The Complete Guide to Using AI as a Legal Professional in Finland in 2025

For legal professionals in Finland, 2025 is the year AI moves from theoretical risk to everyday compliance - the EU AI Act is in force and national rules (including a proposed Act on the Supervision of Certain AI Systems) are landing soon, so understanding what

high‑risk

means and who supervises it is essential (Chambers Guide to Artificial Intelligence 2025 - Finland).

Recent government proposals and guidance sharpen the focus on transparency, bias mitigation and public‑sector limits on generative tools - for example, public chatbots must disclose they are automated - and Finland's sandbox and supervisory plans are explained in practical detail (Hannes Snellman analysis of EU AI Act regulatory developments in Finland).

This guide distils those rules into usable steps - and for lawyers who want hands‑on skill building, the Register for the Nucamp AI Essentials for Work bootcamp covers prompts, governance and workplace use - making complex regulation feel as manageable as a 30‑day pilot with clear KPIs.

In 2025 the EU's new, risk‑based AI regime is not a distant idea but a working framework that matters for every Finnish practice: the AI Act's provisions for general‑purpose AI (GPAI) and core governance rules come into application on 2 August 2025, while Member States must name national competent authorities by the same date - yet Finland has chosen a decentralised model, with a draft implementing act proposing ten existing market surveillance authorities and the Finnish Transport and Communications Agency (Traficom) as the single point of contact (see the Ministry press release and Hannes Snellman's analysis).

That means lawyers must manage EU obligations (like GPAI transparency and high‑risk controls) alongside an evolving national overlay - the Government submitted a proposal on 8 May 2025 to set out supervision and sanctions, and a separate draft on AI sandboxes was published on 25 April 2025.

Practically: treat EU deadlines as firm, but plan for Finnish supervision to look like

ten small lighthouses

coordinated by Traficom rather than one central beacon - a useful mental image when deciding who you'll need to notify, advise or cooperate with in a compliance review.

Finland's national AI strategy has been channelled through the AuroraAI programme - a Ministry of Finance‑led effort to make public services more human‑centred, data‑secure and life‑event aware by building an interoperable AuroraAI network that helps citizens and service providers find each other; the project ran from 2020 (launch) with network availability planned for 2022 and was followed by evaluations and winding‑down activity into 2023 (see the official AuroraAI programme page - timelines and reports).

AuroraAI was deliberately experimental - envisioning, for example, a “digital twin” to guide a person through job changes or other life events - and its lessons now feed Finland's broader governance approach to trustworthy AI, including emphasis on ethics, experimentation and cross‑sector interoperability (OECD overview of AI governance).

For legal professionals this matters because the public‑sector playbook prioritises human‑centred design, clear data governance and evaluative oversight: the technical ambition (an open network of service connectors) meets practical constraints (ethical scrutiny, audits and, ultimately, determined sunsetting of specific services), so compliance work must bridge procurement, privacy and service‑design review in equal measure.

“the core idea of the AuroraAI program involves proactively offering services to people according to their own life‑events.”

Risk‑based compliance in Finland means translating the EU's tiered approach into pragmatic steps for firms and public bodies: start by inventorying every AI use, classify each system against Annex III and Article 6's rules, and document any decision that says:

not high‑risk

so you can show the rationale to national authorities later (providers who think a system is not high‑risk must record that assessment) - the EU Commission's clear timeline and obligations are a practical north star (EU Commission AI Act overview and timeline).

Remember the distribution of duties - most heavy lifting sits with providers (technical documentation, risk‑management, data governance), while deployers (professional users, including Finnish public organisations) must ensure human oversight and follow instructions for use - a distinction crucial when contracting vendors or planning pilots (see the high‑level summary for checklists and GPAI rules).

Short‑term priorities for 2025–26 are simple but non‑negotiable: classify systems, embed lifecycle risk management, prepare technical documentation and logs for traceability, and decide whether to pursue internal or notified‑body conformity assessment for high‑risk systems; private AI assurance can accelerate readiness but does not replace formal conformity assessments.

Think of the process as installing both a seatbelt and a dashboard camera - the seatbelt (risk management) keeps people safe; the camera (documentation, logging, audits) proves compliance if regulators ask.

For Finnish lawyers advising on AI, data protection is the backbone of any compliant deployment: the Office of the Data Protection Ombudsman's practical guidance makes clear that every AI project that touches personal data needs a lawful basis (including when data is used to train models), an upfront risk assessment from the data‑subject perspective and, where appropriate, a DPIA and proportionate security measures (see the Ombudsman's guidance on AI systems and data protection).

GDPR and Finland's Data Protection Act set the baseline - principles like data minimisation, purpose limitation, transparency and the right to rectification/deletion must be designed into models and user flows rather than bolted on afterwards - so document choices and keep audit trails to prove why each dataset or feature was necessary.

Special caution is required for biometric processing: facial recognition and other biometric profiling are treated as sensitive and often fall into the AI Act's high‑risk remit, so explicit consent or a narrow public‑interest basis and extra safeguards may be needed (see the Finland practice guide on AI governance).

A simple heuristic helps: treat training data like a sealed client file - only open the pages you need, log who viewed them, and be ready to show regulators why each page mattered.

Generative AI can be a huge productivity booster for Finnish legal teams, but the dominant risks - hallucinations, confidentiality/IP leaks and biased outputs - require concrete guards: treat every AI draft as a hypothesis, not a finished brief, and build mandatory human‑in‑the‑loop checks, citation verification and auditable logs into workflows (the Finland practice guide stresses human oversight and transparency for public‑sector AI and warns against using generative tools for discretionary legal judgments without supervision; see the Chambers Guide to Artificial Intelligence 2025 - Finland).

