What is an AI testing?

AI testing is the practice of systematically evaluating AI systems for safety, fairness, accuracy, and robustness before and after deployment. Unlike traditional software testing, where you check whether code runs correctly, AI testing evaluates how a system behaves across a wide range of conditions, including hostile ones.

AI systems learn from data and generate outputs that can vary depending on context. A model might perform well on benchmarks but still produce biased decisions, fabricate information, leak sensitive data, or generate harmful content when faced with real-world inputs. AI testing is how you find out.

Why AI testing matters

When an AI system produces biased hiring recommendations, fabricates medical information, or leaks customer data, the consequences are real. Testing catches these issues before they reach users and keeps catching them once the system is live.

It is also a regulatory expectation. The EU AI Act, NIST AI Risk Management Framework, and ISO 42001 all require structured evaluation of AI systems. Without testing, there is no evidence to show that your systems meet those requirements.

What AI testing covers

Our platform evaluates AI systems across five core risk dimensions:

• Bias - Does the system produce different outcomes for different demographic groups? Are there patterns of discrimination, stereotyping, or unequal treatment?
• Efficacy - Does the system actually deliver accurate, useful results for its intended purpose? Are there performance gaps across different scenarios?
• Robustness - Does the system hold up under unusual, noisy, or adversarial inputs? Can it be easily manipulated or broken?
• Privacy - Does the system leak training data, personal information, or sensitive details? Does it handle data in compliance with regulations?
• Explainability - Can the system's decisions be understood, traced, and explained to stakeholders, regulators, and affected users?

Within these dimensions, our testing suite covers specific failure modes including hallucinations, toxicity, jailbreaking, prompt injection, data extraction, stereotyping, and offensive language generation.

How AI testing works on our platform

We use two complementary approaches:

• ‍Benchmarking assesses an AI system's performance against a predefined task by mapping the system's outputs to a dataset of prompts and expected responses. This tells you how well the model performs under controlled conditions and lets you compare performance across models or track changes over time.‍
• Adversarial testing stress-tests the model to uncover unknown risks and vulnerabilities. This includes red teaming, jailbreak attempts, prompt injection, and other attack techniques designed to push the model into producing outputs it should not. This tells you how the model behaves when someone is actively trying to make it fail.

Together, these approaches give you a complete picture: how the system performs at its best and how it performs at its worst.

Our platform provides over 100 automated tests across all of these areas. Tests can be run on demand or scheduled as part of your governance workflow. Results are scored and broken down by category, feeding directly into your risk profile, compliance reports, and monitoring dashboards. Reports are structured for legal, technical, and executive audiences.

AI testing is part of the Protect solution in our governance platform. Once you have discovered and inventoried your AI systems, testing helps you understand how they actually behave in practice. Results connect to your risk assessments, compliance workflows, policy enforcement, and runtime monitoring so that everything stays in one place.

Testing is not a one-time step. It runs continuously, before and after deployment, catching new issues as models are updated, as new attack techniques emerge, and as regulations evolve.

If you want to know more about how we do AI testing and evaluation on your systems, get a demo now.