Legal AI Glossary

A machine learning technique where systems query humans for feedback on uncertain data. In e-discovery, active learning accelerates predictive coding by quickly improving accuracy in finding relevant documents.

Continuous AI scanning of contracts, vendors, and transactions for emerging risks. It helps legal teams catch issues before they escalate.

AI that proposes alternative contract language based on counterparty behavior and industry norms. Adaptive suggestions reduce friction in negotiations.

AI systems that act as autonomous agents, able to plan, reason, and take multi-step actions without continuous human input. In legal practice, agentic AI can execute workflows such as reviewing contracts, redrafting clauses, escalating risks, and updating playbooks in parallel, boosting efficiency and reducing manual oversight.

Computer systems simulating human reasoning, learning, and prediction. In law, AI powers applications from legal research to contract review and compliance monitoring.

A legal AI tool that identifies and standardises contract clauses (e.g. indemnity, liability caps). Commonly used in contract analysis and due diligence for law firms.

AI systems that track corporate activity, transactions, or communications to detect potential regulatory breaches.

AI that scans agreements to flag risks, omissions, and deviations from playbooks. Widely used in M&A due diligence and vendor management.

Systems that generate concise overviews of lengthy contracts, briefs, or case files. Speeds up review while keeping lawyers focused on key issues.

Automated detection and markup of risky or non-standard clauses against a playbook. Speeds negotiation while preserving party positions and audit trails.

Algorithms that assign risk levels to contracts, vendors, or matters based on clauses, obligations, and counterparty posture.

Using machine learning to suggest alternative clauses, highlight risks, and track counterparty moves during contract negotiations. Increases speed while maintaining legal oversight.

Discriminatory outcomes in AI caused by biased data or flawed design. In law, bias risks arise in hiring, sentencing, predictive policing, and compliance analytics.

A set of software rules that allows different applications to communicate and share data. In legal tech, APIs connect practice management software, e-discovery platforms, and AI tools to streamline workflows.

A legal doctrine protecting confidential communications between lawyers and clients. AI tools must be configured to safeguard privileged data during review and discovery.

The use of AI to automatically review contracts, invoices, or compliance records for accuracy and risk. Audit automation reduces manual effort, increases coverage, and provides defensible reporting in regulatory or litigation contexts.

Chronological records of every action taken in a system, such as edits, approvals, and data access. With AI, audit trails become richer and more reliable — automatically tagging risky changes, detecting anomalies, and generating defensible reports for litigation or regulatory compliance.

AI tools that flag changes and amendments across contract lifecycles. This ensures obligations remain clear after renegotiations.

AI that classifies clauses by type, obligation, and risk level. Automated tagging accelerates contract review and enables large-scale risk analysis.

AI-generated summaries of key risks across a portfolio of contracts. Useful for board reporting, audits, and transaction readiness.

AI that monitors signature progress across multiple stakeholders. It reduces delays in execution and provides real-time status updates.

AI systems tailored for banks and financial institutions, ensuring compliance with regulations like Basel III, AML, and KYC. They automate review of lending agreements, flag suspicious transactions, and generate defensible compliance reports.

Read more: Harnessing Luminance’s Legal-Grade™ AI: Empowering Finance Teams with Superior Financial Oversight

Comparing AI system performance against datasets or competitors. Legal departments use benchmarking to evaluate accuracy in review and analysis tools.

AI models whose inner logic is opaque to users. In law, they raise risks for explainability and admissibility.

The process of showing changes between versions of a contract. AI-powered blacklining highlights edits most likely to impact risk.

Distributed ledger technology ensuring secure, tamper-proof transactions. For legal purposes, it supports smart contracts, digital evidence authentication, and intellectual property rights management.

Mandatory communications following a data breach. Automation ensures timeliness and compliance with regulations like GDPR.

The review of thousands of contracts simultaneously, enabled by AI-driven search and extraction. Bulk review allows legal teams to identify risks, obligations, or trends across an entire portfolio without line-by-line manual work.

Automated removal of sensitive or privileged content across large data sets. AI-driven redaction reduces risk of human error in litigation and compliance.

AI analyzing judicial opinions to identify trends, precedent strength, and judge behavior. Lawyers use these insights to refine litigation strategy.

