Question 1

How long does AI security testing take?

Accepted Answer

Comprehensive AI security testing typically takes 2-4 weeks depending on the scope and complexity of your AI system. This includes adversarial testing, red teaming, compliance validation, and detailed reporting. Rush assessments can be completed in 1-2 weeks for critical deployments.

Question 2

What makes adversarial testing different from standard benchmarks?

Accepted Answer

Standard benchmarks like MMLU test general knowledge and produce scores like 95% accuracy that don't predict real-world performance. Adversarial testing specifically targets your AI's weaknesses with crafted inputs designed to expose failures in your actual use case, including prompt injection, jailbreaks, and edge cases that standard tests miss.

Question 3

How do you measure AI performance in real-world scenarios?

Accepted Answer

We use industry-specific test datasets developed with domain experts, regulatory compliance scenarios based on current legislation, and adversarial prompts designed to expose vulnerabilities. Our methodology combines automated testing of 6,500+ scenarios with expert red team validation to identify failure modes that impact production performance.

Question 4

Can you test AI systems in specific industries like aviation or healthcare?

Accepted Answer

Yes, we specialize in domain-specific AI testing with deep expertise in aviation, defense, and regulated industries. Our methodology includes industry-specific test cases, regulatory compliance scenarios, and safety-critical validation that standard testing approaches cannot provide.

Question 5

What compliance frameworks do you test against?

Accepted Answer

We provide comprehensive testing against OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework (AI RMF 1.0), EU AI Act requirements, GDPR data protection standards, and industry-specific regulations. Each framework is validated through targeted test scenarios with detailed compliance documentation.

Question 6

What AI compliance regulations apply to my chatbot?

Accepted Answer

AI chatbots must comply with GDPR for data protection, the EU AI Act for high-risk AI systems, NIST AI Risk Management Framework for responsible AI deployment, and industry-specific regulations such as financial services, healthcare, or aviation standards. Requirements vary based on your industry, geography, and use case.

Question 7

How long does AI chatbot compliance testing take?

Accepted Answer

Comprehensive compliance testing typically takes 2-4 weeks, including GDPR data protection audits, EU AI Act conformity assessment, bias detection testing, and regulatory documentation. Timeline depends on the complexity of your chatbot, number of compliance frameworks, and depth of testing required.

Question 8

What happens if my chatbot fails compliance testing?

Accepted Answer

We provide detailed remediation guidance for each identified compliance issue, including specific technical fixes, policy updates, and architectural recommendations. Our reports prioritize findings by severity and regulatory impact, allowing you to address critical violations first. We offer re-testing services after remediation to verify compliance.

Question 9

Do I need compliance testing for internal chatbots?

Accepted Answer

Yes, internal chatbots processing employee data must comply with GDPR, data protection laws, and corporate governance policies. Internal systems can also create liability if they exhibit bias, leak sensitive information, or make inappropriate recommendations. Compliance testing ensures your internal AI meets legal and ethical standards.

Question 10

How often should I test my AI chatbot for compliance?

Accepted Answer

Initial compliance testing before deployment is mandatory. After that, testing should occur whenever you update the underlying model, modify training data, change system prompts, or when new regulations take effect. We recommend quarterly compliance reviews for customer-facing chatbots and annual audits for lower-risk internal systems.

Question 11

What is the OWASP Top 10 for LLM Applications?

Accepted Answer

The OWASP Top 10 for LLM Applications is a security framework identifying the most critical vulnerabilities in Large Language Model systems. It covers prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft.

Question 12

How do you test for prompt injection vulnerabilities?

Accepted Answer

We test for both direct and indirect prompt injection using jailbreak attempt detection, role manipulation testing, system prompt extraction attempts, and context injection validation. Our methodology includes 6,500+ adversarial test cases specifically designed to expose prompt injection vulnerabilities, combined with expert red team validation.

Question 13

What is sensitive information disclosure in LLMs?

Accepted Answer

Sensitive information disclosure occurs when LLMs leak personally identifiable information (PII), proprietary data, API keys, or confidential information through their responses. We test for training data extraction, PII leakage, API key exposure, and confidential data inference using targeted adversarial prompts designed to expose these vulnerabilities.

