

[ AI Safety & Red Teaming ]
Identify vulnerabilities in safety, bias, and toxicity with expert-driven adversarial testing data at scale.
Request a safety audit[ The Safety Gap ]
Standard evaluations can miss critical failure modes in safety, fairness, and robustness.
Closing the gap between automated benchmarks and real-world adversarial conditions requires structured human evaluation.
The Solution? Expert-led adversarial test datasets that surface the edge cases automated tools miss across safety, bias, and content policy.
Adversarial inputs that bypass safety guardrails and elicit restricted outputs.
Uneven outputs across languages and user groups that damage trust and invite scrutiny.
Emerging AI standards require documented adversarial testing. Gaps in your safety record become legal liability.
[ Why Rise Data Labs ]
Adversarial AI testing requires nuanced human judgment that rule-based tools simply cannot replicate.
01
Human testers don't follow scripts. They find what automated tools are blind to.
02
Safety failures don't speak one language. Neither do the testers.
03
Evaluation outputs structured for regulatory review and audit documentation.
[ Capabilities ]
Type
Description
Use Case
Prompt Injection Testing
Testing for jailbreak vectors, instruction overrides, and guardrail bypass techniques.
Evaluating a customer-facing chatbot for multi-turn injection resilience.
Toxicity Evaluation
Detecting harmful, offensive, or policy-violating outputs across diverse input conditions.
Screening a content generation model for hate speech and harmful content across user personas.
Bias Auditing
Measuring output consistency and fairness across demographic groups and protected categories.
Auditing a multilingual assistant for disparate treatment across gender and ethnicity.
Policy Compliance
Validating adherence to internal safety policies and external regulatory frameworks under adversarial conditions.
Stress-testing an enterprise AI against EU AI Act safety requirements before deployment.
Hallucination Detection
Identifying factually incorrect, fabricated, or misleading outputs through expert verification.
Verifying factual accuracy of a medical Q&A model across high-stakes health domains.
[ How It Works ]
01
We map your model's deployment context, user base, and regulatory requirements to define the evaluation scope.
02
Expert red teamers design attack scenarios, personas, and prompt strategies tailored to your model's specific risk surface.
03
Trained evaluators execute adversarial sessions, document failures, and assign severity ratings across each category.
04
Receive a structured report with categorized vulnerabilities, sample failure prompts, severity scores, and actionable fix guidance.
Move beyond standard benchmarks. Start adversarial evaluation today.
Request a sample set