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

Chile's AI debate has practical teeth for lawyers: the May 2024 draft Artificial Intelligence Bill - built on a risk‑based model aligned with the EU AI Act - is moving through Congress and recently advanced in deputies' committees toward the Finance Committee (read the latest update at AZ.cl), which means obligations on transparency, documentation and prohibited “excessive‑risk” uses are likely to land on legal desks soon.

Expect thorny issues around liability, gaps in existing privacy law and institutional capacity as regulators and firms adopt new oversight duties; as JDSupra notes,

AI is shifting “from a discretionary tool to a regulated and auditable asset.”

This guide helps Chilean legal professionals turn that legislative momentum into actionable compliance, and practical skills such as those taught in Nucamp AI Essentials for Work bootcamp (Register for the 15-week program) can fast‑track promptcraft and workplace AI literacy.

AI matters for Chilean lawyers in 2025 because it is moving from theoretical promise to concrete pressure on everyday practice: courts struggling with backlogs (Chambers notes procedure readings can take up to a month and final judgments up to six months) are already testing AI for document processing and routine adjudication, and the same dynamics that speed dispute resolution also create regulatory obligations for firms and in‑house teams; the draft law before Congress embraces a risk‑based model (EU‑aligned) that will layer duties - transparency, documentation, human oversight and risk assessments - onto many deployments, from hiring tools to biometric ID and credit scoring (see the bill overview at Chambers report on AI in dispute resolution in Chile, Carey law firm overview of the Chile AI regulation bill, Kliemt analysis of Chile's AI bill and policy limits).

Chile's draft AI law (Bill No. 16821‑19) has quickly become the spine of national AI policy: introduced on 7 May 2024, it adopts an EU‑style, risk‑based taxonomy (unacceptable, high, limited and no‑evident risk) that would subject high‑risk systems to pre‑market checks, documentation, human oversight and reporting duties while placing outright bans on certain “unacceptable” uses, as laid out in the bill overview from Alessandri summary of Chile Bill No. 16821‑19 AI law.

The project has real momentum - recent committee approvals moved it to the Finance Committee - yet commentators flag sharp implementation questions: oversight authority is tied to a future Personal Data Protection Agency, but critics worry whether Chile currently has the institutional capacity or clear liability rules to enforce the new regime, a point explored in a balanced Kliemt analysis of Chile's AI bill and local implementation limits.

For practicing lawyers the takeaway is pragmatic and immediate: the law is designed to reshape deployments and compliance (with penalties and administrative measures on the table), and the stakes are tangible - fines can reach into the tens of thousands of UTM - so firms should map exposures now while watching committee votes and agency design closely, as reported in the latest AZ.cl update on Chile artificial intelligence law latest developments.

Chile's draft AI law uses a four‑tier, EU‑inspired risk taxonomy that has immediate, practical consequences for legal teams: unacceptable‑risk systems (social scoring, subliminal manipulation, mass biometric ID) are banned outright, high‑risk uses (healthcare diagnostics, credit scoring, employment tools, justice support, critical infrastructure) trigger strict risk management, documentation, human oversight and testing, limited‑risk tools (chatbots, recommendation engines) carry transparency duties, and minimal‑risk systems remain lightly regulated - a clear summary can be found in Carey Chile draft AI law overview and in the technical mapping at Janus GRC Chile AI law technical mapping.

For lawyers the “so what?” is concrete: counsel must help clients build an AI inventory and classification playbook, draft contracts that allocate model‑risk and audit rights to vendors, design documentation and impact‑assessment templates, and align AI controls with Chile's cybersecurity and data‑protection duties to avoid administrative fines (reported up to 20,000 UTM) and, for prohibited systems, possible criminal exposure for responsible executives.

The regulatory design also shifts responsibility onto operators to self‑classify systems (rather than an EU‑style pre‑market conformity route), so legal teams should establish repeatable procedures for reclassification, incident reporting, and supervisory engagement to turn a looming compliance burden into a defensible governance advantage.

