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    Vibe coding security

    Security and compliance for AI-built apps

    You shipped it in a weekend. Now a customer wants to know if it’s “secure” and “GDPR compliant”, and the honest answer is a shrug. Connect Ansvar to the agent that built your app and turn the shrug into a cited report.

    You

    Threat-model my app. Next.js + Postgres on Vercel, Stripe billing, EU users.

    Agent
    STRIDE threat model — server-enforced workflow
    Stripe webhook endpoint accepts unsigned eventsHigh

    Verify webhook signatures; reject unsigned payloads.

    GDPR Art. 32 — Security of processing · eur-lex.europa.eu
    The gap

    Shipping fast is the easy half

    A weekend of prompting gets you a working app. It does not get you a reviewed one.

    How it works

    Three steps, no new tools to learn

    The same agent that wrote your code runs the review.

    1. ConnectAdd one MCP server to Claude, Cursor, ChatGPT, Copilot, or Gemini — whichever agent you already argue with. OAuth sign-in, a few minutes. Setup guide.
    2. DescribeTell your agent what the app is, in plain words: “Next.js, Postgres, Stripe, EU users.” No forms, no uploads — your code stays with you. The review works from a description.
    3. Get the reportA server-enforced workflow walks your agent through a STRIDE threat model — and, when you handle personal data, a separate privacy threat model — then produces a report (PDF, if you like) you can send to the customer instead of a nervous emoji.
    The report

    From a description to fourteen findings

    Same fictional app as the prompt up top: first the map, then the findings from walking it — the part your customer actually reads.

    You

    Next.js + Postgres on Vercel, Stripe billing, EU users.

    The system map: your users (EU, browsers) and Stripe (external billing) sit on the internet side of trust boundary B1. Users reach the Next.js API on Vercel over HTTPS and Stripe calls it with webhooks — both crossing B1. The API queries Postgres, which holds personal data, across trust boundary B2.

    The review starts where arrows cross the dashed lines.

    report.pdf — threat registerranked by severity
    • Elevation of privilegeCritical

      Any signed-in user can read any other account's data

      Scope every query to the session's user id — signed-in isn't authorized.

      GDPR Art. 5(1)(f) — Integrity & confidentiality
    • RepudiationHigh

      No auth or admin logs — a breach would be invisible

      Log sign-ins, resets, and admin actions — you can't report a breach you can't see.

      GDPR Art. 32 — Security of processing
    • SpoofingMedium

      Password-reset tokens never expire and survive a password change

      Single-use reset tokens with a short clock.

      OWASP ASVS v4.0.3 — Credential recovery
    • Information disclosureMedium

      The reset form tells strangers which emails have an account

      Return the same response whether the address exists or not.

      marked unresolved — no served legal basis found
    Excerpt — 4 of 14 findingsBrowse a full sample
    • Tomorrow's fix list

      Ranked by severity. Start at the top, close them like tickets.

    • The questionnaire answer

      “Do you have a threat model?” Yes — attached.

    • Proof, not promises

      Findings that touch the law carry the citation. Gaps get flagged, not filled in.

    • False positives die at home

      Your agent has the code — or grant it GitHub access — and can check each finding against what's actually there. Ansvar never asks for it.

    Receipts

    It cites the actual law — or it tells you it can't

    Compliance folklore is free everywhere. Sources are the product.

    FAQ

    Questions builders ask before connecting

    If yours is not here, ask us — every message gets a human answer.

    Is my AI-built app GDPR compliant?

    No tool can stamp you compliant, and anyone who says otherwise is selling stickers. What you can do is find out which rules actually touch your app, then produce the assessments they expect — security measures, and a data protection impact assessment when what you do with people's data is likely to be high-risk — with citations to the real text.

    What does a threat model give me?

    A structured list of what can go wrong in your app, component by component, ranked, with fixes — and citations to the legal requirements involved, where there are any. It's usually the first thing a security-minded customer asks for. Now you'll have one.

    Do I need a DPIA?

    Only when your processing is likely to be high-risk for the people in your data — think profiling that affects real outcomes, lots of sensitive data, or monitoring public spaces at scale. If that sounds uncomfortably familiar, the DPIA workflow (Team plan) walks the assessment and produces the report. Orientation, not legal advice.

    Which tools does this work with?

    Claude, Cursor, ChatGPT, Microsoft Copilot, Gemini — anything that speaks MCP.

    Is there a free plan?

    Yes, with a business sign-up: regulatory search, law lookups, citation checks, and the CVE lane. Threat modeling starts on the Premium plan — prices live on the pricing page.

    Can it scan my code?

    No — on purpose. It threat-models the system you describe and screens the dependencies you name; it never asks for your code. Your agent is a different story: it already has the repo (or you can grant it GitHub access), so it can check each finding against the actual code and call out the false positives — that check stays on your side. Keep your code scanner — this adds the design review and the law on top.

    Let your agent clean up after itself

    Connect the gateway and get the review done before the next customer asks.

    Free covers the vulnerability screen and law lookups (business sign-up). Threat modeling starts on Premium — metered monthly, STRIDE and privacy runs counted separately; see Pricing. Research support for professional review, not legal advice.