What is a Robustness Assessment?

A Robustness Assessment evaluates how reliably your AI system performs when conditions change, inputs are unexpected, or the system faces adversarial pressure. It answers a simple but critical question: does your AI system hold up when things do not go as planned?

Like our other assessments, Robustness evaluation in our platform includes both a qualitative component and a quantitative component.

Why robustness matters

AI systems are deployed in the real world, where inputs are messy, conditions change over time, and users do not always behave as expected. A system that works perfectly in testing but fails under real-world pressure is a governance risk.

Robustness issues can show up in many ways - a model that gives wrong answers when data is slightly noisy, a system that breaks when a new type of input appears, or an AI agent that can be tricked into bypassing its safety controls. Our Robustness Assessment helps you identify these weaknesses before they cause problems in production.

Qualitative Robustness Assessment

The qualitative stage evaluates your system's robustness through structured questions about how it was built, tested, and maintained. This covers areas like:

• Whether sensitivity analysis has been performed to understand how input changes affect outputs
• What strategies are in place to handle data drift and shifting distributions over time
• Whether the system has been tested under stress conditions and unusual scenarios
• What monitoring is in place for detecting performance degradation
• Whether error analysis and backward compatibility checks are part of your process

These questions are designed to surface structural weaknesses in how robustness is handled across the system's lifecycle.

Quantitative Robustness Assessment

For a deeper, data-driven evaluation, you can run a Quantitative Robustness Assessment. This requires providing your dataset and trained model so we can test the system's behavior under controlled conditions.

The quantitative assessment measures how your system's performance changes when we introduce variations to the input data - such as adding noise, altering features, or simulating edge cases. This gives you a measurable view of how stable your system really is.

Red Teaming

For AI systems that generate natural language - such as chatbots, content generators, or AI agents - we also offer Red Teaming as part of robustness evaluation. Red Teaming is an adversarial testing process where our platform automatically runs structured attack scenarios against your AI system.

This includes testing for:

• Jailbreak resistance - whether the system can be manipulated into bypassing its safety policies
• Policy compliance under adversarial conditions
• Behavioral consistency when prompts are designed to confuse or mislead the system

Red Teaming tests run at the task, agent, or workflow level, so you get visibility into robustness at every layer of your AI system.