2026 Global AI Regulations Guide: EU AI Act Countdown — Enterprise Compliance in 5 Steps

1. 2026: A Pivotal Year for Global AI Regulation

2026 is undoubtedly the most milestone-significant year in global AI regulatory history. The EU AI Act's core provisions — high-risk AI system obligations — will take full effect on August 2^[1], ending a two-year transition period; in the absence of unified federal legislation in the U.S., Colorado and Texas have taken the lead with state laws to fill the regulatory vacuum^[2][3]; and in the Asia-Pacific region, following Taiwan's and South Korea's passage of AI-specific legislation, the regulatory puzzle has gained new pieces. For any enterprise operating internationally, AI compliance in 2026 is no longer a question of "whether to act" but an execution challenge of "how to simultaneously meet multi-country, multi-level, multi-framework requirements."

OECD AI Policy Observatory data shows^[5] that as of early 2026, over 70 countries or economies globally have issued at least one AI-related policy, strategy, or regulation. From a regulatory philosophy perspective, countries broadly fall into three camps: the EU-led "risk-based hard law regulation," the U.S.-led "industry self-regulation supplemented by state laws," and the Japan/Singapore-led "soft governance with industry guidelines." The differences among these three camps reflect not only different political and economic traditions but also directly determine the design direction of enterprise compliance strategies.

This article will analyze the AI regulatory landscape and trends in each major jurisdiction across four regions — the EU, the U.S., China, and Asia-Pacific — and on this basis provide actionable cross-border compliance frameworks and governance practice recommendations for Taiwanese enterprises.

2. EU AI Act: The World's Strictest AI Regulatory Standard

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) officially entered into force on August 1, 2024^[1], making it the world's first comprehensive AI legislation based on risk classification. This regulation's influence extends far beyond EU borders — just as GDPR catalyzed a global wave of data protection legislation, the EU AI Act is redefining the global baseline for AI product and service compliance. For Taiwanese enterprises, even if the company itself is established in Taiwan, it is subject to extraterritorial jurisdiction as long as the output of its AI system is used within the EU.

2.1 Risk Classification System in Detail

The core architecture of the EU AI Act is a four-tier risk classification system, imposing differentiated obligations on AI systems of different risk levels, from "unacceptable risk" to "minimal risk"^[1]:

2.2 Compliance Requirements for High-Risk AI Systems

August 2, 2026 is a date that all enterprises providing or deploying high-risk AI systems in the EU market must remember. By then, Providers and Deployers of high-risk AI systems must fulfill the following core obligations^[1]:

Risk Management System: Enterprises must establish and maintain a risk management system spanning the AI system's entire lifecycle, covering risk identification, risk analysis, risk evaluation, and risk mitigation. This is not a one-time documentation exercise but a continuously operating management process — risk assessment results must be regularly updated in response to system changes, market feedback, and technological evolution.

Data Governance: Training, validation, and testing datasets must meet explicit quality standards, including data relevance, representativeness, error-free nature, and completeness. Enterprises must demonstrate that their data collection and processing considers the AI system's intended purpose, geographic and demographic factors, and that appropriate measures have been taken to detect and address biases in the data.

Technical Documentation: Enterprises must prepare detailed technical documentation before placing the system on the market, covering the system's general description, design specifications, development process, monitoring plan, and conformity statement with applicable standards. The purpose of technical documentation is to enable competent authorities to assess system compliance.

Record-Keeping: High-risk AI systems must have the capability to automatically record event logs to ensure traceability of the system's operation. Logs should cover system activation times, input data reference information, decision outcomes, and any anomalous situations.

Human Oversight: System design must ensure that human operators can appropriately oversee system operations, understand system capabilities and limitations, correctly interpret system outputs, and intervene or disable the system when necessary. This requirement reflects the EU's "human-centric" governance philosophy.

2.3 Dedicated Provisions for General-Purpose AI Models (GPAI)

The EU AI Act has dedicated chapter provisions for General-Purpose AI Models (GPAI), directly affecting all enterprises using foundation models such as the OpenAI GPT series, Anthropic Claude, and Meta Llama. All GPAI providers must fulfill basic transparency obligations — including maintaining technical documentation, providing usage information to downstream deployers, complying with copyright law, and publishing a summary of training content. GPAI models identified as having "Systemic Risk" — currently using a reference threshold of cumulative training computation exceeding 10^25 FLOPs — must additionally conduct model evaluations, adversarial testing, incident reporting, and ensure adequate cybersecurity protection^[1].

