Artificial intelligence: top priorities for in-house legal teams in 2026
By the end of 2025, AI regulation in major markets had moved from guidance and voluntary principles to binding legal obligations. In 2026, several of those regimes enter operational phases, and regulators are signalling that informal governance will not be considered an adequate control environment.
At the same time, enterprise adoption continues to outpace governance maturity. McKinsey’s 2025 global survey reported 88% of respondents said their organisations were using AI in at least one business function. As AI use expands into customer interactions, credit and pricing, HR decision support, and safety-critical or regulated settings, legal teams need to help the business make defensible decisions about risk appetite, controls, and accountability.
Below are five practical priorities for 2026, framed for decision support.
1) Move from principles to a documented AI risk program
In 2026, “we have AI principles” is unlikely to be sufficient where AI affects customers, employees, or regulated operations. The compliance direction in multiple jurisdictions is towards risk-based classification, pre-deployment assessment, monitoring, and incident response.
EU AI Act timeline: The European Commission’s public timeline confirms the AI Act entered into force on 1 August 2024 and becomes fully applicable on 2 August 2026, with staged obligations and some extended transition periods (including for certain high-risk systems embedded in regulated products).
Vietnam AI Law: Vietnam enacted a standalone AI law in December 2025, effective 1 March 2026, with a risk-based structure that includes system classification and assessment expectations for higher-risk systems.
Colorado AI Act (US): Colorado’s SB24-205 imposes obligations on deployers of “high-risk” AI systems and includes effective dates beginning 1 February 2026 for key deployer duties.
What in-house legal should drive internally in 2026:
A clear definition of what counts as an “AI system” for internal governance (including third-party embedded AI).
A classification method (low/medium/high impact) that ties to controls and approval pathways.
A repeatable pre-deployment review for higher-impact use cases (legal, privacy, security, model governance, human oversight, and testing requirements).
Ongoing monitoring, change control (model updates, prompt changes, retraining), and incident response playbooks.
2) Make transparency obligations operational, not just policy statements
Transparency is becoming a consistent regulatory requirement: people should understand when they are dealing with AI, and organisations should be able to explain the role AI played in significant outcomes.
The EU AI Act includes staged transparency obligations and related requirements across categories of AI systems and models.
South Korea’s AI Basic Act took effect on 22 January 2026 and includes transparency measures (including labelling expectations for AI outputs) and requirements addressing “high-impact” AI.
In Australia, privacy reforms introduce a new transparency requirement for automated decision-making disclosures in privacy policies, effective from 10 December 2026 (applicable where personal information is used to make, or substantially influence, decisions that could reasonably be expected to significantly affect an individual’s rights or interests).
Decision support actions for legal teams:
Require product and procurement teams to document where AI is user-facing, and what disclosures are triggered by geography and use case.
Ensure external statements (marketing, customer support scripts, product documentation) align with how the system actually behaves and its limitations.
For high-impact decision pathways (credit, pricing/eligibility, employment), ensure there is a documented explanation framework, logging, and a human escalation path.
3) Treat safety, bias, and vulnerable-user risk as core compliance themes
In 2026, regulators are increasingly focused on foreseeable and preventable harms: discriminatory outcomes, unsafe content experiences (especially for children), and failures to control high-impact applications.
The Colorado AI Act explicitly targets “algorithmic discrimination” risks for high-risk systems and sets out compliance expectations for deployers.
In the EU, high-risk categories include employment and access to essential services, where bias and explainability are central.
Practical steps:
Require bias and performance testing proportional to the decision's impact, not the model's novelty.
Ensure a defined owner for “harm prevention” controls (content safety, vulnerable cohorts, complaint handling, rapid rollback).
Build a clear escalation route for situations where AI outputs could raise safety concerns, coercion, or discrimination.
4) Converge AI governance with privacy and cybersecurity programs
For most organisations, the biggest AI risk is not “the model” in isolation; it is how the model is connected to data, internal systems, identity/access controls, and external channels.
NIST released a preliminary Cyber AI Profile on 16 December 2025, designed to integrate AI considerations into established cybersecurity governance and risk management practices.
Australia’s forthcoming privacy transparency requirements for automated decision-making will raise the compliance bar for mapping decision flows and the use of personal information by December 2026.
Decision support actions:
Treat AI systems as “sensitive assets” in your security and third-party risk registers (access, logging, incident response, and monitoring).
Align procurement requirements so vendors provide usable information on training data practices, security controls, incident notification, and audit support.
Ensure “AI-enabled” cyber risks are covered: prompt injection, data leakage pathways, model misuse, and tool access abuse.
5) Copyright and training data provenance remain live commercial risks
Copyright and training data issues will continue to drive litigation, regulatory attention, and contractual disputes. For deploying organisations, the practical risk is less academic infringement theory and more whether you can justify your use of tools and outputs in a way that withstands challenge.
A defensible posture generally requires:
clarity on what tools are used and in what contexts (internal use vs external publication)
contractual protections (warranties/indemnities where feasible, limitations understood)
output controls for high-risk use cases (brand, advertising, product content, customer communications)
a documented escalation process for takedown requests and claims
What a “2026-ready” legal plan looks like
A pragmatic legal work program for 2026 usually includes:
Inventory: where AI is used (including shadow AI and embedded vendor tools).
Classification: impact-based tiers with clear control requirements.
Controls: pre-deployment assessment, human oversight design, testing standards, logging, and change management.
Contracts: vendor due diligence, risk allocation, incident reporting, audit rights, and data handling terms.
Transparency: disclosures, privacy policy alignment, customer-facing communications controls.
Response: incident playbooks for safety, privacy, cyber, and legal claims.
The goal is not to slow adoption. It is to ensure the organisation can demonstrate, with evidence, that it identified foreseeable risks, implemented proportionate controls, and responded quickly when issues arise.