Systematization and Prospects of LegalTech through Digital Technology
With the advancement of digital technology, the way rules such as laws are formed is changing. It is necessary to consider a new architecture aimed at the structuring, automation, and reliability of law. While it is called LegalTech, it is a concept that broadly includes laws, rules, regulations, and guidelines, and can be applied not only to administration but also to the rules of private companies.
Let's organize recent developments.
Chapter 1 Introduction: Redefining LegalTech
In recent years, LegalTech has evolved from being a mere 'legal efficiency tool' to the re-architecting of the legal system itself through data and technology.
At its core are the following three directions.
Law as Data:
Structuring laws, contracts, regulations, and precedents in a machine-readable format, and organizing term definitions as data to enable interoperability.Law as Process:
Visualizing, supporting, and simulating legislation, interpretation, and operation using AI and rule engines.Law as Infrastructure:
Ensuring traceability, transparency, and accountability through blockchain, eIDAS, FAIR-R, etc.
Here, we capture LegalTech as a comprehensive system of these three layers and organize it systematically while considering European trends (ELI/ECLI, AKN4EU, RaC, etc.).
Chapter 2 Structural Transformation Brought by the Digitalization of Law
In a digital society, law is no longer a static text, but is being reconstructed as a dynamic and self-learning social system. And machine readability is required. As the real world and the digital world are connected in real-time through AI, data spaces, smart cities, etc., the concept of 'continuous improvement' is also required for the legal system.
The factors can be summarized into the following four points.
1. Information Overload and the Rise of the AI Society
In an era where AI is used for administrative decisions and contract reviews, legal rules must be expressed in a 'format that AI can understand.' Structuring articles, definitions, obligations, and exceptions is a prerequisite for AI transparency and fairness.
2. Demand for Real-time Administration and Smart Governance
Social activity history is acquired in real-time through sensors and blockchain. This makes it possible to instantly grasp the application status and social effects of laws, and to flexibly update regulations and policies.
3. Institutionalization of Feedback Loops
In Evidence-based Policy Making, laws and systems are not finished once enacted; the focus is on a loop of collecting implementation data, measuring effects, and reflecting them in the next legislation. This cycle is also called the 'Digital Law Lifecycle' and holds the key to the agility and transparency of administration.
4. Demands for Accountability and Transparency
In a society where AI and algorithms are involved in decision-making, the obligation to explain 'why that decision was made' is expanding.
Thus, the digitalization of law is positioned not just as efficiency, but as an implementation → evaluation → redesign institutional reform that rebuilds social trust through continuous improvement.
Chapter 3 Three-Layer Structure Model of LegalTech
3.1 Three-Layer Structure Model of LegalTech
From the perspective of 'what should be guaranteed' (clarification of governance objectives), it can be organized as follows.
3.2 Governance Layer: Integrated Design of Law and Ethics
This layer defines the very 'purpose' of LegalTech.
Here, institutional design is conducted to ensure the legitimacy of law and the accountability of AI and data.
Role: Defines social, legal, and ethical values, and directs the behavioral principles of technology.
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Key Institutions:
AI Act: Mandatory risk classification, transparency, and accountability for AI.
Data Act / Data Governance Act: Guarantees fairness in data access rights and sharing.
Interoperable Europe Act (IEA): Institutionalizes interoperability between administrations and across borders.
eIDAS2: Trust infrastructure for electronic signatures and digital IDs.
Deliverables: Codifies laws, ethical norms, and policy principles (Rule as Code) into a format interpretable by AI and systems.
In other words, the governance layer is the 'legal OS layer that guarantees the coexistence of AI and humans,' serving as the ideological core that integrates other layers (meaning and trust).
3.3 Semantic Layer: Knowledge Infrastructure Supporting Mutual Understanding
This layer aims to 'eliminate discrepancies in meaning' between law, data, and AI.
In Europe, semantic interoperability is positioned as the foundation of AI governance.
Role: Enables both machines and humans to interpret data and laws with the same meaning.
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Key Technologies:
RDF/OWL: Describing laws, contracts, and rules in a graph structure.
