The Complete Guide to Using AI in the Government Industry in Finland in 2025

Finland's public sector stands at a pivotal moment: with the EU AI Act coming into force and national implementation in motion, agencies face a fast-moving mix of opportunity and legal guardrails - think a patchwork of ten existing market‑surveillance authorities anchored by Traficom as a single point of contact and a new national sandbox plan to test systems under supervision.

Key obligations for general‑purpose and high‑risk models start applying on 2 August 2025 (EU Artificial Intelligence Act application on 2 Aug 2025), and recent Ministry guidance (Feb 2025) stresses transparency and human oversight for generative AI in public administration.

This guide shows what Finnish teams must watch, and how practical training - like the 15‑week AI Essentials for Work bootcamp - can help civil servants gain prompt-writing and deployment skills fast (AI Essentials for Work bootcamp syllabus | Nucamp).

The legal baseline for AI in Finland in 2025 is a careful, phased mix of EU rules and national plumbing: the EU Artificial Intelligence Act's provisions for general‑purpose AI take effect across the bloc on 2 August 2025, meaning obligations for large generative models kick in even as Helsinki finishes its implementing rules (EU Artificial Intelligence Act application on 2 Aug 2025).

National work is well advanced but conditional - the government sent its first‑phase proposal to Parliament on 8 May 2025 to set up supervisory powers and penalties, and Finland's draft prefers a decentralised model that names some ten existing market‑surveillance authorities with Traficom as the single point of contact rather than a single new regulator (Government proposal to implement the first phase of the AI Act).

Expect a two‑tier rhythm: EU obligations for GPAI are already on the calendar while nationally the sandbox and sanctioning architecture will land later (sandboxes under the AI Act must be ready by 2 August 2026), so teams should prioritise documentation, transparency and human‑in‑the‑loop controls now rather than waiting - think of it as stitching legal compliance onto live innovation, not a pause button (AI regulatory sandboxes guidance).

Clear guardrails already tell public servants when AI is useful - and when it must step back: in Finland AI is appropriate as a support tool for processing, triage and drafting (for example to speed up document verification or to surface options), but it must not replace a human when legal or discretionary judgment is required; national guidance and practice make plain that current automated decision‑making rules “do not apply to learning systems” and, correspondingly, machine‑learning systems are not authorised to make final decisions in discretionary matters - think of an AI that can draft a ruling but cannot “press the stamp.” At the same time the EU AI Act tightens the leash on public‑facing systems: many public services (education, health, benefits, employment, transport) fall into the Act's high‑risk bucket and therefore need documented risk assessments, dataset quality, traceability and human monitoring before deployment.

For cases where the law would otherwise block useful experiments, Finland is building regulated testing spaces: national AI regulatory sandboxes let agencies and vendors trial systems under supervision and gather evidence for compliance and legislative fixes.

Practical takeaways for agencies: log provenance and decisions, require human‑in‑the‑loop sign‑off, train staff in AI literacy, and use sandboxes for safe innovation rather than bending rules.

"Providers and deployers of AI systems shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf, taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in, and considering the persons or groups of persons on whom the AI systems are to be used."

Practical compliance for public AI deployments in Finland means translating legal checkboxes into everyday operational habits: run and document proportionate risk assessments for anything that could affect education, health, benefits or transport; enforce high data‑quality standards and provenance tracking for training sets; log model versions, inputs and decision traces so results are auditable; and build clear human‑in‑the‑loop gates and verification steps so AI never makes the final discretionary call.

These obligations flow directly from the AI Act's high‑risk rules and Finnish guidance - see the developer guide on required risk mitigation and dataset quality (Finnish Developer Guide: Responsible AI Data Ethics and Legal Recommendations) and the Ministry of Finance's generative‑AI guidance stressing transparency and verification (Ministry of Finance Guidelines: Using Generative AI in Public Administration).