Platforms centralizing case documents, deadlines, billing, and communications. AI features automate classification, alerts, and workflow tracking.

A technique where AI models generate intermediate reasoning steps before producing a final answer. Chain-of-thought improves transparency in how conclusions (e.g., risk scores or case predictions) are reached.

Conversational tools for client intake, FAQs, and form assistance. They improve efficiency and access, though they do not substitute for legal advice.

Read more: Product Feature Spotlight: Answer any Contract Query with Lumi Pro, The Google of Contracts

A generative AI tool used in law for drafting, summarizing, and research. As this is a generalist model, lawyers must validate outputs for accuracy and ethical compliance.

AI that reviews and monitors contracts across research, sourcing, and production in chemical companies. It ensures adherence to evolving safety and environmental regulations, while tracking obligations to improve quality, compliance, and market competitiveness.

The comparison of a clause against internal playbooks or external market standards. It helps reviewers judge whether language is aggressive, unusual, or acceptable and guides redlines during negotiation.

Flags departures from preferred or market-standard language. Supports consistent negotiation and risk scoring.

A visual AI tool that highlights negotiation pressure points across multiple contracts. Heatmaps give legal teams instant insight into risk-heavy terms.

A curated set of pre-approved contract provisions. AI systems pull from the library to suggest standard language during drafting and review.

AI scoring how flexible a clause is based on negotiation history. Scoring helps predict which terms are deal-breakers.

Tools that detect near-duplicate or semantically similar clauses across agreements.

Platforms managing contract drafting, negotiation, compliance, and renewal. AI adds clause libraries, risk alerts, and renewal tracking.

Structured lists of regulatory requirements applied to business operations. AI enhances them with dynamic updates and monitoring.

Automated systems tracking corporate activity for regulatory breaches. AI flags anomalies in transactions, contracts, and employee communications.

Secure, isolated AI environments where sensitive client data can be processed without exposure to external systems.

Technology that monitors NDAs and other obligations across portfolios. Tracking prevents inadvertent disclosures of sensitive data.

Contracts restricting disclosure of sensitive information. AI tools can speed up review by highlighting unusual or risky clauses.

Generative AI that adapts clause drafting not only to contract type but also to jurisdiction, counterparty, and industry norms.

AI suggesting edits not just based on text but also negotiation history and party preferences. This increases speed while aligning outcomes with risk posture.

AI extracting key data points (like governing law, renewal dates, or payment terms) from contracts. Supports faster reporting and compliance monitoring.

Applying AI to review executed contracts and extract obligations, risks, and key data points. Contract analysis enables portfolio-wide visibility, compliance reporting, and faster responses in audits, M&A, or regulatory investigations.

Shared workspaces where AI supports multi-party negotiation. Collaboration tools reduce email back-and-forth and improve transparency.

Detects textual changes between drafts to surface risk-relevant edits. Essential in high-velocity deal cycles.

The use of AI and clause libraries to generate first drafts of agreements. AI drafting tools accelerate contract creation, standardize language, and reduce risk, while lawyers refine the drafts for accuracy and negotiation strategy.

End-to-end system that uses AI to analyze, negotiate, and monitor contracts. Distinct from CLMs by focusing on insight, not just workflow.

AI tools that support and speed up negotiation by redlining clauses, suggesting fallbacks, and tracking deviations from playbooks.

The use of AI to analyze contracts for risks, missing clauses, and compliance with playbooks or regulations. Speeds up review and gives lawyers structured insights without manual page-by-page analysis.

Visual AI indicators highlighting clauses with the highest risk impact. Heatmaps help lawyers focus attention where it matters most.

Model-driven scoring of agreements based on clause strength, omissions, and party posture. Helps triage reviews and escalate high-risk deals.

An AI assistant embedded within legal workflows that helps lawyers review, draft, or analyze documents in real time.

Assessing risk based on a counterparty’s past negotiation stances, contract terms, and financial health. Supports procurement and vendor management.

Removing identifiers from datasets to protect privacy. It enables AI analysis without breaching confidentiality obligations.

Unauthorized access to sensitive data requiring legal response. AI assists in detection, response, and compliance reporting.

