THE AUTHOR:
Annie Lespérance, Head of Americas at Jus Mundi
Everyone in law is talking about AI: new tools, smarter models, bigger promises. But behind all the noise, a more decisive race is unfolding: the competition for high-quality, structured legal data.
The truth is, even the most advanced AI models are only as good as the data they learn from, and in law, that data is uniquely complex, fragmented, and multilingual. In international arbitration, especially where cases span jurisdictions, languages, and procedural frameworks, the ability to access, understand, and reason over that data has become a true competitive edge.
In a recent white paper with Stanford CodeX, we explored exactly this problem: why general-purpose AI tools struggle in arbitration and what’s needed to build systems that actually think and reason like arbitration practitioners.
This article takes that conversation further by examining how the “data wars” are reshaping legal research. We’ll look at why more data doesn’t always mean better AI, what makes arbitration such a unique proving ground for specialized systems, and how you can use structured intelligence to move from information overload to real strategic advantage.
The Arbitration Data Problem
Most legal AI systems train on publicly available data: published case law, statutes, and general legal content. What’s available to one system is available to all. This creates basic competency but zero differentiation.
International arbitration faces a particularly critical challenge. Unlike domestic legal systems with systematic publication, arbitration operates across a fragmented landscape:
- Awards aren’t systematically published: While some awards are published, some remain confidential or unpublished. The precedents that could inform your case strategy may exist, but may be inaccessible.
- Institutional rules vary across 100+ organizations: Understanding how ICC Rules differ from SIAC Rules, or how the 2021 ICC Rules changed from 2017, requires comprehensive access to institutional materials and their evolution.
- Multilingual sources fragment knowledge: Critical awards exist in French, Spanish, Mandarin, and other languages. Academic commentary appears across languages and jurisdictions. Arbitration requires not just translation, but an understanding of legal concepts across linguistic contexts and nuances.
- Procedural frameworks scatter across sources: IBA Rules, UNCITRAL Guidelines, and similar resources aren’t systematically indexed or cross-referenced with the case law applying them.
Missing one critical precedent, such as one tribunal’s jurisdictional interpretation or one institution’s procedural guidance, can determine outcomes worth millions.
From Raw Data to Usable Intelligence
But having a massive database isn’t the same as having useful information. Legal AI needs data that’s organized and connected, where the relationships between sources matter just as much as the sources themselves.
Good data organization means knowing which sources are binding law versus helpful guidelines. It means tracking so systems know whether to apply ICC Rules 2021 or 2017 based on when arbitration was initiated. It means connecting related authorities so when one award cites a specific treaty provision, the system knows to look at other awards interpreting that same provision.
This kind of organization requires both legal expertise and technical know-how, and it gets more valuable over time. A well-organized arbitration database, like Jus Mundi, built through institutional partnerships, helps you find exactly what you need, combine information from different sources more accurately, avoid AI hallucinations, and complete research faster with more confidence.
Why Good Data Needs the Right AI Architecture
Here’s what most legal AI tools won’t tell you: even with the world’s best arbitration database, results vary dramatically based on how the AI actually works.
Take a comprehensive arbitration database: every available award, all institutional rules, academic commentary, everything properly organized. Now give that exact same database to two different AI systems:
System One uses standard RAG (Retrieval-Augmented Generation) architecture, the approach most legal AI tools employ. It retrieves documents quickly based on query relevance, feeds them into the AI context window, and generates well-formatted answers. The reasoning process remains hidden, a black box that practitioners must trust or verify through manual review.
But, ask this system about IBA Rules in document production disputes, and problems emerge. It might cite the outdated 2010 version instead of the current 2020 one. It could present IBA Rules as binding law when they’re actually optional guidelines. It might miss that specific institutional rules often override IBA provisions, or ignore how different tribunals apply these frameworks differently.
These aren’t rare glitches; they’re built-in limitations. The system treats all sources as equally important, can’t distinguish binding rules from helpful guidelines, and doesn’t follow the logical structure lawyers use to analyze problems.
System Two, Jus Mundi, uses agentic architecture specifically designed for arbitration workflows. Before generating any answer, it analyzes what the query actually requires. It plans a research approach, determining which institutional rules apply, what case law would be relevant, and whether academic commentary would add necessary context. Then, it reflects on its own findings, identifying any gaps in reasoning or missing sources, and decides whether to refine its plan, conduct additional research, or move forward to an answer. It executes this plan through coordinated research streams, synthesizes findings using legal reasoning methodology, and maintains transparency at every step so practitioners can verify the analytical process.
When this system addresses IBA Rules questions, it recognizes these are guidelines rather than binding provisions. It identifies which version applies and notes when rules changed. It understands that Article 3(2) of the ICC Rules grants tribunals discretion that may override IBA recommendations. It can synthesize across awards to show how different tribunals apply IBA frameworks in practice, noting patterns and variations.
Why does this matter so much? Because architecture determines what questions AI can actually answer, with the level of quality required by legal professionals, not just how fast. It affects whether you can trust the results, not just whether they look good. It makes the difference between sophisticated legal analysis and impressive-sounding summaries.
This is why Jus AI represents a fundamental architectural shift rather than an incremental improvement. The system thinks, plans, reflects, and executes research like arbitration practitioners do because it follows the same reasoning methodology that produces reliable legal analysis. Every research step remains transparent, enabling verification that generic systems’ black-box approaches cannot provide.