DCAT-AP, CPSV-AP: Semantic standards for administration and open data.
ELI / ECLI: Managing laws and judicial precedents with consistent identifiers and metadata.
Knowledge Graphs (EU KG, CELLAR, EURIO): A knowledge base linking policies, laws, and organizations.
Deliverables: An environment where AI can understand the structural, definitional, and scope-of-application aspects of legal provisions and verify logical consistency.
This layer goes beyond mere vocabulary sharing to form a "knowledge infrastructure that shares meaning across society as a whole." Its greatest feature is that people, organizations, and AI can make legal judgments using the same "language (ontology)."
3.4 Trust Layer: Guaranteeing Quality and Authenticity
In LegalTech, "trust" is not merely a security issue. It is a critical matter that makes the entire process of legal judgment, AI output, and data usage explainable.
Role: Making it possible to verify "who" created data and AI results, and "on what basis."
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Key Technologies:
FAIR-R: High-quality data standards usable by AI (Findable, Accessible, Interoperable, Reusable + Reliable).
PROV-O / Provenance Framework: Explicit documentation of data origin, processing history, responsible entities, etc.
Verifiable Credential (W3C): Digitization of legal certification.
Blockchain / Timestamping: Tamper prevention and persistence of trust history.
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Deliverables:
Enables reproduction of 'which decisions were made based on which data and evidence' among AI, government, and citizens.
Guarantees AI safety based on data quality verification results.
The purpose of this layer is to 'digitize trust.' In other words, it ensures the transparency, explainability, and authenticity of legal processes through code and data.
3.5 Interrelation of the Three Layers: System × Meaning × Trust
These three layers are not independent but function complementarily.
System Layer → Meaning Layer: The objectives of laws and policies are embodied as semantic structures (ontology, vocabulary).
Semantic Layer → Trust Layer: Data integrity and audit trails are guaranteed through shared definitions and identifiers.
Trust Layer → Institutional Layer: Accountability based on trust data is fed back into the system and reflected in policy revisions and legal amendments.
Through this cycle, a Trust-driven Semantic Governance Model is realized, in which the collaboration between law, AI, and society is continuously updated.
3.6 Summary: A New Structure of Law in an AI Society
The three-layer structure of LegalTech symbolizes the following ideological shift.
In other words, the future of law is being reconstructed not as 'paper text,' but as a social system centered on meaning and trust. Understanding this structure is the key to simultaneously ensuring AI safety, data quality, and legal reliability.
Chapter 4: Digitalization of Legal Usage
4.1 Search and Understanding of Legal Information
Traditionally, the use of law has relied on human reading comprehension, but with the development of digital technology, legal search, comparison, and automatic summarization are becoming a reality.
Search engines based on Natural Language Processing (NLP) and knowledge graphs (e.g., EUR-Lex, Legislation.gov.uk) analyze the structure of provisions, defined terms, and reference relationships, enabling information retrieval based on context.
As a result, the direction is shifting from searching for laws to understanding legal meaning.
Particularly in the EU, the ELI (European Legislation Identifier) has made it possible to permanently identify legislation, facilitating comparisons between the legal systems of different countries.
4.2 Comparison of Laws and Compliance Checking
When companies and governments introduce new technologies, there is a need for mechanisms that automatically determine whether relevant regulations apply.
For example, for high-risk technologies such as generative AI and medical devices, initiatives to assist with conformity assessment using AI are also required.
In addition, the use of model contracts and regulatory templates (Smart Contract Templates) promotes the interoperability of laws and structural understanding.
4.3 Automation and Verification of Legal Documents
By utilizing Natural Language Generation (NLG) and rule-based engines, the automatic creation of contracts and automatic verification of legal compliance are progressing.
Contracts are not just documents, but are required to be managed as structured data that can be automatically updated when laws change. Such technologies are being considered in a direction that integrates law enforcement, auditing, and accountability through collaboration with RaC (Rule as Code).