The use of AI to pull structured information from unstructured legal data like contracts, emails, and discovery sets. In practice, data extraction enables fast clause identification, compliance checks, and due diligence across large document repositories.

Tagging documents or clauses to train AI models. Accurate labeling improves contract review and e-discovery systems.

AI handling data subject requests under GDPR, CCPA, or other privacy laws. It reduces manual workload and ensures timeliness.

Practices protecting sensitive legal information from unauthorized use. Includes encryption, access control, and AI-driven monitoring.

The principle that data is subject to the laws of the country where it is stored. Legal teams must design systems around sovereignty rules.

Rights to access, correct, or delete personal data. Legal tech helps businesses respond to state privacy law requests.

Reducing contract turnaround time using AI redlining, playbooks, and automated workflows. A key selling point for in-house counsel and sales teams.

AI-supported function that speeds approval workflows in high-volume contracting environments. Ensures legal stays engaged without blocking sales.

AI combining analytics and automation to guide decision-making. Legal departments use it for compliance and risk management.

AI using layered neural networks to analyze complex data. It powers contract classification, translation, and speech-to-text.

The process of detecting differences between a contract and an organization’s preferred standards or market norms. AI highlights these deviations so negotiators can prioritize risky or non-standard terms for review.

Systems that track what information has been disclosed during litigation. They reduce risks of privilege waiver and incomplete discovery

The process of reviewing electronic records for litigation. AI accelerates relevance filtering, privilege detection, and predictive coding.

Software generating contracts, pleadings, or forms from templates. AI improves customization and reduces drafting errors.

AI grouping documents by similarity. Clustering is widely used in e-discovery and regulatory reviews.

AI confirming that contracts and filings are unaltered. Verification prevents fraud and ensures trust.

Platforms organizing and securing legal documents. AI features enable smart search, tagging, and compliance tracking.

Comprehensive assessments of risks during M&A or vendor onboarding. Automation accelerates report generation and improves accuracy.

AI that continuously updates a contract playbook based on market trends and negotiation history. Keeps standards current without manual updates

Platforms managing legal invoices and spend. AI flags errors, inefficiencies, and non-compliance with billing guidelines.

Collecting, reviewing, and producing electronically stored information for litigation. AI accelerates relevance review and privilege checks.

Digital signatures legally binding under US law. They streamline contract execution while ensuring compliance.

Encoding data to prevent unauthorized access. Encryption is essential for cybersecurity and client confidentiality.

A single system that manages the full contract lifecycle or discovery process, from document ingestion through review, negotiation, analysis, and compliance monitoring

AI for handling contracts in oil, gas, and renewables, where regulatory compliance is complex. It monitors obligations, flags environmental risks, and supports negotiations for large infrastructure projects.

An organization-wide review that spans financial, legal, compliance, and risk domains. Enterprise audits are increasingly supported by AI, which accelerates analysis across contracts, policies, and systems.

Platforms integrating matter, billing, compliance, and reporting functions. AI enhances ELM with predictive analytics.

AI identifying names, clauses, and terms in text. NER underpins contract review, compliance, and e-discovery.

AI spotting auto-renewal clauses that could trap value. Monitoring ensures proactive renegotiation.

The ability to understand how an AI model reached its output. In legal contexts, explainability is critical for regulatory compliance, bias detection, and courtroom admissibility.

Harmonic mean of precision and recall used to assess review quality. Common in TAR and clause extraction benchmarking.

Pre-approved alternative provisions offered when counterparties reject preferred language. Critical for fast turnaround and risk alignment.

AI tailored for banks and financial institutions to manage regulatory compliance, lending agreements, and transactional risk. It detects exposure to sanctions, flags non-compliant terms, and enhances governance by surfacing obligations and anomalies across portfolios.

Read more: Harnessing Luminance’s Legal-Grade™ AI: Empowering Finance Teams with Superior Financial Oversight

AI that generates initial drafts of contracts, NDAs, or other legal documents from pre-approved templates and clause libraries. It accelerates drafting cycles and enforces consistency with company standards.

AI-driven analysis of contracts to assess exposure during unforeseen events. This became critical during the COVID-19 pandemic.

AI that flags anomalies in financial or transactional data. Legal teams use it for compliance and risk management.