3. United States: A Patchwork of State Laws in the Absence of Federal Action

In stark contrast to the EU's unified legislation, the U.S. has yet to pass a comprehensive federal AI regulatory law. Although the White House issued the Executive Order on Safe, Secure, and Trustworthy AI (Executive Order 14110) in 2023, its legal binding force is limited and faces policy direction uncertainty with administration changes. Against this backdrop, states have used their own legislative powers to fill the federal vacuum, creating a complex regulatory patchwork^[7].

3.1 Colorado AI Act (SB 24-205)

The Colorado AI Act is the first comprehensive AI consumer protection law in the U.S.^[2] and will take effect on June 30, 2026 (originally scheduled for February 1, 2026, later postponed to June 30). Key features of the act include:

Scope: Applies to Developers and Deployers of "high-risk AI systems" operating in Colorado or serving its residents. "High-risk AI systems" are defined as those making or substantially assisting "consequential decisions" in education, employment, financial services, healthcare, insurance, housing, or legal services.

Developer obligations: Developers must provide deployers with reasonable documentation and information enabling them to understand the AI system's functionality, limitations, intended uses, and known risks; publicly disclose known or reasonably foreseeable risk types; and conduct bias testing and mitigation measures before system release.

Deployer obligations: Deployers must implement risk management policies and procedures; notify consumers before or within a reasonable time after a consequential decision that an AI system is being used; provide consumers with channels to appeal AI decisions; and notify the state attorney general within 90 days upon learning that an AI system has caused Algorithmic Discrimination.

3.2 Texas TRAIGA (HB 1709)

The Texas Responsible AI Governance Act (TRAIGA) has been in effect since September 1, 2025^[3], making it one of the most important state-level AI regulations currently in force in the U.S. Compared to the Colorado AI Act, TRAIGA takes a different regulatory approach:

Focus on "high-risk" decisions: TRAIGA limits its scope to situations where "deployers use generative AI to make, or as a significant factor in making, consequential decisions." Its "Consequential Decision" covers employment, education, finance, healthcare, insurance, and housing.

Transparency and notification obligations: When deployers use generative AI to make decisions with legal effect or similarly significant impact on individuals, they must notify affected individuals before or within a reasonable time after the decision and provide an appeal pathway.

Anti-discrimination provisions: The act prohibits using AI systems for illegal discrimination based on protected characteristics (race, gender, age, etc.) and requires enterprises to take reasonable measures to prevent algorithmic discrimination.

3.3 Other State Laws and Federal-Level Developments

Beyond Colorado and Texas, multiple states have passed or are advancing AI-related legislation: Illinois' AI Video Interview Act requires employers to obtain candidate consent when using AI to analyze video interviews; California's multiple AI bills cover deepfakes, AI-generated content disclosure, and election AI; New York City Local Law 144 requires employers using automated employment decision tools to conduct annual bias audits. As of early 2026, over 45 states have proposed AI-related bills^[7]. At the federal level, NIST AI RMF^[6], while not a mandatory regulation, has become the de facto standard for enterprises building AI governance frameworks and an important reference for courts and regulators assessing enterprises' "reasonable duty of care."

4. China's AI Regulation: Sector-by-Sector Incremental Control

China is one of the earliest countries globally to enact specific legislation for particular AI applications. Unlike the EU's "one regulation covering all AI" comprehensive approach, China has adopted a "sector-by-sector, batch-by-batch" regulatory strategy, issuing dedicated regulations for different AI application scenarios, forming a layered but relatively fragmented regulatory system.

Deep Synthesis Provisions (effective January 2023): Targeting deepfakes and AI-generated content, requiring service providers to label deep synthesis content, implement user real-name registration, and establish content review mechanisms. This is one of the earliest AI-generated content regulations globally.

Interim Measures for Generative AI Services (effective August 2023): Requiring generative AI service providers to conduct legality reviews of training data, implement content filtering mechanisms, clearly label AI-generated content for users, and file algorithm registrations with competent authorities. This applies to organizations providing generative AI services to the public within China.