Question 14

How long does OWASP LLM compliance testing take?

Accepted Answer

Comprehensive OWASP Top 10 for LLM compliance testing typically takes 2-3 weeks, including automated vulnerability scanning across all 10 categories, manual red team validation by security experts, detailed remediation guidance with code examples, and compliance documentation for regulatory requirements.

Question 15

Can OWASP LLM testing prevent AI security breaches?

Accepted Answer

Yes, proactive OWASP LLM testing identifies and helps remediate vulnerabilities before they can be exploited. By testing against all 10 categories with both automated and manual techniques, we expose security gaps that attackers could leverage, providing specific remediation steps to prevent breaches, data leakage, and unauthorized access.

Question 16

What is the NIST AI Risk Management Framework?

Accepted Answer

The NIST AI Risk Management Framework (AI RMF 1.0) provides organizations with a comprehensive approach to designing, developing, deploying, and using AI systems responsibly. It consists of four core functions: Govern (establishing AI governance and policies), Map (identifying AI risks and impacts), Measure (assessing and tracking AI risks), and Manage (implementing risk mitigation strategies).

Question 17

How does Airside Labs help with NIST AI RMF compliance?

Accepted Answer

Airside Labs addresses critical requirements of the NIST AI RMF through comprehensive risk assessment testing and ongoing validation across all four core functions. We provide specialized coverage including bias detection, reliability testing, and evaluation frameworks designed to validate trustworthy AI deployment with the documentation and evidence needed for responsible AI governance.

Question 18

Is NIST AI RMF compliance mandatory?

Accepted Answer

While NIST AI RMF is voluntary guidance rather than mandatory regulation, many organizations adopt it as best practice for responsible AI deployment. Government contractors, regulated industries, and organizations seeking to demonstrate responsible AI practices often implement NIST AI RMF to establish credibility and reduce AI-related risks.

Question 19

What is the difference between NIST AI RMF and other AI compliance frameworks?

Accepted Answer

NIST AI RMF is a voluntary risk management framework focused on trustworthy AI characteristics across the AI lifecycle. Unlike regulatory frameworks like the EU AI Act (which has legal requirements) or OWASP LLM Top 10 (which focuses specifically on LLM security vulnerabilities), NIST AI RMF provides holistic guidance for responsible AI governance encompassing fairness, transparency, accountability, and safety.

Question 20

How long does NIST AI RMF implementation take?

Accepted Answer

NIST AI RMF implementation timeline varies based on organizational maturity and AI system complexity. Initial assessment and gap analysis typically takes 2-4 weeks. Full framework implementation including governance policies, risk mapping, measurement systems, and management processes generally requires 3-6 months. Ongoing monitoring and validation continues throughout the AI system lifecycle.

Question 21

Why do 95% of AI projects fail?

Accepted Answer

According to MIT research, 95% of AI projects fail to deliver ROI due to inadequate testing, lack of domain-specific validation, insufficient compliance coverage, and unrealistic performance expectations based on misleading benchmark scores. Success requires practical implementation expertise, rigorous adversarial testing, and proper regulatory compliance from the start.

Question 22

How quickly can I see ROI from AI implementation?

Accepted Answer

With proper implementation and testing, AI systems typically show measurable ROI within 3-6 months. Our approach focuses on practical, high-impact use cases with clear success metrics. We help you avoid the common pitfalls that lead to failed AI projects by ensuring your system is properly tested, compliant, and aligned with actual business needs before full deployment.

Question 23

Do you only test AI systems or help implement them too?

Accepted Answer

We provide both comprehensive testing and expert implementation support. Our services include: building AI agents and systems that deliver real ROI, adversarial testing and security validation, full compliance coverage for all major frameworks, ongoing monitoring and optimization, and hands-on implementation guidance to ensure your AI project succeeds.

Question 24

How is this different from standard AI testing tools?

Accepted Answer

Standard tools test against generic benchmarks that don't predict real-world performance. We combine domain-specific test datasets, adversarial testing designed to expose actual vulnerabilities, expert red team validation, comprehensive compliance coverage, and practical implementation expertise. Our focus is on building AI systems that actually work in production, not just achieving high scores on meaningless benchmarks.