EU data privacy law with global reach. US companies with EU data must comply, often with AI tools supporting compliance.

Read more: Six Years of GDPR: The Importance of Compliance and How Legal-Grade™ AI Can Help

AI that creates text, images, or media. In law, it drafts contracts, summarizes documents, and produces client communications.

Attorney-approved master templates and clause sets used in AI contract drafting to ensure consistency and compliance.

Policies and procedures regulating technology use in legal teams. Frameworks ensure accountability and risk reduction.

Forcing AI outputs to cite retrieved sources. In legal practice, grounding improves trust, auditability, and defensibility by tying summaries or redrafts to verifiable documents.

When AI produces plausible but false information. In legal practice, hallucinations raise risks in research and drafting.

AI safeguards that detect and suppress fabricated or non-verifiable legal content during drafting and research.

AI spotting clauses that trigger obligations years in the future. It prevents missed renewals, payments, or compliance deadlines.

Attorney validation within AI workflows. Balances speed with accuracy and ethical responsibility.

Combining AI, robotic process automation, and analytics. In legal ops, it accelerates workflows like billing and matter management.

The process of confirming a signer’s or participant’s identity. AI strengthens verification in e-signatures and virtual hearings.

Policies managing how organizations handle data lifecycle. In law, it ensures compliance, defensible deletion, and discovery readiness.

AI techniques for finding relevant information in large datasets. Core to e-discovery and legal research systems.

AI tools that streamline policy drafting, risk evaluation, and claims review. They detect non-standard terms, monitor regulatory alignment, and help insurers resolve disputes more efficiently.

Read more: How Are Insurers Using Luminance’s Legal-Grade™ AI to Stay Ahead?

AI extracting and classifying information from documents. IDP supports contract analysis, compliance, and litigation workflows.

Ensuring different AI systems like CLM, billing, and discovery tools share data securely. Interoperability reduces silos and increases workflow efficiency.

AI tools which can analyze contracts or case law across multiple legal systems without retraining, meaning clients can apply one platform consistently while still adapting outputs to local laws and regulatory requirements.

Centralized repositories of legal insights and precedents. AI improves them by tagging, searching, and recommending content.

Pulling structured knowledge from unstructured contract text. Powers dashboards, analytics, and compliance monitoring.

AI structures mapping relationships between legal entities, cases, and clauses. They power advanced search and due diligence in complex matters.

Systems for capturing and reusing institutional legal knowledge. AI improves KM by tagging, classifying, and recommending relevant precedents.

AI systems that can process, analyze, and generate outputs across multiple languages, enabling consistent contract review and compliance monitoring across jurisdictions, and reducing the need for retraining or translation.

AI models trained on massive text corpora to generate or analyze language. In law, LLMs support drafting, summarizing, and research tasks.

A comprehensive review of an organization’s contracts, policies, and operations, used by in-house counsel to ensure regulatory compliance and risk visibility.

Software generating contracts, NDAs, and pleadings from templates. AI personalization ensures documents reflect unique deal or case requirements.

AI platforms that allow legal teams to build and customize workflows with minimal coding. They strike a balance between flexibility and ease of use, enabling faster deployment of tailored contract, compliance, or discovery solutions.

A subset of AI where algorithms learn patterns from data to make predictions. In law, ML powers e-discovery, analytics, and compliance tools.

Outsourced large-scale document review often supported by AI. Review teams use predictive coding to accelerate relevance and privilege assessments.

AI designed to manage supply chain contracts, vendor agreements, and regulatory obligations in the manufacturing sector. It helps companies track disruptions, standardize global contracting, and respond rapidly to ESG and compliance requirements.

Read more: How are Manufacturers Using Luminance’s Legal-Grade AI?

Clauses that align with industry norms. AI compares draft terms against benchmarks to highlight aggressive or unusual language.

Visual interfaces that consolidate information on a legal matter. Dashboards provide KPIs, risks, and deadlines at a glance.

Platforms organizing all details of legal matters, including documents, spend, and deadlines. AI adds automation and reporting features

AI ranking legal matters by urgency and risk. Prioritization helps allocate resources effectively.

Hidden file information like authorship, edits, and timestamps. Metadata is critical in contract repository use, e-discovery and authenticity verification.

