🔴Illinois HB 3773IN EFFECTUp to ~$70K/violation|🔴Texas TRAIGA (HB 149)IN EFFECTAG-enforced|🔴Utah AI Policy ActIN EFFECT$2,500/violation|⚠️Colorado AI Act (SB 205)Jan 1, 2027AG-enforced|⚠️California SB 942Aug 2, 2026$5K/day|⚠️EU AI Act Art. 50Aug 2, 2026€35M or 7% revenue|⚠️New York RAISE ActJan 1, 2027AG civil penalties|
European Union · EU AI ActNo AI-specific federal instrument — measured against the Systematic Compilation (2026-09-02)

Switzerland AI Compliance Requirements

Mandatory and recommended controls under EU AI Act + national rules, including the role of the local data-protection authority.

Deadline: No AI-specific compliance deadline, because Switzerland has enacted no AI-specific instrument. The revised DSG and its ordinance have both been in force since 1 September 2023 and apply to AI systems that process personal data from that date.Penalty: No AI-specific penalty exists in Swiss federal law. Penalty amounts under the DSG and the Kartellgesetz are deliberately NOT quoted here: the operative penal articles were not read at source in this pass, and a fine figure taken from a secondary source is not something this product publishes.

How AI law works in Switzerland

Switzerland has no artificial-intelligence act, and this is now a standing measurement rather than a desk finding: the Bundeskanzlei's own Systematic Compilation was queried on 2026-09-02, restricted to instruments whose enforcement status is 'in force', and no federal enactment carries 'künstliche Intelligenz' in its title. That agrees with the Federal Office of Justice's own statement that there is no specific Swiss AI legislation. What binds an AI deployer is general federal law. The revised Bundesgesetz über den Datenschutz of 25 September 2020 (SR 235.1) entered into force on 1 September 2023, replacing the 1992 Act, and governs the personal data an AI system processes; the Datenschutzverordnung of 31 August 2022 (SR 235.11) entered into force the same day and carries the operational detail — Switzerland's regulation layer for this subject is real, and it is an ordinance rather than an AI statute. Competition exposure runs through the Kartellgesetz of 6 October 1995 (SR 251) and, for deceptive automated marketing, the Bundesgesetz gegen den unlauteren Wettbewerb of 19 December 1986. ⚠️ A caution that matters here more than anywhere else in this corpus: more than half of the Swiss Systematic Compilation consists of REPEALED consolidations, and the 1992 data-protection Act is still served by the same database under a 'no longer in force' status. Every instrument listed above was filtered on that status at query time, so none of them is a superseded text.

Applicable laws

  • 📜 Bundesgesetz vom 25. September 2020 über den Datenschutz (DSG, SR 235.1)
  • 📜 Verordnung vom 31. August 2022 über den Datenschutz (DSV, SR 235.11)
  • 📜 Bundesgesetz vom 6. Oktober 1995 über Kartelle und andere Wettbewerbsbeschränkungen (KG, SR 251)

EU AI Act requirements begin with system risk assessment. Your organization must evaluate every AI system against the EU AI Act's risk framework: prohibited systems (facial recognition in law enforcement, social credit scoring, subliminal manipulation), high-risk systems (hiring, benefits determination, law enforcement, biometric ID), limited-risk systems (chatbots), and minimal-risk systems (game AI, spam filters). The legal requirement is to classify your system correctly. Misclassification — for example, claiming that a hiring AI is minimal-risk when it is high-risk — is itself a compliance violation. High-risk classification triggers the heaviest compliance burden: conformity assessment, bias and fairness testing, documented risk mitigation, human oversight, transparency, and record-keeping. If you are uncertain whether a system is high-risk, the safe assumption is to treat it as high-risk and apply the full compliance framework.

Pre-deployment conformity assessment is the core requirement for high-risk systems. Before deploying a high-risk AI system (or immediately, if it is already deployed), you must complete a documented assessment covering: data quality — are the training and decision-making data representative of the population affected by the system, and do they contain known biases?; model performance — does the model perform equally well across demographic groups, or is accuracy lower for protected groups?; system explainability — can you explain to an affected individual why the system made a particular decision?; human oversight design — what process allows an individual to escalate the AI decision to human review?; and risk mitigation — what controls have you implemented to reduce the risk of discriminatory outcomes? This assessment must be documented in writing, reviewed by qualified personnel, and updated at least annually.