Technical mitigations matter too - prefer closed legal datasets or synthetic training data, use retrieval‑augmented generation cautiously because RAG is helpful but not a hallucination cure, and insist on vendor assurances, bias audits and contractual rights to provenance and model‑explainability (the Stanford HAI study documents persistent hallucination rates and recommends public benchmarking and traceability as essential safeguards: Stanford HAI on legal hallucinations).

Operationally, require tailored prompting training, a mandatory cite‑check step before any court filing, DPIAs for data used in training, and procurement clauses that assign liability and security obligations - simple, repeatable processes will prevent reputational and regulatory harm (other jurisdictions have already seen sanctions and six‑figure risk: courts and firms have been penalised for unverified AI citations).

Think of mitigation as a three‑layer armour: trusted data, human verification and contractual/technical audit trails, so a single bad output doesn't become a practice‑ending mistake.

AI isn't the problem, poor process is.

What's the best AI for legal work in Finland? The short answer is: it depends on the task, integration needs and data‑protection posture - start by mapping your use cases (due diligence, drafting, search, litigation support) and then match tools that prove strength in those areas rather than chasing hype.

Market surveys show a fragmented field - Harvey and CoCounsel score highly on drafting and contract review while Spellbook and specialist CLMs excel at transactional clause work, and comprehensive platforms like Relaw.ai promise cross‑practice coverage with native Word integration and enterprise security (2025 law firm legal AI tools adoption survey; Relaw.ai 2025 legal AI platform guide).

For Finland, insist on EU‑ready deployments (many vendors now offer separate EU instances), SOC2/ISO assurances, clear guarantees about whether client data is used to train models, and contractual rights to provenance, audit logs and explainability - then pilot for 30–60 days with KPIs on hours saved and citation error rates.

Treat vendor selection like choosing a secure translator for Finnish legalese: it must preserve confidentiality, cite sources reliably and slot straight into Word or your DMS so adoption is not a battle but an upgrade.

"Client confidentiality isn't optional, it's foundational. Any AI solution you bring into your firm must uphold your data protection standards without compromise."

Professional ethics, procurement and contracting for AI in Finland must stitch together the EU‑level risk rules already described with the day‑to‑day duties lawyers have long faced: competence, confidentiality, supervision and candour.

Start procurement with a legal‑tech due diligence checklist - ask for EU‑instance deployment, SOC2/ISO security evidence, clear vendor promises about whether client data will be used to train models, and contractual audit and provenance rights so citations can be traced back to sources; these are the practical levers that convert ethical obligations into enforceable contract terms.

Build mandatory oversight into vendor contracts and retain the final professional responsibility for outputs by requiring human‑in‑the‑loop sign‑offs, red‑team bias testing, DPIA evidence where personal data is involved, and indemnities for hallucinations that cause client harm.

Supervision clauses should extend to non‑lawyer staff and third‑party suppliers and require provider cooperation with Finnish competent authorities when incidents matter for compliance; fee and billing terms should reflect efficiency gains (and avoid charging for time not actually spent), while procurement playbooks must preserve attorney judgment so tools are assistants, not substitutes.

For practical framing on professional duties and verification steps, see the LexisNexis primer on AI and legal ethics and Thomson Reuters' guidance on ethical uses of generative AI in law practice.

AI should act as a legal assistant, not as a substitute for a lawyer.

Liability in Finland is shifting from theoretical to immediate commercial risk: under the existing Finnish Product Liability Act liability is strict (no‑fault), so a causal link between a defective product and damage can be enough to trigger claims, and insurers and compensation levels have been rising as a result (see the summary of Finland's product liability rules).

The EU's Revised Product Liability Directive (RPLD) now explicitly pulls software, AI systems and certain digital services into the product liability net and introduces a cascading liability ladder so injured parties can reach an EU‑based actor (importer, authorised representative or, ultimately, fulfilment service providers) when a non‑EU manufacturer is involved - a legal “relay baton” that can land liability far from the original developer (read the RPLD analysis).

Practical consequences for Finnish firms and advisers are concrete: expect new national amendments (Finland began preparatory work in 2025), broadened evidence‑disclosure duties for cross‑border claims, and a need to recheck insurance limits and recall coverage because the RPLD expands scope of compensable damage (including loss of non‑professional data) and tightens access to evidence.

For legal teams that manage procurement or deploy generative or embedded AI, the clear takeaway is to align contracts, warranties and indemnities with the new hierarchy of liable parties, update insurance programmes to reflect broader exposure, and preserve traceable audit trails so causation and defectiveness can be demonstrated quickly if a claim arises - otherwise a single faulty model update can cascade liability through an EU supply chain.

Practical next steps for Finnish legal teams boil down to a short, repeatable checklist: first, inventory every AI use and assess data‑protection risks before any personal data is processed - follow the Office of the Data Protection Ombudsman's guidance on lawful processing, DPIAs and when processing is “high‑risk” (Office of the Data Protection Ombudsman guidance on data protection and AI systems); second, pick and document a lawful basis for development or training data, apply data minimisation and purpose limitation, and design systems so individuals can exercise their rights; third, run a DPIA where required and keep clear, auditable logs of decisions and datasets so compliance is demonstrable; fourth, pilot new tools for 30–60 days with KPIs (hours saved, citation error rates, incident triggers) and lock in contractual assurances on data use, provenance and security before wider rollout; and finally, upskill teams on prompts, governance and vendor checks - practical classroom or cohort learning accelerates this, for example via a focused programme like the Nucamp AI Essentials for Work bootcamp (Nucamp AI Essentials for Work bootcamp registration) - think of the DPIA as a site plan: it prevents costly rebuilds after regulators knock on the door.