined in Annex III of the Act. There is no AI-based decision-making with impact on individuals' health, safety, or fundamental rights.
Our role: Methos is a deployer of AI, not a provider of the underlying models. We use general-purpose models exclusively through Microsoft Azure AI Foundry, so model-provider obligations under the Act rest with the upstream provider, governed by Microsoft's Data Processing Agreement.
AI audit logging: Every AI operation is recorded in the same GDPR audit-log pipeline, capturing the operation type, model identifier, prompt version, input type (text or file) and size, output size, duration, retry count, success/failure status, timestamp, and a pseudonymised subject reference. Audit logs never contain raw user data or prompt content. Logs are retained for auditability and are not used for model training or profiling.
Legal documents: Stable versions of the Terms of Service, Privacy Policy, and Data Processing Consent (versioned by their "Last updated" date) have been finalised following legal review and are published alongside this report; they are linked from the product interface. The Methos codebase is proprietary and not open source.
Honest open-beta status. This list shrinks with each release.
Questions, concerns, or responsible disclosures: [email protected]
Operated by Methos BiOS s.r.o. (IČO 24921424), Černého 834/32, Bystrc, 635 00 Brno, Czech Republic (European Union). Registered with the Regional Court in Brno, file ref. C 151197.