Bias and fairness testing is a specific requirement for high-risk systems. The EU AI Act does not prescribe a particular testing methodology, but requires that your organization conduct documented testing and be able to demonstrate that you have evaluated the system for discriminatory impact across protected characteristics (race, color, religion, national origin, sex, gender identity, sexual orientation, disability, age, etc.). Testing must include: hold-out test data not used in training, representative of the affected population; evaluation of decision-rate parity across groups (does the AI approve loans at the same rate for all genders, races, and age groups?); and performance parity testing (does the AI make accurate predictions equally well across all groups?). Document test results, identify any disparate impact, and implement mitigation (rebalance training data, adjust decision thresholds, redesign features, or limit the system's scope).

Transparency and human-rights mechanisms are mandatory for all systems, with intensity scaling to risk level. For limited-risk systems (chatbots), you must disclose that the individual is interacting with AI. For high-risk systems, transparency is much deeper: you must inform affected individuals before the AI system makes a decision about them, explain what data the system is using, describe how the system works (at a non-technical level accessible to the individual), and provide the individual with a clear, accessible process to request human review and appeal the AI decision. In employment and benefits contexts, individuals must be able to request re-evaluation by a human reviewer, and that human review must be genuine — a human who has authority to override the AI decision and the information needed to make an independent judgment.

Ongoing monitoring, record-keeping, and individual-rights response are permanent obligations. You must monitor every high-risk AI system's performance and decisions on an ongoing basis (not just at deployment). Maintain audit logs of every high-risk decision for at least three years, capturing inputs, decision outputs, confidence scores, human-review flags, and any human override. When an individual requests an explanation of an AI decision, you must respond within 30 days with accessible, non-technical information about how the system works and why it made that particular decision. When an individual requests appeal or human review, you must provide it. Failure to respond to individual rights requests is a documented compliance violation and a source of private civil liability.

Switzerland compliance requirements, ranked

Mandatory under EU AI Act + GDPR

Risk classification per national framework + GDPR-equivalent DPIA
Public AI-use disclosure to end-users in their language
Human review path for adverse automated decisions (GDPR Art. 22)
Cooperation with Switzerland's supervisory authority + serious-incident reporting
Records sufficient to reconstruct each automated decision (3+ years)

Strongly recommended

Annual third-party bias / fairness audit
AI vendor due-diligence questionnaire (training data provenance, sub-processors, retention)
Cross-functional AI governance committee
Public-facing complaint mechanism

Country context

Applicable framework: Bundesgesetz vom 25. September 2020 über den Datenschutz (DSG, SR 235.1)
Applicable framework: Verordnung vom 31. August 2022 über den Datenschutz (DSV, SR 235.11)
Applicable framework: Bundesgesetz vom 6. Oktober 1995 über Kartelle und andere Wettbewerbsbeschränkungen (KG, SR 251)
Regulator: see official sources block below for Switzerland's primary AI / data-protection authority.
Status: No AI-specific federal instrument — measured against the Systematic Compilation (2026-09-02). Headline penalty exposure: No AI-specific penalty exists in Swiss federal law. Penalty amounts under the DSG and the Kartellgesetz are deliberately NOT quoted here: the operative penal articles were not read at source in this pass, and a fine figure taken from a secondary source is not something this product publishes..

More Switzerland resources

AI Compliance Checklist💰 AI Law Fines & Penalties📖 AI Compliance Guide AI Law Deadlines← All Switzerland resources

Other countries

Germany (EU)France (EU)Netherlands (EU)Spain (EU)Italy (EU)Sweden (EU)
Editorial standards

Anchored to the primary government source (statute, bill text, or agency rule) and verified directly against it · Last verified Sep 2, 2026. See our methodology.

Primary sources · Switzerland