Question 25

What does the free AI chatbot test check for?

Accepted Answer

Our free test generates adversarial prompts designed to expose common security vulnerabilities in AI chatbots, including prompt injection attempts, jailbreak techniques, inappropriate content generation, data leakage risks, and role manipulation attacks. You can test your chatbot with these prompts to identify potential security gaps.

Question 26

How do I use the adversarial prompt generator?

Accepted Answer

Simply describe your chatbot's use case or intended functionality, and our tool generates adversarial test prompts specifically designed to challenge your system. You can then copy these prompts and test them directly against your AI chatbot to see how it responds to potentially malicious or edge-case inputs.

Question 27

Is this really free with no credit card required?

Accepted Answer

Yes, our adversarial prompt generator is completely free to use with no credit card required. You can generate and test as many adversarial prompts as you need. For comprehensive automated testing across 6,500+ scenarios with expert analysis and compliance documentation, we offer paid assessment services.

Question 28

What should I do if my chatbot fails the test?

Accepted Answer

If your chatbot responds inappropriately to adversarial prompts, it indicates security vulnerabilities that should be addressed before production deployment. We recommend implementing input validation, output filtering, content moderation, and regular security testing. For comprehensive remediation guidance and expert support, contact us for a full security assessment.

Question 29

How is this different from comprehensive AI testing?

Accepted Answer

The free tool provides sample adversarial prompts for manual testing, helping you identify obvious vulnerabilities. Comprehensive AI testing includes automated evaluation across 6,500+ scenarios, expert red team validation, compliance framework testing (OWASP, NIST, EU AI Act), detailed remediation guidance, and formal compliance documentation suitable for regulatory requirements.

Question 30

What is the Pre-Flight aviation AI benchmark?

Accepted Answer

Pre-Flight is an open-source benchmark suite with 300+ questions designed to evaluate AI models on aviation-specific knowledge. It tests comprehension of ICAO standards, airport operations, safety procedures, emergency response, regulatory compliance, and complex reasoning required for aviation applications. The benchmark is available on GitHub and integrated into the UK AISI community evaluations package.

Question 31

How can I use the Pre-Flight benchmark for my aviation AI?

Accepted Answer

The Pre-Flight benchmark is open-source and available on GitHub. You can download the question set, run evaluations against your AI model, and compare results against published baselines from leading LLMs. The benchmark supports automated evaluation pipelines and is compatible with standard AI testing frameworks. Detailed documentation and implementation guides are provided in the repository.

Question 32

What aviation domains does Pre-Flight cover?

Accepted Answer

Pre-Flight covers comprehensive aviation domains including ICAO Annex standards and recommended practices, airport operations and ground handling, air traffic control procedures, flight safety and emergency response, aircraft systems and performance, meteorology and weather operations, regulatory compliance and documentation, and maintenance and engineering practices.

Question 33

How does Pre-Flight compare to general AI benchmarks?

Accepted Answer

Unlike general benchmarks like MMLU that test broad knowledge, Pre-Flight focuses exclusively on aviation domain expertise. AI models that score 90%+ on general benchmarks often perform significantly worse on Pre-Flight because aviation requires specialized terminology, regulatory knowledge, safety-critical reasoning, and practical operational understanding that general training doesn't provide.

Question 34

Is Pre-Flight suitable for safety-critical aviation AI validation?

Accepted Answer

Pre-Flight provides essential domain knowledge validation but is one component of comprehensive aviation AI testing. Safety-critical systems require additional adversarial testing, failure mode analysis, regulatory compliance validation, and expert review. For aviation AI deployed in operational environments, Pre-Flight should be combined with specialized safety testing and certification processes.

Question 35

What is included in bias detection testing?

Accepted Answer

Our bias detection testing examines protected characteristics (race, gender, age, disability, religion), socioeconomic bias, geographic and cultural fairness, and language accessibility. We test for discriminatory outputs, unfair treatment patterns, and ensure compliance with equality legislation through systematic evaluation across diverse user scenarios.

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