AI Risk Assessment: Definition and Examples

AI Risk Assessment is the systematic process of identifying, analyzing, and evaluating potential risks associated with the development, deployment, and use of artificial intelligence systems.

Full definition

AI Risk Assessment refers to the set of methods and practices aimed at anticipating, identifying, and quantifying potential dangers associated with AI systems. This process covers technical risks (algorithmic bias, hallucinations, security flaws) as well as ethical, legal, and societal risks (discrimination, privacy violations, manipulation).

This approach is part of a broader AI governance framework. It involves assessing each stage of an AI system's lifecycle: collection and processing of training data, model design, deployment in production, and use by end users. The goal is to map possible failure scenarios and estimate their probability and impact.

In prompt engineering, AI Risk Assessment takes on a particular dimension. Each prompt sent to a language model can generate inaccurate, biased, or potentially harmful responses. Assessing these risks in advance allows for designing more robust prompts, integrating guardrails, and defining verification protocols for model outputs.

With the entry into force of regulations like the European AI Act, AI risk assessment has become a legal obligation for many organizations. AI systems are classified by risk level (minimal, limited, high, unacceptable), and each level imposes specific requirements for transparency, documentation, and human oversight.

Etymology

The term combines 'AI' (Artificial Intelligence), which emerged in the 1950s at the Dartmouth Conference, and 'Risk Assessment', a methodology borrowed from industrial engineering and finance. The expression gained popularity from 2018–2020 with the rise of ethical concerns around AI and the first international regulatory frameworks.

Concrete examples

Audit of a chatbot before deployment in customer service

Act as an AI risk auditor. Assess the potential risks of a customer service chatbot for a bank: identify risk categories (bias, hallucinations, data leaks, regulatory compliance), assign a severity level (low/medium/high/critical), and propose mitigation measures for each identified risk.

Bias assessment in an automated recruitment system

Analyze the bias risks of an AI model used for resume screening. For each possible bias type (gender, age, ethnicity, disability), describe the mechanism by which it could manifest, its impact on candidates, and corrective measures to implement. Present the results in a risk matrix.

Regulatory compliance with the European AI Act

Based on the risk classification of the European AI Act, determine which risk category an AI-assisted medical diagnosis application falls into. List the associated regulatory obligations and propose a step-by-step compliance plan.

Practical usage

In prompt engineering, AI Risk Assessment is applied by systematically including a risk assessment step before deploying a prompt in production. Concretely, this means testing the prompt with edge cases, checking outputs for bias and hallucinations, and adding guardrail instructions within the prompt itself. This practice is essential for any AI system in contact with end users or handling sensitive data.

Related concepts

AI GovernanceAlgorithmic BiasAI SafetyExplainability of AI

FAQ

What is the difference between AI Risk Assessment and AI Safety?

AI Risk Assessment is the process of identifying and evaluating risks, while AI Safety is the broader research field that aims to ensure AI systems behave safely and beneficially. Risk assessment is a tool for AI safety: it maps out dangers to then implement appropriate safeguards.

How to perform an AI risk assessment for a project using LLMs?

For an LLM-based project, start by identifying use cases and target users. Then test the system with adversarial prompts (prompt injection, attempts to bypass instructions). Evaluate risks of hallucination in your specific domain, verify compliance with applicable regulations (GDPR, AI Act), and document each risk with its probability, impact, and planned mitigation measures.

Is AI Risk Assessment legally mandatory?

With the European AI Act now in force, yes, for high-risk AI systems (recruitment, credit, healthcare, justice, etc.). These systems must undergo a conformity assessment including a documented risk analysis. Even outside these obligations, performing a risk assessment is considered an essential best practice for any responsible AI deployment.

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