Practical musts: bake explainability and monitoring into procurement contracts, maintain retention‑grade documentation for audits, train staff (AI literacy rules took effect early in 2025), and use regulated sandboxes when experiments need temporary regulatory relief - think of compliance as stitching a safety net under live innovation so an auditor can follow the decision trail like entries in a ledger.

"Providers and deployers of AI systems shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf, taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in, and considering the persons or groups of persons on whom the AI systems are to be used."

Procurement and contracting for AI projects in Finland should treat suppliers like partners in compliance: write clear, use‑case driven scopes, require rights to outputs and provenance, and bake in GDPR and AI Act compliance, security controls and regular bias audits so contracts don't become paper promises.

Insist on IP and data‑ownership clauses, robust confidentiality and breach protocols, explainability or traceability obligations, measurable performance guarantees and remedies, and express audit and incident‑response rights for customers - contractual points highlighted in recent practical contractual considerations for licensing third‑party AI applications and solutions.

Use public procurement data and benchmarks to shape requirements and value tests: Finland's Hansel platform shows how open procurement data turns invoices into a near‑real‑time “transaction history” for buyers, helping spot outlier pricing, supplier concentration and sustainability metrics (see Finland's Hansel open procurement data creates a digital trail of state spending).

Finally, require suppliers to support sandbox testing and provide evidence for compliance (datasets, model versions, risk assessments) so agencies can pilot safely without outsourcing accountability - a practical contract is the single best hedge against future liability and public scrutiny.

"The most difficult part was not the technical development, but the negotiations with the units. The problem was, who owns the data? Everybody said, this is my data and you're not allowed to make decisions over my data. So the Ministry of Finance wrote a letter to support the process."

Generative AI can be a practical productivity tool for Finnish public services, but the playbook is clear: use it to support tasks - drafting, summarising stakeholder feedback, triage and customer service - while locking human oversight, transparency and data protection into every workflow.

The Ministry of Finance's guidance encourages authorities to exploit generative AI to boost efficiency but insists that any AI‑generated content be verified by the responsible public official before it becomes final, and warns against embedding models into processes that require legal or discretionary judgement (Finnish Ministry of Finance guidelines on using generative AI in public administration).

Risks flagged across Finnish guidance - hallucinations, copyright and deepfake exposure, dataset poisoning and privacy leaks - mean agencies must choose processing bases carefully, run DPIAs and favour closed or purpose‑built training data where feasible; the Data Protection Ombudsman's practical guidance explains when a lawful basis and impact assessment are required (Data Protection Ombudsman guidance on AI and personal data in Finland).

In short: treat generative models as clever scribes, not decision‑makers - a chatbot can suggest wording, but an official must always put the stamp on the answer.

Data protection in Finland builds on the GDPR and the national Data Protection Act (1050/2018), creating a practical framework that public agencies must turn into operational habits: clear privacy notices in plain language, records of processing, proportionate DPIAs for high‑risk systems, and mandatory breach reporting to the supervisory authority within 72 hours where feasible.

The Office of the Data Protection Ombudsman is the national regulator and can audit, demand information and impose sanctions, so agencies should treat the Ombudsman as the primary point of accountability (Finnish Data Protection Act and national legislation - Data Protection Ombudsman).

Controllers that are public authorities must appoint a DPO and large or sensitive processing triggers formal oversight and data‑protection‑by‑design measures; special Finnish rules also keep strong protections in employment contexts and set the age of consent for information society services at 13.

Cross‑border transfers remain subject to GDPR safeguards (SCCs, adequacy, BCRs) and enforcement carries real bite - administrative fines under GDPR can reach up to 4% of global turnover - so transparency, documented lawful bases, minimal data use and staff training are the non‑negotiable building blocks for any government AI roll‑out in Finland (Overview of Finland data protection laws - DLA Piper).

Remember: strong transparency is not paperwork for its own sake but the route that lets citizens verify decisions and appeal them.

