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

Kazakhstan's AI moment arrived in 2025: the Mazhilis approved a Draft Law “On Artificial Intelligence” in its first reading (May 14, 2025), signalling new obligations for developers, users and public bodies and a risk‑based approach that mirrors the EU's AI Act, yet local scholars warn of gaps in transparency, data safeguards and enforcement that lawyers must navigate (see Chambers' analysis of the Draft Law).

For legal professionals in Kazakhstan this means advising on compliance where rules on human oversight, liability and labeling of AI outputs are still settling, while practical steps - like mastering prompts, data‑bias checks and contract clauses - will be essential to protect clients and capture efficiency gains identified by industry studies.

One vivid fact to note: Kazakhstan launched a Kazakh‑language AI model trained on 148 billion tokens in late 2024, so courtroom evidence and regulatory filings increasingly demand AI‑aware counsel; short, practical training such as Nucamp's AI Essentials for Work can fast‑track those workplace skills.

“I view AI with caution, sometimes even fear. This path leads to degradation. Humans should think and analyze for themselves. Otherwise, where are we headed? Are we merely following global AI trends? What about our traditions, culture, and intellectual capabilities? I fear AI,” Anas Bakkozhayev said.

Kazakhstan's regulatory picture in 2025 is rapidly taking shape: the Mazhilis approved the Draft Law “On Artificial Intelligence” in its first reading on May 14, 2025, and the proposal adopts a familiar, EU-style risk-based architecture that separates high, medium and low-risk systems while adding local features like labeling requirements, human-oversight mandates and expanded liability for developers and owners (see Nemko's regulatory summary on Kazakhstan's AI draft law and Chambers' detailed analysis).

The bill aims to tether ambitious AI adoption to ethical guardrails - prohibiting some autonomous uses, demanding explainability where decisions affect rights, and tightening rules on biometric and large-scale personal data processing - but experts point to gaps in enforcement capacity, algorithmic transparency and data-protection alignment that lawyers must watch closely.

Practically, the draft is paired with national infrastructure: a state National AI Platform, a Kazakh-language model trained on 148 billion tokens (December 2024), and government upskilling efforts for civil servants, yet harmonization with the proposed Digital Code and clearer institutional powers remain work in progress (read Euractiv's coverage of the draft law and Nemko's overview).

For legal professionals this means drafting risk-sensitive contracts, advising on consent and IP limits, and preparing clients for transitional compliance as the law moves from first reading to final text.

“The bill reflects major global trends in AI regulation. Many countries have adopted systematic approaches to AI governance. The EU's AI Act, adopted in 2024, serves as the world's first risk-based AI legislation and is already a model for countries like Kazakhstan,” Saimova said.

By 2025 the global AI story is no longer abstract - and Kazakhstan sits squarely inside that fast-moving arc: Stanford HAI 2025 AI Index report shows legislative attention surging (mentions of AI rose 21.3% across 75 countries since 2023) and dramatic technical and cost shifts - an inference-cost fall of over 280‑fold for GPT‑3.5‑level systems - meaning governments and firms can deploy powerful tools much more cheaply and widely; see the full analysis at the Stanford HAI 2025 AI Index report.

Business adoption echoes the policy rush: industry surveys report roughly three in four organisations using or piloting AI, while market reports put 2025 AI valuation in the hundreds of billions and forecast steep growth, underlining why Kazakh regulators moved the Draft Law “On Artificial Intelligence” into Mazhilis review.

For Kazakhstan's legal community this combination of rapid uptake, tighter rules and sovereign ambitions (including local models and QazTech initiatives) means lawyers must translate global trends into practical counsel - risk‑based compliance roadmaps, contract clauses for provenance and IP, and pragmatic due diligence on third‑party models - so that clients neither stumble into liability nor miss efficiency gains; for a snapshot of market trends and adoption see the Founders Forum 2024–2025 market overview and Nucamp AI Essentials for Work syllabus on linking services to the QazTech platform.

“This year it's all about the customer,” said Kate Claassen, Head of Global Internet Investment Banking at Morgan Stanley.