Chile's incoming AI rules will not float in isolation - they collide with a newly assertive cybersecurity regime, evolving data‑protection rules and updated computer‑crime law in ways that must shape legal advice today.

Where AI systems touch

essential services

or are used by Operators of Vital Importance, Law No. 21.663 (the Cybersecurity Framework Law) already forces operators to run an ISMS, appoint a cybersecurity delegate, and meet tight incident timelines - including a three‑hour early alert and 72‑hour incident report to the National CSIRT - so any high‑risk AI deployment needs incident playbooks aligned with ANCI protocols (see DLA Piper's overview of the Act).

Parallel changes to privacy law (Law 21.719, with breach‑notification and a new Personal Data Protection Agency coming into force) mean AI models that process personal data must bake in privacy‑by‑design controls and clear breach escalation paths; and the modernised computer‑crime statute (Law 21.459) raises the prospect of criminal exposure for negligent or illicit intrusions or misuse of models.

For lawyers that means mapping AI inventories to ANCI classifications, updating vendor contracts for audit and reporting rights, drafting privacy‑by‑design clauses tied to breach timelines, and preparing board‑level risk briefings - because in Chile the compliance clock for cyber and privacy runs fast and non‑compliance can bring heavy sanctions and operational disruption (as the local regulatory primers recommend).

Sector-by-sector, Chilean lawyers should expect the biggest ripples where AI touches people's livelihoods and public services: in healthcare and social security (where SUSESO's machine‑learning pilots processed hundreds of thousands of claims and left roughly 20,000 awaiting decisions), in employment and HR tools, in finance and credit scoring, and in justice‑sector triage - all areas flagged as high‑impact under the draft bill and regional surveys.

World Privacy Forum reporting on public procurement and algorithmic bias shows how public procurement dynamics - templates that once forced bias, transparency and explainability checks but that also weighed price heavily and were later paused - create real trade‑offs for agencies choosing vendors, and that tension will mean more work for counsel drafting procurement clauses, liability allocation, audit and explainability rights, and human‑in‑the‑loop safeguards (see the SUSESO case study at World Privacy Forum).

At the same time, commentators note Chile's EU‑style bill may strain local capacities and blunt innovation unless adapted to domestic realities, so lawyers must help clients design sectoral impact‑assessment playbooks, tailored human‑oversight rules for sensitive use cases, and procurement metrics that elevate vendor governance as well as cost (further context at Ius Laboris).

“'Success' might be defined another way; in situations affecting people's wellbeing and livelihoods, use of a well‑designed and assessed model in support of speedier - yet thoughtful - human decisions can constitute success.”

Start compliance where it pays off: build an AI inventory and classification playbook, pilot tight integrations between your Legal AI and document management system to lock institutional knowledge into workflows, and bake those controls into procurement and vendor contracts so audit rights, model‑risk allocation and clause libraries travel with every deployment - exactly the kind of Legal AI‑DMS connectivity that LexisNexis outlines as a way to generate firm‑aligned drafts and surface precedent in seconds (LexisNexis guide: connecting Legal AI with document management systems to generate firm‑aligned drafts).

Couple that integration with robust governance: codified policies, role‑based access, repeatable impact assessments, and training curricula so users know when to rely on AI and when to escalate - approaches recommended by AI practice teams and trackers that stress policy, compliance programs and tailored oversight (Orrick AI Law Center: AI legal practice and compliance resources).

Finally, treat pilots as data points for scaling governance - measure accuracy, privacy risk and cost‑benefit, and prepare in‑house teams to capture routine work safely (a trend detailed in the Thomson Reuters adoption survey) so firms keep control while in‑house legal expands its remit (Thomson Reuters report on generative AI adoption in law firms).

The memorable test: if a trusted assistant can pull the exact clause from a decade‑old retainer and draft a compliant amendment in under a minute, controls and clear vendor terms must already be in place to prove that speed didn't trade away confidentiality or auditability.

