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

For legal professionals in Argentina in 2025, AI has moved from novelty to necessity: national steps like the AAIP “Guide for Public and Private Entities” and Resolution 111/2024's National Integrated Artificial Intelligence Program in Justice show a push to balance efficiency with rights, even as Argentina still evaluates AI through existing personal data rules (AAIP Guide for Public and Private Entities and National Integrated AI Program in Justice, IBA summary of AI legal background under personal data law).

Provincial protocols - like San Juan's IAGen - require anonymization and strict limits, while court-built tools such as the National Tax Court's AI-assisted jurisprudence search (over 12,000 rulings, 2019–2024) illustrate real gains in research speed.

At the same time global studies underline the upside: generative AI can free roughly 240 hours per lawyer per year when used responsibly (Thomson Reuters study on AI productivity and legal use cases).

The message is clear: adopt strategy, document safeguards, and train teams so AI augments advocacy without eroding ethical or data-protection duties.

“This isn't a topic for your partner retreat in six months. This transformation is happening now.”

The AAIP's 2024

Guide for Public and Private Entities on Transparency and Personal Data Protection for Responsible Artificial Intelligence

crystallizes practical duties lawyers in Argentina must translate into contracts, policies and auditable workflows - see the Baker McKenzie summary of the Guide (30 Sept 2024) for the original recommendations.

Key obligations include running impact assessments from project start, embedding protection-by-design and -default, limiting collection to what is strictly necessary, ensuring lawfulness and data quality, promoting explainability and algorithmic transparency, testing models for bias, meeting information duties to data subjects, and maintaining continuous monitoring and documented accountability; the AAIP later highlighted the final Guide as part of its coordination role in early 2025 (see DataGuidance's note on AAIP's publication).

For legal teams this means drafting AI disclosure clauses and privacy policies, insisting on vendor evaluations and multidisciplinary quality checks, preserving timestamped impact-assessment records as defence-ready evidence, and building straightforward checklists so ethical and regulatory risk becomes manageable rather than theoretical.

Argentina's recent judicial playbook makes clear that AI in courts isn't an experiment but an institutional project: Resolution 111/2024, published in the Official Gazette, formally created the “National Integrated Artificial Intelligence Program in Justice” under the Chief of Cabinet of Advisors to speed up administrative and judicial procedures while explicitly requiring that deployments protect fundamental rights and expand access to justice (Argentina Resolution 111/2024 National Integrated AI Program in Justice); complementary local rules give the guidance teeth, as San Juan's General Agreement No.

102/2024 established the mandatory Acceptable Use Protocol for Generative AI (IAGen) that limits generative models to professional judicial tasks, forbids creating offensive or misleading content, bans unauthorized access or data manipulation, and requires strict data anonymization before any system use - in practice, anonymization is non-negotiable (remove names/IDs before a query).

These national and provincial moves sit alongside coordinated NAIP-style initiatives to run impact assessments, control risks, and train staff so tools like AI-assisted search help judges and clerks without eroding transparency or legal safeguards (Buenos Aires Ministry of Justice AI Program implementation priorities), creating a framework where innovation and procedural integrity must travel together.

Practical obligations for Argentine lawyers now revolve around the AAIP's recommendations and related public-sector guidance: run documented impact assessments (DPIAs) from project inception, adopt protection-by-design and -default, limit collection to what is strictly necessary, and build multidisciplinary teams to test data quality and model behaviour before deployment - these are not optional checkboxes but the backbone of defensible AI use in legal workflows, as set out in the AAIP Guide and summarized by Baker McKenzie.

Ensure algorithmic evaluation for bias and explainability, meet information duties with clear AI-facing privacy notices, and put continuous monitoring, security controls and accountability records in place so risks are detected and addressed across the AI lifecycle (design, verification, implementation, operation).

Think of a DPIA as a safety net that catches bias or privacy leakage before it reaches a client file: practical steps like test datasets, vendor audits, documented validation results, and routine logs make compliance demonstrable and operational risk manageable (AAIP Guide for Responsible AI in Argentina (AAIP PDF), Baker McKenzie summary of Argentina AI transparency and data protection guidance).

Using generative AI safely in Argentine legal practice means treating these tools as powerful research and drafting assistants - but never as autonomous decision‑makers.

Provincial rules like San Juan's General Agreement No. 102/2024 (the IAGen protocol) mandate strict pre‑use anonymization (remove names, addresses and ID numbers; even blur images and video recordings) and explicitly prohibit entering sensitive or confidential data into models, while requiring that every AI output be verified by a human before it informs any judicial act (San Juan IAGen protocol: Generative AI use in the judiciary).

Complementing this, national guidance from the AAIP frames impact assessments, protection‑by‑design, access controls, mandatory training and continuous monitoring as core obligations for any public or private deployment (AAIP Guide for Responsible AI: Argentina national AI guidance).

Practically, law firms should build an auditable prompt library, enforce role‑based access to generative tools, require documented human review of outputs, log incidents and run periodic bias and quality checks - because noncompliance can trigger disciplinary measures and, more importantly, risk breaching confidentiality or altering case outcomes.