Governance for AI in Finnish agencies is steadily moving from high‑level principles to operational levers: national bodies and programmes - illustrated by Finland's long‑running AI strategy and an AI ethics committee - anchor a human‑centric approach, while initiatives like AuroraAI and the FCAI ecosystem push for practical standards, testbeds and workforce uplift to make those principles real (Finland national AI strategy report - European Commission AI Watch, NordForsk overview of Nordic national AI strategies).

Translating values into oversight means a layered governance mix - policy, procurement, audits and local control - that echoes the Hourglass Model and AIGA playbook: external laws and ethics filter into organisational policy and then into system‑level controls such as impact assessments, documentation, red‑teaming and continuous monitoring (Sanoma Group AI governance framework and audit case study, see also frameworks described by Promevo).

Practical oversight instruments include regulated sandboxes, dedicated audits, cross‑sector hubs and training pipelines so auditors can trace decisions like ledger entries; a memorable shorthand is to treat governance as an hourglass that channels broad social values into concrete technical gates and then back into public accountability.

"The review aimed to ensure compliance with the forthcoming EU AI Act, among other objectives. It provided valuable insights and identified areas for improvement in the ongoing development of AI governance. This review reinforced our commitment to responsible AI development."

Operational risk management in Finland centers on practical, sector‑aware controls: in health and social care, treat procurement as the first line of defence - apply the NCSC‑FI's information security and data‑protection requirements early in tender planning, require provenance and secure update mechanisms, and build penetration testing and continuity plans into vendor contracts (NCSC‑FI information security and data protection requirements for social welfare and healthcare procurements); the healthcare sector's cyber‑risk literature stresses the same prevention mindset - risk checklists, asset classification and staff training - that can stop a single exploit from cascading into a large patient‑data incident like the psychotherapy record breaches that have shaped national urgency.

At the national level, align operational plans with Finland's cybersecurity strategy and international cooperation commitments (including NIS2 awareness and NATO partnership intelligence‑sharing) so detection, attribution and crisis management scale across ministries; for IT and cloud services, mandate GDPR‑aligned data flows, CSPM and regular risk assessments, and insist suppliers document mitigations and incident playbooks.

For agency teams running pilots or AI in public services, the practical recipe is the same: bake threat modelling, logging and recoverable backups into every rollout, use sandboxed tests where regulatory leeway is needed, and treat audits and tabletop exercises as routine maintenance rather than one‑off events (Cyber risk management in the Finnish healthcare sector (master's thesis)).

Finish the compliance sprint with a clear, practical roadmap: start by inventorying systems and data sources, prioritising low‑risk pilots that exclude sensitive information and can be run in regulated sandboxes; run proportionate DPIAs and dataset‑provenance checks before any training or fine‑tuning; log model versions, inputs and outputs for auditability; bake human‑in‑the‑loop gates into every customer‑facing flow; and lock contractual promises on explainability, update practices and incident response into procurement.

Learn from Finnish pilots - Sitra's lessons for law‑drafting show the real costs of collecting clean Finnish training data and reforming workflows (Sitra generative AI law‑drafting pilot lessons) and Helsinki's phased Copilot experiment demonstrates a safe rollout model (exclude sensitive units, train whole teams, monitor wellbeing and usage) that agencies can copy (Helsinki Copilot pilot report on responsible generative AI).

Parallel to pilots, invest in staff AI literacy now - short, structured training such as the 15‑week AI Essentials for Work course can equip officials to write prompts, verify outputs and maintain human oversight (AI Essentials for Work bootcamp syllabus (Nucamp)).

Pace the rollout: pilots → sandbox evidence → scaled procurement with hard compliance clauses; keep documentation as the single source of truth so auditors, citizens and courts can follow the decision trail, and use public–private funding or sandbox arrangements to lower adoption risk while meeting the EU and national rules that take effect during this transition period.

“in Finland we have digitalised (…) services very, very long time ago, many of them have been at least 30 years and they have produced a lot of data. That city, I would say have at least 600 different data systems, apps, and similar.”