A plug-in that embeds AI tools directly within Microsoft Word to support contract drafting, redlining, and review. It allows lawyers to apply playbooks, generate clause alternatives, and automate risk checks without leaving their drafting environment.

An AI architecture where multiple specialized models (“experts”) are combined, and only the most relevant expert is activated per query. In legal AI, MoE approaches improve efficiency by routing tasks like clause extraction or risk scoring to the best-suited model.

Controls preventing unsafe outputs, privilege leaks, or policy violations. Includes filters, grounding, and allow/deny lists.

Continuous evaluation of AI accuracy and fairness. Monitoring ensures systems remain compliant and defensible.

Frameworks for monitoring the reliability and fairness of AI models. Legal departments use it to reduce compliance and reputational risks.

Cross-language clause detection and translation assistance. Supports global portfolios without losing legal nuance.

AI that identifies parties, dates, and clauses in text. It underpins contract analysis and discovery review.

AI that interprets and generates human language. NLP powers legal chatbots, contract review, and research tools.

AI-powered search that lets users query legal documents in plain English instead of Boolean operators. In law, natural language search improves contract review, e-discovery, and research by retrieving relevant clauses, cases, or obligations from vast data sets quickly and intuitively.

AI systems that generate, review and approve nondisclosure agreements. They flag deviations from standard clauses to reduce legal risk.

AI tracking past negotiation outcomes and counterparty behaviors. Helps predict likely concessions and optimal strategies.

Step-by-step guides that structure the review of contracts around a company’s core negotiating positions. AI-powered checklists highlight deviations, suggest fallbacks, and ensure negotiators focus on the most material risks and opportunities.

Read more: Product Feature Spotlight: Accelerate Negotiations with Luminance’s AI-Powered Checklists

A real-time assistant that guides lawyers during negotiation with fallback suggestions and risk ratings.

AI platforms for legal teams that require no programming knowledge, using drag-and-drop tools to configure workflows. This allows lawyers and business users to build automation for review, approvals, and compliance checks without IT support.

AI tools that identify key duties from agreements. Extractors reduce missed obligations and compliance failures.

Tracking contractual duties, deadlines, notices, and SLAs after signature. AI extracts obligations and triggers reminders.

Visual tools that display extracted obligations across contracts. AI provides alerts and KPI tracking for compliance.

Technology converting scanned documents into searchable text. OCR is foundational for e-discovery and contract digitization.

A top-to-bottom assessment of how efficiently a legal or business function operates. Operational audits examine processes, technology, and people to highlight gaps and opportunities for improvement.

OCR and layout AI to convert scans into structured, searchable contracts. Enables portfolio analytics and migration to CLM.

AI tools that handle administrative tasks traditionally performed by paralegals. They include document review, scheduling, and drafting assistance.

AI that analyzes clinical trial agreements, research contracts, and licensing terms. It accelerates compliance checks with FDA/EMA requirements and ensures data privacy obligations are upheld.

Read more: Supercharging Pharmaceutical Operations with Legal-Grade™ AI: Insights from Anna Rawlence, VP of Product and Customer Experience at Luminance

AI that applies pre-approved negotiation playbooks directly to contracts. It automatically flags deviations, inserts fallback clauses, and aligns language with company standards.

AI that updates negotiation playbooks automatically by learning from closed deals, counterparty positions, and regulatory changes.

Applying attorney-approved fallbacks and standards automatically. Reduces variance while preserving attorney oversight.

A narrowly focused legal technology tool designed to solve one specific problem, such as e-billing, redaction, or NDA review.

AI surfacing relevant past cases from archives. Mining strengthens legal arguments and increases efficiency.

A TAR method where AI learns from attorney-coded samples to classify documents. It accelerates relevance review in e-discovery.

Using AI and RPA to streamline repetitive tasks. This improves efficiency in billing, compliance, and contract workflows.

AI that streamlines supplier contracting, vendor due diligence, and compliance checks during procurement. It flags risky clauses, automates approval workflows, and ensures alignment with company policies and regulatory standards.

Read more: How Are Procurement Teams Leveraging Luminance’s Legal-Grade™ AI to Maximize Momentum?

Techniques for tailoring LLM prompts to ensure accurate, compliant outputs.