Algorithm Recommendation Management Provisions (effective March 2022): Targeting internet platforms using algorithms for content recommendation, requiring algorithm transparency, user opt-out mechanisms, and prohibition of algorithmic price discrimination.

AI Safety Governance Framework (published September 2024): The Cyberspace Administration of China's AI Safety Governance Framework is currently China's most comprehensive AI governance policy document. This framework covers AI R&D safety, model safety, data safety, and application safety, and for the first time explicitly proposes "safety evaluation" and "safety audit" requirements for AI systems, signaling that China's AI regulation is moving from sector-specific rules toward a more systematic governance architecture.

The implication of China's AI regulatory model for Taiwanese enterprises is clear: even when operating in the Chinese market or providing AI services to Chinese users, enterprises need to comply with multiple dedicated regulations rather than a single AI law. Furthermore, China's requirements for AI content safety (particularly ideological compliance and content review mechanisms) are fundamentally different from the EU-U.S. regulatory system, and enterprises must fully account for this specificity when designing cross-border compliance frameworks. Notably, China's algorithm registration system is a unique regulatory mechanism globally — enterprises must complete algorithm registration with the Cyberspace Administration before providing algorithmic recommendation or generative AI services within China. This requirement has no international precedent, and Taiwanese enterprises entering the Chinese market need to invest additional compliance resources.

5. Asia-Pacific Region: Japan, South Korea, Singapore, and Taiwan

AI regulation in the Asia-Pacific region presents a highly diverse landscape. From Japan's soft governance to Taiwan's and South Korea's principles-based legislation to Singapore's industry-oriented approach, each economy has chosen a distinctly different path, all seeking to balance innovation promotion with risk management^[5].

5.1 Japan: A Leader in Soft Governance

Japan has chosen a "social principles plus industry guidelines" soft governance route, with no dedicated hard AI law enacted to date. The 2019 "Social Principles of Human-Centric AI" established seven fundamental principles — human-centricity, education/literacy, privacy protection, safety assurance, fair competition, fairness/accountability/transparency, and innovation. Under this principles framework, various ministries issue industry guidelines: METI issued AI Governance Guidelines, and the Ministry of Internal Affairs and Communications issued application guidelines for telecommunications AI. Japan's hallmark is its strong emphasis on industry self-regulation and multi-stakeholder dialogue mechanisms, rather than mandatory legal regulations.

However, as the EU AI Act's extraterritorial effect creates compliance pressure on Japanese exporters, the Japanese government has also begun exploring whether more binding regulations are needed in specific domains. In 2025, the Japanese government established an "AI System Study Group" to explore whether mandatory regulations should be introduced for high-risk AI applications (such as healthcare, autonomous driving, and finance). Japan's regulatory evolution is worth close attention from Taiwanese enterprises, as Japan and Taiwan share highly similar industrial structures (semiconductors, electronics manufacturing, precision machinery) and export markets (primarily EU and U.S.), making Japan's compliance strategies highly relevant reference points.

5.2 South Korea: An Asia-Pacific Pioneer in AI Basic Law

South Korea passed the AI Basic Act in January 2025, making it one of the earliest countries in Asia-Pacific to enact comprehensive AI-specific legislation. Key features of South Korea's AI Basic Act include: adoption of a high-risk AI classification system covering life/safety and fundamental rights impact areas; establishment of an AI Committee as a cross-ministerial coordination body; requiring developers of high-risk AI systems to conduct Impact Assessments; and imposing labeling obligations on AI-generated content^[5].

The timing of South Korea's legislation closely parallels Taiwan's AI Basic Act, and both countries' legislative experiences can serve as mutual references — particularly regarding high-risk AI classification standards and the speed of industry guideline development. South Korea has taken a more proactive stance on AI industrial policy, with the government investing heavily in AI chips, AI healthcare, and AI manufacturing, while simultaneously establishing regulatory sandbox mechanisms on the regulatory side, allowing innovative AI applications to be tested in controlled environments. This "promotion and regulation in parallel" model is worth Taiwan's consideration.