4.4 Global Legal Compliance
As digitalization and the internationalization of supply chains progress, cross-border legal compliance and the cross-sectional understanding of national laws and regulations have become important issues.
In particular, the role of LegalTech is rapidly expanding from the following three perspectives.
(1) International Trade
Companies need to legally determine 'which countries they can export their products and services to.'
By modeling the laws of each country as machine-readable rules, a system can be developed in which AI automatically determines risk countries, prohibited items, and approval requirements.
(2) Cross-border Data and Privacy Compliance
With the global expansion of cloud and AI, compliance with laws regarding data transfer has become an essential requirement for companies and governments. In addition to the GDPR (EU General Data Protection Regulation), each country has enacted its own personal information protection laws. Efforts are being made to manage these in an integrated manner, and the use of knowledge graphs is also being considered.
Functions that use AI and other tools to analyze data localization clauses in each country and automatically select and advise on data distribution routes are also required, but for this, the development of reference data is essential.
(3) Collaboration on Global AI and Ethical Regulations
Consistency in international AI legislation, such as the AI Act (EU) and AI ethics guidelines (OECD/UNESCO), is required. LegalTech has the potential to provide an environment where these rules can be ontologized, compared, and verified. For example, if a knowledge graph that maps AI-related guidelines from each country is developed, it may become possible for global companies to verify compliance using the same standards across multiple countries.
4.5 The New Face of Global Legal Affairs
In this way, the demand for LegalTech is shifting from "digitization of domestic law" to "machine-readability and automated compliance of international legal regulations".
By realizing such technologies and environments, companies will be able to automatically verify the laws of various countries and coordinate in real time.
Determination of exportability
Legality check for personal data transfer
Compliance audit of international contracts
Ultimately, the goal is for AI to analyze differences between laws and to oversee, translate, and coordinate international legal systems.
4.6 Automated Generation of Contracts and Regulations
Using generative AI and syntactic analysis, it has become possible to automatically create contracts, detect risks, and revise clauses. Furthermore, modularization by functional block makes it possible to increase the reusability of laws.
Legal documents will evolve into a hybrid representation of "natural language x structured data".
4.7 Development toward "Law as a Dictionary"
Many terms are defined within laws; efforts to turn these into dictionaries and attempts to link laws, precedents, and guidelines to reuse them as a Knowledge Graph are progressing.
ELI/ECLI, CELLAR, and EU Vocabularies are prime examples of this.
Chapter 5: Law Creation and RaC (Rule as Code)
5.1 Designing Law as Code
RaC (Rule as Code) is a method of redesigning law as "machine-readable code." Laws are expressed through programming-like code or modeling. Furthermore, by utilizing and expressing them through structured data (LegalRuleML, AKN4EU), etc., they can be interpreted commonly by governments, companies, and AI.
This ensures "consistency of interpretation," "transparency," and "immediate executability".
5.2 Introduction of Modeling Technology
In the implementation of RaC, multiple modeling languages are linked to consistently express business procedures, logical structures, and data structures.
BPMN: Definition of procedures based on laws and regulations
UML / SysML: Structuring of systems, roles, and objects
Ontology / GraphDB: Modeling of legal concepts, definitions, and relationships
ArchiMate: High-level architectural model for overseeing the entire system (described later)
With this, law changes from "documents to be read" to "models to be executed".
5.3 Architecture Design of Legal Systems using ArchiMate
ArchiMate is a standard framework capable of modeling everything from policy objectives to technical infrastructure in an integrated manner, making it extremely effective for LegalTech.
In particular, it can be utilized from the following three perspectives when implementing RaC.
Connecting objectives and means:
Connect objectives in a traceable manner down to data management and rule implementation.Visualization of multi-layer structures:
It is possible to depict the legal system as an overall design, spanning from the policy layer (Motivation) to the business layer (Business), application layer, data layer, and technology layer.System revision and impact analysis:
It becomes easier to extract processes, data, and systems affected by legal amendments, thereby facilitating agile legislation and dynamic governance.
For example, if the "High-Risk AI System Certification Process" of the AI Act is modeled using ArchiMate, it will look as follows, allowing the hierarchical structure to be understood at a glance.