A vivid detail to remember: sanitizing a file for AI use often means more than removing a name - images, metadata and hidden IDs must be scrubbed too - so operational checklists and mandatory training turn best practice into habit and keep innovation from undermining professional duties.

The National Tax Court's (TFS) court‑driven AI search is a concrete example of judicial modernization in Argentina: developed in record time using only the Court's own resources, the system leverages NLP and machine‑learning to interpret full descriptions or legal questions (not just keywords), tolerate typographical slips, and even recommend relevant doctrines - making it easier to surface precedents that use different terminology than the query; today it indexes more than 12,000 rulings from 2019–2024 and updates automatically.

For lawyers and clerks this means faster, context‑aware research and richer lines of argumentation, while also serving as a model for courts aiming to balance efficiency with transparency and rights protections.

Risk management for AI in Argentina now revolves around a few non‑negotiables: identifying and testing for bias, proving explainability, locking down data security, and naming accountable humans for every system in use - steps the AAIP and judicial programs call out explicitly.

The World Law Group's review of Argentina's 2024 advances flags core threats - bias and discrimination, poor data quality, privacy violations, security risks and lack of transparency - so firms must bake mitigation into procurement and operation rather than treat it as an afterthought (World Law Group report: Argentina Artificial Intelligence in Justice 2024).

Guidance and practice notes stress human supervision and clear liability lines: legal projects should appoint a responsible human, run documented DPIAs/pre‑market assessments, keep auditable logs of model behaviour, and require explainability and vendor evidence of bias testing before any deployment (Lexology guidance: Recommendations for AI implementation - liability & governance, IAPP global tracker: pre‑market risk assessments for AI legislation).

Practically, this means mandatory anonymization for judicial use, role‑based access, routine security audits, and written sign‑offs so that when an AI suggestion reaches a brief or a bench memo there is a clear trail showing who validated it and why - turning abstract risks into defensible, operational controls that courts and regulators will expect.

The regulatory landscape for AI in Argentina is active and plural: Congress is debating multiple draft laws that together could move the country from soft‑law guidance toward a formal, risk‑based regime - think pre‑market assessments, registries and explicit duties for providers and users - so legal teams should watch proposals like the comprehensive Bill 3003‑D‑2024 and several companion bills that propose a national AI registry and minimum standards (see MoFo's roundup of draft laws for specifics).

These legislative efforts sit atop AAIP and executive dispositions - Resolution 161/2023's transparency and data‑protection program and the 2023

Recommendations for Reliable AI

- which already require impact assessments, human oversight and data safeguards; tracking developments via resources such as the IAPP global tracker helps firms align cross‑border obligations.

Expect a hybrid outcome in 2025: EU‑style risk buckets and pre‑deployment checks for high‑risk systems, coupled with political pressure to keep rules innovation‑friendly (a national registry could read like a public inventory of high‑risk tools), so firms should prioritize audit trails, vendor clauses and adaptable DPIAs now to stay compliant as the regime crystallizes.

Practical checklist: make AAIP and national guidance operational in your firm by turning high-level duties into everyday habits - start every AI project with a documented DPIA and clear sign‑offs, appoint a named responsible person to ensure human oversight and accountability, and enforce strict anonymization (scrub names, metadata and hidden IDs, not just visible fields) before any model query; require vendor due diligence and contract clauses for transparency, bias testing and explainability, keep an auditable prompt library with versioning and human review records, restrict tools via role‑based access and mandatory training, and log uses plus incidents so every output has a traceable human validation.

These steps reflect Argentina's AAIP guidance and the country's emphasis on human supervision and trustworthy AI - see the AAIP Guide for Responsible AI (Sept 2024) for the core obligations and the World Law Group's note on human supervision for practical alignment with ethical standards.

Treat this checklist as routine operational lawyering: DPIAs, vendor proofs, signed validations and periodic bias/quality audits turn abstract duties into defensible, documentable controls that protect clients and preserve professional standards while letting AI speed up research and drafting.

Wrap up: Argentina's 2025 AI moment rewards legal teams that pair curiosity with discipline - watch evolving rules like Bill 3003‑D‑2024 and national programs that stress transparency, human oversight and risk assessments, but don't wait for laws to land before acting (Nemko guide to AI regulation in Argentina).

Practical compliance looks like routine DPIAs, strict anonymization and timestamped audit trails that explain why a model suggestion was accepted or rejected - concrete evidence that will matter in court or before a regulator.

Balance is the goal: protect clients and fundamental rights while using AI to speed research and drafting, and train staff to turn technical requirements into everyday habits.

For hands‑on skills that legal teams can apply immediately, consider structured training such as Nucamp's AI Essentials for Work bootcamp (15 weeks) to build promptcraft, tool governance and prompt libraries that keep provenance intact.

In short: monitor reform, document decisions, and invest in practical training so innovation and compliance travel together across Argentine practice.