5.3 Singapore: A Pragmatic Industry-Oriented Approach

Singapore has adopted the world's most industry-oriented AI governance model. IMDA published the second edition of its Model AI Governance Framework in 2024^[9], proposing four core principles: internal governance structures and measures, human involvement in decision-making processes, operations management and monitoring, and stakeholder interaction and communication. Singapore's hallmark is its "AI Verify" framework — an open-source AI governance testing toolkit that enables enterprises to self-verify their AI systems' performance across dimensions such as fairness, transparency, and robustness. This "tooling-first" strategy lowers the compliance barrier for enterprises and provides an actionable baseline for multinational companies.

5.4 Taiwan's AI Basic Act: Alignment Challenges Under a Principles Framework

Taiwan's AI Basic Act was promulgated and took effect on January 14, 2026^[4], establishing four principles — "human-centric, sustainable development, effective governance, reasonable accountability" — and designating the National Science and Technology Council (NSTC) as the central competent authority^[10]. Taiwan's choice of principles-based legislation rather than detailed regulation preserves policy flexibility on one hand, but on the other has sparked industry anxiety about "when will compliance standards become clear?" The most closely watched next step is the high-risk AI classification guidelines expected from MODA (Ministry of Digital Affairs) in the first half of 2026 — these guidelines will directly determine which enterprises' AI systems need priority compliance.

From the perspective of global regulatory alignment, Taiwan's AI Basic Act faces three critical challenges. First, interoperability with the EU AI Act: a large number of Taiwanese tech companies' products and services enter the EU market, requiring local compliance frameworks that can interface with EU AI Act requirements to reduce enterprises' duplicate compliance costs. Second, consistency with OECD AI Principles: Taiwan's AI Basic Act's four principles are highly consistent with OECD AI Principles^[5], but implementation mechanisms still need to catch up quickly. Third, timeline pressure for subordinate legislation and industry guidelines: the effectiveness of principles-based legislation depends on the speed and quality of subsequent subordinate legislation — if the subordinate legislation process is too slow, enterprises may remain in a gray area of unclear compliance standards for extended periods.

The most pragmatic strategy for Taiwanese enterprises currently is a "dual-track approach": on one hand, closely track MODA's high-risk classification guideline progress to prepare for local compliance^[10]; on the other hand, for AI products already in or planned for the EU market, build compliance directly to EU AI Act standards — this investment serves not only the EU market but also lays a solid foundation for local compliance when Taiwan's subordinate legislation is issued. The Financial Supervisory Commission has already published core principles for AI applications in the financial industry, and industry guidelines from the Ministry of Health and Welfare and the Ministry of Labor are also being developed. Enterprises should actively participate in public consultation processes to ensure the feasibility of industry practices is fully considered in standard-setting.

6. Enterprise Cross-Border AI Compliance Framework: A Three-Layer Architecture Methodology

Facing the fragmented global AI regulatory landscape, it is impossible for Taiwanese enterprises to build independent compliance systems for each jurisdiction — this is not only too costly but would also cause internal governance chaos. Based on Deloitte's AI governance research^[8] and NIST AI RMF guidance principles^[6], we recommend enterprises adopt a "three-layer architecture" cross-border compliance methodology:

6.1 Foundation Layer: NIST AI RMF as the Core Governance Framework

NIST AI RMF's^[6] Govern-Map-Measure-Manage four-function framework provides enterprises with a regulation-agnostic AI governance methodology. The advantage of using this as the foundation layer is that it is not tied to any specific country's regulatory requirements, yet its governance principles are highly compatible with all major global AI regulations. Govern — establish organization-level AI governance policies, roles, and processes; Map — identify and analyze AI system risk sources and impact scope; Measure — evaluate and quantify AI system risk levels, establishing metrics and thresholds; Manage — implement risk mitigation measures and continuous monitoring. The governance capabilities built on this foundation layer can universally serve compliance needs across all countries.

6.2 Compliance Ceiling Layer: EU AI Act Standards

For enterprises operating in or planning to enter the EU market, the EU AI Act's requirements should serve as the compliance ceiling^[1]. The rationale is twofold: first, the EU AI Act is currently the world's strictest AI regulation, and meeting its requirements typically means meeting other countries' requirements as well; second, just as GDPR's "Brussels Effect" spread globally, the EU AI Act is becoming the de facto global standard for AI compliance — many countries' subsequent legislation references its risk classification and compliance requirements. Specifically, enterprises should align with EU AI Act standards in the following areas: risk classification methodology, technical documentation requirements for high-risk AI systems, conformity assessment processes, and transparency obligations.

