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AI Risk Testing - When AI Goes Wrong - BetterQA

AI Risk Testing

When AI Goes Wrong

AI systems can fail in unexpected ways. We test for edge cases, adversarial inputs, and failure modes before your users find them.

Request Assessment

73%

AI Projects Face Issues

$M+

AI Failure Costs

1000+

Edge Cases Tested

24/7

Monitoring

Risk Categories

Common AI Failures

What can go wrong with AI systems in production and how we test for these risks.

Hallucinations

LLM Risk

Confident but wrong responses. Made-up facts, citations, or data that appear legitimate but are fabricated.

Prompt Injection

Security Risk

Malicious inputs that manipulate AI behavior, bypass safety controls, or extract sensitive information.

Bias & Discrimination

Fairness Risk

Unfair treatment of protected groups in predictions, recommendations, or generated content.

Model Drift

Reliability Risk

Performance degrades over time as real-world data patterns diverge from training distributions.

Threat Model

Identify potential failure modes for your AI system

Red Team

Adversarial testing to find exploits and edge cases

Validate

Verify outputs against ground truth and expectations

Harden

Implement guardrails and safety measures

Monitor

Continuous detection of anomalies in production

Why It Matters

Protect your users and your reputation

Avoid Headlines

AI failures make news. Test privately before they become public incidents affecting your brand reputation.

Regulatory Ready

EU AI Act, NIST AI RMF, and other regulations require documented testing and risk assessments.

Protect Users

Prevent harmful outputs that could affect vulnerable populations or lead to discrimination claims.

Reduce Liability

Documented testing demonstrates due diligence if issues arise, reducing legal and financial exposure.

Common Questions

Everything you need to know

What AI systems can you test? +

LLMs (GPT, Claude, Llama), chatbots, recommendation engines, computer vision systems, NLP pipelines, and custom ML models. We test both APIs and self-hosted systems.

How do you test for prompt injection? +

We use known attack patterns, generate custom adversarial inputs, and test system prompt leakage. We verify that safety measures and content filters work as intended under adversarial conditions.

What's included in an AI risk assessment? +

Threat modeling, red team testing, fairness analysis, hallucination testing, security testing, and a prioritized remediation roadmap with specific guardrail recommendations.

Do you help fix the issues you find? +

Yes. We provide specific recommendations and can implement guardrails, content filters, prompt hardening, and monitoring solutions to address identified risks.

Test Your AI Before It Fails

Get a comprehensive risk assessment of your AI system before issues reach production.