When the question is “Which country is using AI the most?” the answer depends on the lens: China leads in adoption and the rapid uptake of AI in science and industry, while the United States still dominates model production, funding and hardware - a split that matters for Kazakhstan's lawyers who must advise on procurement, cross‑border data flows and risk allocation.

2024 data show U.S. institutions produced far more headline models (40 notable models) than China (15) and Europe (three), even as China's use of AI in scientific work and firm‑level piloting outpaces peers; the Stanford HAI 2025 AI Index also flags a dramatic drop in costs (inference for GPT‑3.5‑level systems fell over 280‑fold), meaning powerful AI is cheaper and easier to deploy across smaller markets.

For Kazakhstan, the practical takeaway is twofold: expect both offshore models and home‑grown offerings to be available (and sometimes contested) and prepare contract, IP and data‑governance clauses that reflect where a model was built, trained and hosted.

Read the comparative context in the Stanford HAI 2025 AI Index report - global AI adoption data and the analysis of China's rapid scientific adoption in Science|Business: China leads in AI use in scientific research.

“In recent years, China has taken the lead in AI-driven research, outpacing both the US and the EU, not just in sheer output, but also in terms ...”

Which country has the most advanced AI in the world? Short answer for 2025: the United States still sets the frontier in model production and funding, while China has rapidly closed the performance gap on major benchmarks - an important split for Kazakhstan's legal community to translate into practice.

Stanford HAI's 2025 AI Index shows the U.S. produced far more notable models in 2024 and accounted for far larger private investment, even as Chinese models reached near‑parity on tests and China leads in publications and patents; the same report also notes inference costs fell over 280‑fold, making powerful systems far more accessible to smaller organisations worldwide (see the Stanford HAI 2025 AI Index report).

Independent rankings likewise place the U.S. first and China second in R&D leadership (Top 10 Countries in AI R&D rankings (Aug. 2025)).

For Kazakhstan lawyers this means vet every model's provenance, hosting location and training data in procurement clauses; draft IP, liability and cross‑border data provisions that reflect whether a model was built in the U.S., China or elsewhere; and factor rapidly falling deployment costs into vendor due diligence and risk assessments so clients do not inherit opaque or high‑risk systems.

China's public roadmap to “be the world's primary leader in AI by 2030” is no vague boast - it's a coordinated push that mixes state funds, national compute hubs, sectoral “AI Plus” fast‑tracks and local pilot zones, and it is already reshaping regional markets and standards; read the original Next‑Generation AI Development Plan for the playbook China Next‑Generation AI Development Plan full text.

For Kazakhstan this matters in three practical ways: first, Beijing's export of models and governance practices through initiatives like the Digital Silk Road gives Kazakh firms and government bodies faster access to ready‑made AI systems (and to the legal knots that come with cross‑border data, IP and procurement); second, U.S. export controls and China's rush to substitute domestic chips mean some vendors will promise capability without the underlying hardware resilience - an obvious red flag for contract and liability drafting; and third, China's industrial policy (summarised in RAND's Full Stack review) shows how state‑backed compute, talent and open‑source pushes can lower cost barriers and accelerate deployment, creating both opportunity for local modernization and urgency for lawyers to lock down provenance, compliance and risk‑allocation terms RAND Full Stack review of China's AI industrial policy and to watch how the Digital Silk Road spreads standards and datasets Digital Silk Road standards and datasets case study.

A vivid indicator: some analyses now describe Beijing's intent to weave AI into the vast majority of its economy - targets like integrating AI across nearly the whole economy by 2030 - so Kazakh lawyers should treat China's trajectory as a fast‑moving source of both legal risk and practical tools for clients seeking AI adoption.

“China is not behind anybody.”

Start with a pragmatic inventory: map every AI touchpoint in your practice, then classify systems under the Draft Law's risk tiers so high‑risk uses (court‑facing tools, biometric screening, public‑sector decisioning) get immediate, written controls.

Build contracts that force vendor transparency - provenance, hosting location, training data - and carve out IP and liability rules that reflect Kazakhstan's position that authorship is a “person,” while insisting on labeling and human‑in‑the‑loop oversight where required; see Chambers' legal analysis of the Draft Law for drafting pointers and Nemko's regulatory overview for the risk‑tier checklist.