The Security Dimension Underlying Data & Architecture
There’s a fundamental tension at the heart of legal AI: AI systems improve through data access, but legal practice demands absolute confidentiality. How organizations resolve this tension determines their competitive positioning in ways that extend far beyond compliance.
Most consumer AI uses your questions and responses as training data to improve future performance. For legal practice, this creates catastrophic risk. Attorney-client privilege, work product doctrine, and basic confidentiality obligations make data aggregation fundamentally incompatible with professional responsibility. Arbitration adds another layer: parties choose this forum specifically for confidentiality. Using AI that might train on their sensitive commercial disputes defeats the entire point.
That’s why certifications matter. An arbitration involving trade secrets worth hundreds of millions demands AI systems built for fortress-level protection: consumer-grade safeguards simply don’t suffice.
ISO 27001 sets the global benchmark for information security management, confirming that strict controls govern how sensitive data is stored, accessed, and shared. ISO 42001, the first international certification for AI management systems (and one Jus Mundi was the first arbitration AI company to achieve) ensures that every AI process meets standards for transparency, traceability, and ethical governance. Finally, SOC 2 Type I compliance validates that our infrastructure upholds rigorous principles of privacy, availability, and confidentiality.
Strategic Imperatives for Technology Leaders & Arbitration Practitioners
The legal AI landscape is fragmenting into two distinct tiers: generic systems built for broad legal markets, and specialized systems built for specific practice areas. Understanding what questions to ask determines whether your firm truly gains a strategic advantage or just gradual efficiency.
Effective evaluation requires a framework that goes beyond surface-level features:
- Data Access: Don’t accept vague claims about “comprehensive databases.” Ask specifically: what institutional partnerships provide exclusive data? What coverage exists across jurisdictions and languages? How current is the database; are updates systematic or sporadic? Can the vendor demonstrate specific sources competitors lack?
- Data Curation: Raw data volume matters less than structure and context. How is information organized to reflect legal hierarchies? Does the system track rule versions and effective dates? Are cross-references between authorities built into the database structure? What legal expertise informed the curation process?
- Control: True confidence comes from knowing exactly where an AI’s answers come from. Can you choose or limit the sources the system relies on? Does it clearly show which materials were used in generating a response? Can you exclude certain datasets (for example, non-authoritative or secondary commentary) when needed? Does the system allow you to trace every citation back to its origin within the database? And most importantly: does the AI ever draw on unknown or external sources beyond your control?
- AI Architecture: This separates leaders from followers. Can the system distinguish between binding rules and persuasive guidelines? Does it follow legal reasoning methodology or just retrieve and summarize? Is the reasoning process transparent and verifiable? How does it handle complex queries requiring synthesis across source types?
- Security Standards: what specific certifications has the vendor achieved: not just claims, but independently audited standards? What are explicit data retention policies? Where is data stored and under what regulatory framework? How are confidentiality and privilege protected?
- Transparency and Verification: Can practitioners see how the AI reached conclusions? Are citations granular and verifiable? Does the system acknowledge uncertainty when appropriate? What quality controls prevent hallucinations?
The Path Forward
The legal AI landscape continues to evolve rapidly, but certain strategic realities are becoming clear. Generic solutions may improve incrementally as models advance, but they can’t replicate the compound advantages that come from arbitration-specific systems with exclusive data access and specialized architecture.
This is where Jus Mundi comes in.
Jus Mundi combines the world’s most comprehensive arbitration database with an AI architecture built specifically for arbitration workflows, creating the specialized data and technology foundation that sustainable competitive advantage requires.
The Data Foundation: Through partnerships with over 100 arbitral institutions, including the ICC, AAA-ICDR, ICSID, SCC, SIAC, Jus Mundi provides access to arbitration awards, institutional rules, treaties, and expert commentary across 90+ countries. This is legally curated information structured to reflect authority hierarchies, procedural relationships, and the contextual nuances that arbitration practitioners need.
The AI Architecture: Jus AI represents the first agentic AI system designed specifically for arbitration research. Rather than using generic retrieval-and-generate approaches, the system thinks, plans, and executes research following arbitration reasoning methodology. Complete transparency at every research stage enables verification that black-box systems cannot provide. Dual-layer quality validation (combining AI-powered evaluation with expert human review) ensures professional-grade reliability.
The Security Commitment: Jus Mundi holds ISO 27001 certification for information security management, ISO 42001 certification for AI management systems (the first legal tech company to achieve this standard), and SOC 2 Type I compliance for service organizations. Strict non-retention policies ensure your data never trains models or gets shared with other users.
For firms serious about gaining competitive advantage from legal AI rather than incremental efficiency, this combination of exclusive data access, specialized architecture, and professional-grade security creates differentiation that generic providers cannot match. Explore the platform and discover how Jus Mundi’s exclusive database and Jus AI’s arbitration-first, agentic AI can empower your team.
About Jus Mundi
Founded in 2019 and recognized as a mission-led company, Jus Mundi is a pioneer in the legal technology industry dedicated to powering global justice through artificial intelligence. Headquartered in Paris, with additional offices in New York, London, and Singapore. Jus Mundi serves over 150,000 users from law firms, multinational corporations, governmental bodies, and academic institutions in more than 80 countries. Through its proprietary AI technology, Jus Mundi provides global legal intelligence, data-driven arbitration professional selection, and business development services.
Press Contact Helene Maïo, Senior Digital Marketing Manager, Jus Mundi – [email protected]
*The views and opinions expressed by authors are theirs and do not necessarily reflect those of their organizations, employers, or Daily Jus, Jus Mundi, or Jus Connect.
