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AI & Data Audit

A systematic review of your AI systems, data pipelines, and governance — flaws, risks, and fixes, in plain language.

An AI & Data Audit is an independent, systematic review of your AI systems, data pipelines, and governance practices. It pinpoints flaws and risks — from data quality and model behavior to access control and FADP/GDPR compliance — and delivers a prioritized remediation plan.

Who this is for

Organizations running AI in production, inheriting systems from vendors, or facing compliance questions they can't yet answer with confidence.

What you get

  • An honest picture of what your AI and data estate actually looks like
  • A risk assessment covering technical, operational, and regulatory exposure
  • A prioritized remediation plan — quick wins first, structural fixes sequenced
  • Evidence you can show auditors, boards, and security reviewers

What's included

System and pipeline review

We examine your AI applications, data pipelines, and integrations: architecture, reliability, monitoring, and failure modes.

Data quality assessment

Source coverage, freshness, duplication, and access patterns — the data problems that quietly break AI systems.

Governance and compliance check

Permissions, audit trails, human review points, and data protection measured against FADP, GDPR, and your internal policies.

Remediation plan

Findings ranked by severity and effort, with concrete fixes — not a hundred-page report that no one acts on.

How it works

  1. 01

    Define the audit scope

    We agree which systems, pipelines, and policies are in scope, and gather documentation and access.

  2. 02

    Review systems and data

    Hands-on inspection of architecture, code where relevant, data quality, and operational practices.

  3. 03

    Assess risks

    Findings are scored on likelihood and impact across technical, operational, and regulatory dimensions.

  4. 04

    Deliver the remediation plan

    A readout for both technical teams and leadership, with a prioritized fix list and effort estimates.

Deliverables

  • Audit report with severity-ranked findings
  • Data quality assessment
  • Compliance gap analysis (FADP/GDPR)
  • Prioritized remediation plan with effort estimates

Frequently asked questions

When is an AI audit worth doing?

Three common triggers: an AI system is about to go into production, a vendor-built system needs an independent second opinion, or compliance and security teams are asking questions the current documentation cannot answer.

Do you audit systems you didn't build?

Yes — most audits cover systems built by others. Independence is the point: we have no incentive to defend past decisions, and the findings reflect that.

How disruptive is the audit for our team?

Light. We need documentation, read access, and a handful of interviews. Most audits complete in two to four weeks with only a few hours of your team's time per week.

Ready to scope this with us?

Tell us about your systems, data, and goals. We reply within one Swiss business day with a concrete next step.

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