AI tools for government and public entities that manage procurement, grants, and regulatory compliance. They improve transparency, automate tender reviews, and ensure adherence to public contracting laws.

Applying statistics and AI to predict legal outcomes. It combines historical data with machine learning for risk forecasting.

An AI technique combining search with generative models. In legal AI, RAG ensures outputs are grounded in verified sources.

AI that reviews leases, property sale contracts, and financing agreements. It flags unusual rent escalations, renewal clauses, or zoning restrictions, speeding up deal cycles.

Tools that remove sensitive or privileged information from documents. AI improves accuracy and speeds large-scale redaction.

Systems that log edits between drafts of agreements. Tracking ensures accountability and reduces negotiation disputes.

AI-driven monitoring of regulations across jurisdictions. It alerts businesses to changes and ensures ongoing compliance.

Centralized views of evolving regulations by geography or industry. Dashboards allow real-time compliance monitoring.

AI predicting upcoming legal and regulatory changes across jurisdictions.

Monitoring renewal windows and evergreen provisions, to prevent value leakage and unwanted extensions.

AI that supports procurement and supplier contract management at scale. It identifies risky terms, enforces consistent pricing/discount clauses, and monitors ESG commitments in the supply chain.

Assigning risk levels to contracts, clients, or matters using AI analysis. Risk scoring helps prioritize review and mitigation.

Software robots automating repetitive, rule-based tasks. RPA in law streamlines billing, data entry, and document processing.

Software-as-a-Service delivery of legal technology via the cloud. SaaS models lower costs and enable rapid deployment.

Compliance process to check counterparties against sanctions lists. Automation ensures timely detection and reporting.

Systems enabling business users to generate contracts via guided AI workflows. Reduces reliance on legal for standard agreements.

Finds meaning-equivalent terms beyond exact keywords. Critical for variant clause discovery and precedent hunting.

Self-executing agreements coded on blockchain. Used in finance, supply chain, and IP licensing.

Narrowly focused AI models designed for a specific domain such as contract review, e-discovery, or compliance monitoring. Unlike general-purpose models, specialist AI achieves higher accuracy on legal tasks because it is trained on domain-specific data.

Organized data like spreadsheets or databases. AI handles structured data more easily than unstructured legal text.

AI trained on labeled legal data such as clauses or case outcomes. Supervised learning powers contract review, discovery, and analytics.

AI that monitors supplier contracts, logistics agreements, and global trade obligations for compliance and risk, helping legal teams track ESG commitments, detect sanctions or embargo violations, and ensure continuity across complex international supply chains.

Artificially generated datasets used to train AI. In law, it enables AI development without breaching client confidentiality.

AI-assisted document review in e-discovery. TAR reduces costs by prioritizing relevant files.

AI that supports fast-moving technology companies in contract management, compliance, and commercial transactions. It ensures cross-border agreements align with evolving regulations, accelerates deal cycles, and provides insights into revenue opportunities and risks.

AI designed for telecom operators to manage licensing, cross- border data transfers, and spectrum agreements. It helps ensure compliance with GDPR, CCPA, and telecom-specific regulations.

AI analyzing large volumes of legal text for patterns and insights. Applications include compliance monitoring and case law research.

Ongoing AI screening of vendors and partners for sanctions, compliance, or reputational issues.

The practice of offering discrete legal tasks (such as contract review, discovery, or drafting) instead of full-service representation. AI enables firms and legal departments to deliver unbundled services efficiently by automating repetitive components while lawyers focus on high-value work.

Data without predefined format, such as emails or PDFs. Legal AI tools excel at extracting meaning from unstructured text.

AI clustering documents or clauses without labels. Unsupervised learning enables fast discovery and contract classification.

Secure online repositories for M&A due diligence. AI features support contract search, redaction, and risk analysis.

Embedding identifiers in AI-generated legal text to verify authorship and authenticity.

Automated extraction of public data from websites. AI scrapers assist in monitoring regulations and case developments.

The coordination of multiple legal processes and automation tools into a unified system. This ensures tasks like contract review, billing, and compliance checks flow seamlessly between teams, reducing delays and errors.

A cybersecurity model requiring continuous verification of all users. Law firms adopt zero trust to protect sensitive client data.