“Within the next six months everybody at the firm will be using it,”

Technical and operational controls in Chilean legal settings should focus on three pragmatic pillars - data and model governance, human oversight, and independent validation - so firms can capture AI's efficiency without inheriting historical harms: start by hardening data controls (data provenance, synthetic augmentation for under‑represented groups and proxy‑variable checks) and formalising vendor terms that guarantee audit rights and explainability; combine that with human‑in‑the‑loop workflows and role‑based access so lawyers remain the final check on high‑impact outputs; and mandate iterative, outcome‑focused audits and continuous monitoring to detect bias drift over time, an approach urged by both the IJCA analysis of judicial AI bias (see the IJCA paper on Bias in AI, 10.36745/ijca.598) and industry guidance on building trust in AI (see PwC's measures to reduce algorithmic bias).

Practical vendor choices matter: small and medium Chilean firms can pilot copilots that integrate with matter workflows (for example, a Clio Duo copilot to automate intake, summaries and billing) but only after contracting for logging, model‑change notifications and third‑party validation.

The “so what?” is stark: a poorly governed model can amplify a century of biased practice into a single, repeatable decision (the COMPAS example in the literature shows how bias can be magnified), whereas a disciplined stack - data controls, explainable models, diverse teams and independent audits - turns AI from a legal liability into a governed productivity multiplier.

Turn legislative uncertainty into a clear, phased compliance playbook: immediately run a rapid AI inventory and regulatory gap analysis to map systems against Chile's risk taxonomy and privacy/cyber timelines, and codify who classifies systems and how - an essential first step highlighted in Nemko's practical checklist for Chilean compliance (Nemko AI Regulation Chile compliance checklist); within 30–90 days, prioritise high‑risk pilots with DPIAs, logging and incident‑reporting hooks, vendor contract addenda that guarantee audit rights and model‑change notices, and a repeatable impact‑assessment template so reclassification is routine rather than ad‑hoc (the Kliemt analysis stresses that employers must treat AI as a regulated, auditable asset, not a discretionary tool: Kliemt analysis Chile AI Bill prepare for AI as a regulated asset); over the next 6–18 months, institutionalise governance - appoint an AI delegate, embed human‑in‑the‑loop rules, adopt ISO/IEC‑aligned controls and sandboxes for safe testing, and document authorisation workflows in case a future AI Commission or Data Protection Agency requires submissions (HGomez's analysis warns that unauthorised high‑risk deployments can carry stiff penalties, including a 200 UTM fine - roughly USD $14,000 - and, in certain breaches, criminal exposure: HGomez analysis Chile AI authorisation penalties high-risk requirements); the practical test: if evidence of model accuracy, DPIAs, contractual audit rights and incident playbooks can be produced within a regulatory review window, the firm has converted a compliance burden into a defensible governance advantage.

Chile's AI debate is entering a moment of action, and legal teams should convert uncertainty into a defensible playbook: run a rapid AI inventory and map systems to the Bill's EU‑style risk taxonomy, clarify where the draft's vague “significant risk” language could widen exposure (see Antonia Nudman's practical column at AZ.cl - An AI law for Chile: The urgency of defining the essentials), prioritise DPIAs and vendor contract addenda for high‑risk projects, and align incident playbooks with Chile's cybersecurity and authorisation windows so evidence of model accuracy, logging and audit rights can be produced within regulator review periods (the Bill contemplates formal authorisation processes and timeframes).

Read the measured policy critique at Kliemt Blog - Chile's AI Bill: a pioneering policy facing local limits to balance compliance with innovation, then institutionalise governance - appoint an AI delegate, embed human‑in‑the‑loop rules, and run training so lawyers and staff know when to escalate; for practical workplace AI skills and prompt literacy, consider cohort training like the Nucamp Nucamp AI Essentials for Work (15-week cohort) to turn regulatory duties into operational capability and make the firm's fast AI drafting both auditable and defensible.

“We are in open conversation with the CMF, the pension supervisory authority, and Subtel to ensure that the mandate given to us by law to have a single window is a reality.”