6.3 Local Adaptation Layer: Country-Specific Requirements

On top of the foundation and ceiling layers, enterprises need to make localized adaptations for special regulatory requirements in each operating market. For example: in the Chinese market, additional compliance with algorithm registration, content safety, and training data legality review requirements is needed; in various U.S. states, specific consumer notification mechanisms and bias audit requirements must be addressed^[2][3]; in Taiwan, dual compliance with the AI Basic Act and the Personal Data Protection Act must be aligned^[4]; in Singapore, AI Verify tools can be leveraged as compliance verification aids^[9]. The key to the local adaptation layer is "incremental management" — investing additionally only in areas that the foundation and ceiling layers cannot cover, avoiding redundant construction.

7. Compliance Timeline Overview and Action Plan

Below is a summary of the key timeline milestones for global AI regulations in 2026, helping enterprises prioritize their compliance actions^[7][8]:

7.1 Immediate Action (2026 Q1): Inventory, Assessment, and Awareness Building

The first step of compliance is always "knowing what you have." Led by the IT department with coordination from legal and business units, enterprises should build a comprehensive AI system registry. The inventory scope should include not only self-developed AI systems but also all third-party AI services in use — including AI features embedded in SaaS products, generative AI tools (such as ChatGPT, Claude, Copilot), and AI modules provided by suppliers. Simultaneously, conduct AI regulatory briefings for senior management to ensure the board and C-level are aware of how the global AI regulatory landscape affects the enterprise.

7.2 Short-term Planning (2026 Q2): Gap Analysis and Framework Building

Complete an AI compliance Gap Analysis — comparing the current state of the enterprise's AI systems against regulatory requirements in each operating market to identify compliance gaps requiring priority attention. For high-risk AI systems entering the EU market, risk management systems, technical documentation, log-keeping mechanisms, and human oversight processes should be completed before August 2. Simultaneously, begin Colorado AI Act compliance preparation — establishing risk management policies, consumer notification mechanisms, and appeal channels.

7.3 Mid-term Deepening (2026 Q3-Q4): Institutional Operations and Continuous Improvement

Embed AI governance into the enterprise's existing governance architecture — incorporate AI risk into the Enterprise Risk Management (ERM) framework and include AI compliance in internal audit plans. Establish model lifecycle management processes covering governance checkpoints across development, testing, deployment, monitoring, updating, and retirement. Track the latest developments in subordinate legislation and industry guidelines across countries — Taiwan MODA's high-risk classification guidelines, South Korea's implementation rules, EU AI Act enforcement cases — and adjust local compliance measures accordingly^[8].

7.4 Long-term Vision (2027 and Beyond): Compliance Culture and Competitive Advantage

Mature AI governance capabilities are not just a compliance cost but a strategic enterprise asset. In international supply chains, enterprises with robust AI governance frameworks will enjoy preferred trust from partners; in capital markets, thorough AI governance disclosure in ESG reports will enhance investor confidence; in consumer markets, responsible AI use will become a new dimension of brand differentiation^[8]. Enterprises should establish periodic AI governance maturity assessments, continuously track international regulatory evolution, and convert compliance experience into standardized internal knowledge assets.

8. AI Governance Framework Building in Practice

Regardless of the order in which enterprises address various countries' regulations, a robust internal AI governance framework is the foundation of all compliance work. The following provides actionable building guidelines across three dimensions: organizational structure, process design, and technical foundations.

8.1 Governance Organizational Structure

Enterprises should establish an AI Governance Committee as a cross-functional standing body, comprising at minimum the following roles: CTO or CIO (serving as chair), Chief Legal Officer (responsible for regulatory compliance), Chief Risk Officer (responsible for risk management), Chief Data Officer (responsible for data governance), and key business unit representatives. The committee's core functions include: setting AI usage policies, reviewing high-risk AI project deployment requests, handling AI ethics disputes, coordinating cross-border compliance standards, and regularly reporting on AI governance to the board. At the board level, at least one director should possess AI literacy to effectively challenge AI risk reports^[8].