Tighten data practices now: align model training and deployment with Kazakhstan's Personal Data Protection rules, obtain explicit consent for biometrics, and keep immutable audit logs and explainability records so decisions can be defended in court.

Operational steps matter too - appoint an AI compliance owner, run bias and safety checks on models (including that new Kazakh‑language model trained on 148 billion tokens), and pilot governance playbooks through the National AI Platform before scaling.

Finally, treat the transition as iterative: document decisions, engage regulators early, and bake contractual exit and remediation rights into every procurement so clients gain AI advantage without inheriting opaque risk.

“The bill reflects major global trends in AI regulation.”

Contracts in Kazakhstan's AI era must be sharply practical: insist on vendor transparency (provenance, hosting location, training datasets), express allocations of IP for AI‑assisted versus AI‑generated outputs, and clear warranties, audit rights and exit remedies so firms are not left holding opaque models.

The Draft Law and related guidance adopt EU‑style risk tiers, so draft clauses that trigger extra controls for high‑risk systems (human‑in‑the‑loop, labeling, explainability) and tie indemnities to compliance with Kazakhstan's Personal Data Protection Law - remember biometric processing still requires explicit consent and breaches must be reported quickly under local rules (see DLA Piper overview of Kazakhstan data protection).

Liability remains a live issue: the government is weighing a digital code and the draft increases developer/owner exposure where AI harms rights, health or safety, so negotiate limits of liability, insurance requirements and remediation steps now (see the Nemko regulatory summary of AI risk-based checklist).

Intellectual property follows Kazakhstan's human‑centric approach - copyright protects a “creation of the human mind,” so contracts should specify whether a human author, a firm assignment, or joint ownership applies when outputs are produced with tools like the new Kazakh‑language model trained on 148 billion tokens.

Practical drafting that links compliance, provenance and auditability will keep clients functional and defensible as the law settles.

“The bill reflects major global trends in AI regulation. Many countries have adopted systematic approaches to AI governance. The EU's AI Act, adopted in 2024, serves as the world's first risk-based AI legislation and is already a model for countries like Kazakhstan,” Saimova said.

Turn AI from a risk into a repeatable advantage by treating tools, skills and governance as a single operational program: start with an exhaustive inventory of every AI touchpoint (client intake, contract review, court‑facing drafting, biometric screening) and tier systems by risk so high‑risk uses get written controls and human‑in‑the‑loop checks; train teams quickly with local, instructor‑led options such as NobleProg's onsite or online AI courses in Kazakhstan to move lawyers from curious to competent; formalise a short compliance sprint - for example, a 2‑day Certified AI Legal Compliance Analyst workshop by Tonex - to lock in audit rights, explainability checks and vendor‑transparency clauses; adopt a certification roadmap (see USAII's guidance) so paralegals, associates and partners follow a shared curriculum; and embed governance operationally by appointing an AI compliance owner, mandating immutable audit logs, and piloting vendors before scaling.

Practical governance means contracts that force provenance, hosting and training‑data disclosure, bias and safety testing on any Kazakh‑language model you use, and exit/remediation clauses so clients don't inherit opaque systems - small, documented steps that turn regulatory uncertainty into a manageable checklist and real client value (not just a compliance headache).

As Kazakhstan's AI rules move from concept to codified practice - building on the 2015 Law “On Informatisation” and the Mazhilis' first reading of a Draft Law “On Artificial Intelligence” in 2025 - legal practices must turn headline risk into everyday checklists: map every AI touchpoint, classify systems under the draft's EU‑style risk tiers, and insist on vendor transparency for provenance, hosting and training data so contracts can allocate IP, liability and remediation clearly (see Chambers' analysis of the Draft Law).

Align data handling with existing Personal Data Protection limits, treat biometric uses as consent‑sensitive, and log explainability and audit records in case decisions are challenged - Nemko's regulatory overview outlines these practical compliance pillars and the National AI Platform's role.

Train a named AI compliance owner, run bias and safety checks (including on Kazakhstan's new Kazakh‑language model trained on 148 billion tokens), and pick short, practical courses that fast‑track workplace skills; for example the Nucamp AI Essentials for Work syllabus offers a 15‑week, non‑technical pathway to prompt writing and operational governance that helps firms translate regulation into defensible practice.