Policy Goal: Safety and Accountability
Business Process: AI Review, Registration, and Monitoring
Application Component: RaC Engine and Audit DB
Data Object: FAIR-R Compliant Data and Model Cards
Technology Service: eIDAS Signature and Provenance Records
5.4 Agile Governance
This is a method of operating policies through cycles of small-scale experimentation, evaluation, and revision. Legal systems modeled with ArchiMate, BPMN, etc., can be quickly reflected in simulations, evaluations, and revisions.
In Europe, in addition to this, institutional verification is conducted through Regulatory Sandboxes, etc., supporting the continuous improvement of laws.
Chapter 6 Legal Introduction and Verification: From Implementation to Improvement
6.1 Simulation and Impact Analysis
In the implementation of digital legislation, it is essential to conduct a Legal Impact Assessment before enactment.
This is clearly stated in the AI Act and the Interoperable Europe Act, and it is a mechanism to verify from multiple perspectives "how new laws and regulations affect data usage, corporate activities, civil rights, and ethics."
This realizes Evidence-based Lawmaking.
6.2 No-code Introduction and Circular Use of Operational Data
By utilizing Low-code/No-code environments, government officials and companies can implement legal compliance applications themselves.
In operation, implementation data is automatically collected and evaluated, and used as the basis for the next legal amendment or guideline update.Laws evolve from "static text" to "self-learning systems".
Such mechanisms are compatible with the concepts of AI auditing, MLOps, and RegOps (Regulation Operations).
Chapter 7 Ensuring Trust in Legal Infrastructure and Data Quality
In a digital society, the reliability of the legal infrastructure is the "heart" of the entire social system. If the integrity of legal data or identification information is compromised, the chain of trust across society—including administrative decisions, AI inferences, and contract enforcement—will collapse.
Therefore, the central challenge of LegalTech is not "automation," but "Institutionalized Trust." To support this reliability, the following four points are essential, and guaranteeing them technically and institutionally is a prerequisite for the digital implementation of law.
Data Quality
Provenance
Integrity & Authenticity
Semantic Consistency
7.1 Reference Data and Identifier Systems
As a legal foundation, trusted data, or a Source of Truth (SOT), is essential.
The development of identifiers, documents, and dictionaries is required.
ELI / ECLI: European standards for uniquely identifying legislation and case law
CELLAR: A semantic repository for all EU legislation
EU Vocabularies: An integrated foundation for terminology, classifications, and code lists
7.2 Semantic Consistency and Data Quality
From the perspective of AI safety, data integrity, consistency, and provenance are required. Furthermore, the FAIR-R principles (Findable, Accessible, Interoperable, Reusable, Reliable), which represent a new perspective on quality management in an AI society, can also be applied to legal data.
7.3 Ensuring Trust and Transparency
Blockchain signatures, Verifiable Credentials, and eIDAS2 guarantee the authenticity, tamper-resistance, and accountability of legal documents and contracts.
Chapter 8: Governance Transformation through LegalTech
8.1 Integration of Institutions, Technology, and Ethics
LegalTech enhances transparency across society by integrating governance, technology, and ethics. This is not a challenge that can be solved in isolation.
It is necessary for diverse stakeholders to cooperate and advance these efforts.
8.2 Transition to a Trust-Driven Society
The common element running through AI, data, and law is "Trust." In Europe, legal systems are being reconstructed as Trust-by-Design to create a society where AI is explainable and legally accountable.
Chapter 9: Conclusion: The Big Picture of a Digital Society
The essence of LegalTech is not a 'tool to make law more efficient,' but an 'infrastructure to redesign society'.
Its components are as follows.
Datafication (Law as Data): Development of semantic structures and reference data
Automation (Law as Code): Operation via AI and rule engines
Trustification (Law as Infrastructure): Guaranteeing transparency and accountability
Through this trinity structure, law evolves into an entity that is 'understood,' 'executed,' and 'trusted.' That is the new Social Contract of Trust for law in the AI era.