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Discover how our audit modules ensure measurable compliance, strong AI governance, and actionable insights for your organization.
Evaluates the legal logic, argumentative structure, and deductive capacity of an AI model.
Objective:
Assess the AI’s ability to perform logical reasoning and make informed decisions based on complex data and scenarios.
Key Use Case:
Verification of a model’s capacity to make accurate logical deductions or inferences.
Example:
An AI assistant used to advise a client on a complex legal matter. The AI must be able to understand the relationships between multiple pieces of information to deliver relevant and coherent guidance.
Use Cases:
Competitions, multiple-choice exams, case studies, and educational simulations.
Tests conversational consistency across multiple exchanges.
Objective:
Test the AI’s ability to maintain coherence and relevance throughout multiple exchanges with the user.
Key Use Case:
Verification of conversational fluency and consistency in scenarios where the AI interacts repeatedly with the user.
Example:
An HR chatbot answering questions about employee benefits. After a series of inquiries, the AI must maintain consistent and non-contradictory answers, even as the context gradually evolves.
Use Cases:
HR chatbots, legal support systems, contractual dialogues.
Simulates a real case with progressive steps and dynamic interactions.
Objective:
Assess the AI’s ability to manage complex and dynamic scenarios by testing its responsiveness to specific predefined situations.
Key Use Case:
Evaluate how the AI reacts when additional or contradictory information is introduced during an interaction.
Example:
A virtual customer service agent is tested on its behavior when a client changes their request or provides new information during the conversation.
Use Cases:
Dismissal procedures, employee assistance, formal notices.
Compares a model’s responses to two opposing viewpoints.
Objective:
Analyze the AI’s ability to handle contradictory situations while maintaining logical consistency and avoiding incoherent or biased responses.
Key Use Case:
Test how the AI reacts when faced with opposing statements within the same session.
Example:
Evaluate a legal assistant responding to contradictory questions from a client and determine whether the AI can logically clarify inconsistencies.
Use Cases:
Litigation, arbitration, structured debate.
Submits “trap” prompts to detect regulatory weaknesses or dangerous biases.
Objective:
Verify the AI’s resilience to adversarial attacks, where modified inputs may cause the model to make incorrect or unsafe decisions.
Key Use Case:
Identify vulnerabilities in AI systems and test their robustness against malicious manipulations.
Example:
A recommendation model is tested by simulating altered inputs to check whether the AI suggests an incorrect or biased product to a user.
Use Cases:
GDPR compliance, manipulation, disinformation.
Evaluates neutrality and the absence of sensitive bias (gender, origin, social situation).
Objective:
Analyze whether the AI model exhibits ethical or discriminatory bias and test its ability to ensure fair and equitable decision-making.
Key Use Case:
Ensure that the AI makes ethical decisions and does not reinforce stereotypes or prejudices.
Example:
An AI recruitment system is evaluated to determine whether it discriminates against certain candidates based on gender, age, or ethnic origin.
Use Cases:
Criminal law, labor law, discrimination, fundamental rights.
Verifies the reliability of legal foundations: cited laws, doctrine, case law, and compliance with positive law.
Objective:
Evaluate the origin and accuracy of the data on which the AI bases its reasoning to ensure they are reliable and transparent.
Key Use Case:
Test the transparency and legitimacy of the AI’s data sources to confirm their authenticity and reliability.
Example:
A predictive AI model analyzing court decisions is tested using legal syllogism to verify that the data used come from verifiable and legitimate sources.
Use Cases:
Generated documents, legal opinions, validation of legal reasoning, syllogism, specific case analysis.
Compares two models or two versions of the same prompt to assess their relevance and clarity.
Objective:
Compare the performance of different AI models to determine which offers the best compliance level or lowest risk exposure.
Key Use Case:
Conduct A/B tests to evaluate which model produces the most ethical, reliable, and compliant results.
Example:
Two customer service AI models are compared — one using a traditional conversational approach, the other using advanced behavioral analysis — to determine which is more ethical and effective.
Use Cases:
LLM selection, technological benchmarking.
Assesses the model’s ability to respond accurately even when language inputs are degraded or imprecise.
Objective:
Test the model’s capacity to remain performant and reliable under extreme or unexpected conditions.
Key Use Case:
Evaluate how the AI reacts when input data are flawed, noisy, or intentionally disrupted.
Example:
An online demand forecasting model is tested for its ability to handle data entry errors or sudden fluctuations caused by new legal texts or jurisprudential updates.
Use Cases:
Non-lawyer users, accessibility, digital inclusion.
Verifies whether the model accounts for legal developments in laws, case law, and regulations.
Objective:
Evaluate the AI’s performance and reliability over time, particularly its adaptability to evolving contexts.
Key Use Case:
Test whether the AI remains coherent and accurate as data or legal frameworks evolve.
Example:
An AI assistant in a legal aid call center is tested to ensure it remains effective in handling client inquiries even as laws or procedures change over time.
Use Cases:
New legislation, procedural deadlines, legal reforms.
Evaluates the consistency between two AI responses to the same question and verifies compliance against a legal corpus.
Objective:
Assess the coherence between two AI-generated answers to the same question. Identify semantic, reasoning, or tonal divergences and detect critical weaknesses.
Key Use Case:
Compare two AI models (or the same model at different times) on the same task to:
Detect unstable or inconsistent behavior
Identify reasoning errors or biases
Qualify legal, factual, or stylistic discrepancies
Prioritize cases for manual review
Example:
Two AI responses to the question:
“Can an employee be dismissed for gross misconduct without a preliminary hearing?”
AI-1: Yes, in cases of flagrant misconduct.
AI-2: No, a hearing is always mandatory.
→ The module flags a significant disagreement and recommends human review, specifying whether the conflict is legal, factual, or tonal.
Use Cases:
Law firms & compliance teams (AI robustness audits)
Public institutions (regulatory consistency checks)
EdTech / training platforms (critical reasoning practice)
R&D departments (validation of internal generative agents)
Discover how our modules test, validate, and secure AI systems for organizations that demand robustness, ethics, and regulatory compliance.
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