AI Safety Testing Services for Safer, Compliant AI Systems
Vaidik AI stress-tests large language models and generative AI systems against real-world misuse, bias, and regulatory risk — through red teaming, adversarial testing, and compliance-mapped evaluation frameworks built for global deployment.
Full-Spectrum AI Safety Testing Services
From adversarial red teaming to regulatory readiness, our AI safety testing services evaluate how your model behaves under pressure — not just how it performs in a demo.
Red Teaming & Adversarial Testing
Structured attack simulations that probe your LLM or generative AI system for exploitable weaknesses before real users find them.
AI red teamingBias & Fairness Auditing
Testing across demographic, cultural, and linguistic dimensions to identify skewed, discriminatory, or unequal model outputs.
AI bias testingJailbreak & Prompt Injection Testing
Probing for prompt manipulation, role-play exploits, and instruction-override tactics used to bypass model safeguards.
Jailbreak testingContent Moderation Testing
Evaluating output filters against toxic, violent, sexual, extremist, and self-harm content generation scenarios.
Trust & safety testingAlignment & RLHF Evaluation
Assessing whether model behavior stays consistent with intended values, instructions, and organizational policy.
AI alignment testingRobustness & Stress Testing
Evaluating safety and performance under edge cases, noisy inputs, and out-of-distribution or adversarial prompts.
Adversarial testingGuardrail & Filter Validation
Verifying input/output guardrails, refusal behavior, and escalation paths function as designed under real conditions.
AI guardrail testingHallucination & Factuality Testing
Measuring factual grounding, source attribution accuracy, and confidence calibration across high-stakes use cases.
Model safety evaluationRegulatory Readiness Testing
Mapping model behavior and documentation against NIST AI RMF, EU AI Act, ISO/IEC 42001, and regional guidance.
AI compliance testingHow We Run an AI Safety Testing Engagement
A repeatable, evidence-based process built by AI trust & safety specialists — not a one-time checklist.
Scoping & threat modeling
Define the model, use case, and risk surface to test against.
Attack scenario design
Build test cases matched to your industry and deployment context.
Human + automated testing
Hybrid red team execution with linguists, domain experts, and tooling.
Risk scoring
Findings classified by severity, likelihood, and regulatory relevance.
Reporting & roadmap
Clear remediation guidance mapped to your engineering backlog.
Re-test & monitor
Verify fixes and support ongoing safety evaluation post-launch.
Testing Mapped to Regional AI Regulation
We calibrate every engagement to the frameworks that matter in your market.
🇺🇸USA
Testing aligned to the NIST AI Risk Management Framework and emerging state-level AI laws.
🇬🇧UK
Evaluation practices informed by the UK AI Safety Institute and pro-innovation regulatory approach.
🇦🇺Australia
Testing referenced against Australia's AI Ethics Framework and proposed mandatory guardrails.
🇦🇪UAE
Support aligned to the UAE's national AI strategy and regional AI governance guidance.
🌐Global
EU AI Act, ISO/IEC 42001, and OECD AI Principles referenced for multinational deployments.
Built for High-Stakes AI Deployments
Trust & Safety Testing Built by Practitioners
Human red teamers
Trained specialists design attacks automated tools miss.
Multilingual coverage
Testing across languages and cultural contexts, not English-only.
Regulation-mapped
Every finding tied to a relevant framework or standard.
Confidential by default
NDA-first engagements for proprietary and pre-launch models.
AI Safety Testing Services — Common Questions
AI safety testing evaluates how a model behaves under misuse, edge cases, and adversarial pressure — covering bias, jailbreaks, harmful content, and factual reliability. It's how organizations catch risks before deployment instead of after an incident.
Yes. We test proprietary and fine-tuned LLMs, chatbots, copilots, and generative AI products across text, and can extend evaluation to multimodal systems on request.
Yes. Engagements can be scoped against the NIST AI Risk Management Framework, EU AI Act risk categories, ISO/IEC 42001, and regional guidance relevant to your market.
Scope-dependent. A focused red-teaming sprint typically runs two to three weeks, while a full nine-domain safety audit with remediation support runs longer. Timelines are confirmed after scoping.
Yes. We work with proprietary, fine-tuned, and pre-launch models under NDA, and can test via secure sandbox or API access depending on your infrastructure.
Yes. Vaidik AI supports clients across the USA, UK, Australia, UAE, and other global markets, with testing calibrated to relevant regional frameworks.
Ready to stress-test your AI system before your users do?
Talk to our AI safety testing team about red teaming, bias auditing, and compliance-mapped evaluation for your model.