8.2 AI System Risk Assessment Process

Enterprises should establish a standardized AI system risk assessment process, evaluating each AI system across five dimensions: Decision Impact — does the system's output directly or indirectly affect individuals' significant rights (employment, credit, health, education)? Data Sensitivity — does the system process personal data, sensitive attributes, or confidential business information? Degree of Autonomy — does the system's decision have a human review step, or is it fully automated? Scale and Scope — what is the number of people affected and the geographic scope? Reversibility — are the system's decisions easily reversed or corrected? Based on this assessment, classify AI systems into high, medium, and low risk levels, with differentiated governance requirements for each — high-risk systems require independent verification and continuous monitoring, medium-risk systems require self-assessment and periodic review, and low-risk systems need only basic registration and recording.

8.3 Vendor and Third-Party AI Management

Modern enterprises' AI compliance challenges extend beyond self-developed AI systems. Much of the AI capability enterprises use comes from third-party vendors — AI features embedded in SaaS products, large language models called via API, AI modules provided by suppliers, etc. The EU AI Act explicitly stipulates that AI system "Deployers," even if they are not the system's developers, remain responsible for the system's compliant use in their business context^[1]. This means enterprises cannot fully transfer compliance responsibility to vendors.

Enterprises should incorporate AI compliance assessments into their procurement process — requiring third-party AI vendors to provide Model Cards, data governance statements, bias testing reports, and risk assessment results. Contracts should clearly specify vendor compliance obligations, information disclosure scope, incident notification timelines, and indemnification liability. For third-party AI systems used in critical business scenarios, enterprises should conduct independent verification testing rather than relying solely on vendors' self-declarations.

8.4 Continuous Compliance Monitoring Mechanisms

AI compliance is not a one-time project but a continuously operating management process. Enterprises should establish three types of monitoring mechanisms: Regulatory dynamics monitoring — assign dedicated personnel or commission external consultants to continuously track the latest global AI regulatory developments, compiling monthly regulatory update summaries for the AI Governance Committee; System performance monitoring — implement real-time performance tracking for deployed AI systems, detecting Model Drift, data distribution changes, and anomalous prediction behavior; Compliance status monitoring — build a compliance dashboard showing each AI system's compliance status across operating markets, upcoming compliance timeline milestones, and unresolved compliance gaps^[6].

9. Conclusion: From Regulatory Fragmentation to Unified Governance Capability

The 2026 global AI regulatory landscape presents an image of unprecedented complexity. The EU sets the global bar with the strictest hard law^[1], the U.S. pieces together regulatory coverage through decentralized state laws^[7], and Asia-Pacific countries explore their own paths between soft governance and principles-based legislation. This fragmented regulatory reality will not change in the short term — in fact, as more countries join AI legislative efforts, complexity will only continue to increase.

For Taiwanese enterprises, the correct response is not passively waiting for regulations to become clear, but proactively building internal AI governance capabilities. AI governance capability is "transferable" — an enterprise that has established a comprehensive governance system under the EU AI Act framework, when facing new regulations from Taiwan or other countries, only needs incremental adaptation rather than starting from scratch^[8]. Conversely, scrambling to respond only as regulations take effect one by one results in higher compliance costs and potentially missed market access windows.

From a broader perspective, global AI regulatory convergence is an irreversible long-term trend. OECD AI Principles^[5] have already provided countries with a common value foundation, international standards such as ISO/IEC 42001 are building mutual recognition bridges for cross-border compliance, and the EU AI Act's "Brussels Effect" is exporting its standards globally. Enterprises that build international-grade AI governance capabilities early will hold a first-mover advantage in this convergence process — not only at the compliance level but also in winning customer trust, entering international supply chains, and attracting top talent.

The pace of AI regulatory evolution may not keep up with the iteration speed of AI technology itself, but the direction of regulation is clear: transparency, accountability, and human-centricity. If enterprises can internalize these principles as organizational culture rather than viewing them merely as a compliance burden, they will see not risk but opportunity in every regulatory update.

Finally, we want to emphasize: AI compliance should not be viewed as an isolated legal task but as an integral part of the enterprise's digital transformation strategy. A well-designed AI governance framework not only protects enterprises from regulatory penalties but also improves AI system quality and reliability, enhances stakeholder trust, reduces AI project failure risk, and ultimately creates tangible business value. This is the core idea of "compliance as competitiveness."