When organisations think about AI security testing, many automatically turn to established cybersecurity firms like NCC Group. But LLM penetration testing isn't just traditional cybersecurity with a new coat of paint—it requires fundamentally different expertise and approaches.
Want to understand your AI compliance risks? Take our 2-minute assessment to see how your AI systems measure up against regulatory requirements.
Why LLM Testing is Different
Traditional penetration testing focuses on breaking into systems. LLM red teaming focuses on breaking how systems think. This requires understanding:
- Prompt injection techniques specific to language models
- Training data contamination risks
- Model behavior under adversarial conditions
- Regulatory compliance for AI systems in different industries
- Domain-specific failure modes (healthcare AI vs. financial AI vs. customer service AI)
The Alternative Landscape
1. AI-Native Security Specialists
Companies like:
- Robust Intelligence
- HiddenLayer
- Protect AI
Strengths: Built specifically for AI/ML security, deep technical knowledge of model architectures
Considerations: May lack industry-specific regulatory knowledge
2. Academic and Research-Based Providers
Examples:
- University research labs
- Organisations like Anthropic's safety team
- Independent AI researchers
Strengths: Cutting-edge techniques, published methodologies
Considerations: May not understand commercial compliance requirements
3. Industry-Specific Specialists
Examples:
- Healthcare AI security specialists
- Financial services AI auditors
- Aviation/transport AI evaluators (Airside Labs)
- Legal tech AI compliance specialists
Strengths: Deep understanding of sector-specific regulations and failure modes
Considerations: May have narrower technical scope
Why Industry-Specific Matters:
When Airside Labs tests an aviation AI system, we're not just looking for generic prompt injections. We're testing scenarios like:
- Can the AI be tricked into providing incorrect flight safety information?
- Does it properly handle NOTAM (Notice to Airmen) data under adversarial conditions?
- Will it maintain proper separation standards when given conflicting instructions?
This use case-driven approach reveals risks that generic testing misses. A financial services AI needs testing for regulatory compliance around investment advice, while a healthcare AI needs evaluation for medical misinformation risks.
The Use Case-Driven Testing Advantage:
Industry specialists understand that AI failures aren't just technical—they're contextual. The same prompt injection technique might be:
- A minor annoyance in a gaming chatbot
- A compliance violation in financial services
- A safety hazard in aviation
- A patient safety risk in healthcare
Generic testing treats all AI systems the same. Use case-driven testing recognizes that context determines risk severity.
4. Big Tech Consulting Arms
Examples:
- Microsoft's AI security services
- Google Cloud's AI security offerings
- AWS's responsible AI services
Strengths: Integration with existing cloud infrastructure, broad resources Considerations: Potential conflicts of interest with their own AI products
What to Look For in an LLM Testing Provider
1. Regulatory Expertise
Your LLM testing needs to align with your industry's compliance requirements. A provider testing a healthcare chatbot needs to understand HIPAA implications, not just jailbreaking techniques.
2. Domain Knowledge
Generic "AI red teaming" misses industry-specific risks. Financial AI has different failure modes than customer service AI.
3. Use Case Understanding
The best testing providers don't just run generic attack scripts. They understand your specific business context and test scenarios that could actually impact your operations.
4. Evolving Methodology
The field is moving fast. Your provider should be adapting techniques as new models and attack vectors emerge.
5. Explainable Results
You need to understand not just what broke, but why it matters for your specific use case and regulatory environment.
The Compliance Gap
Here's what many traditional cybersecurity firms miss: Most AI failures aren't security breaches—they're compliance violations.
When your customer service AI accidentally:
- Shares competitor information
- Makes medical claims it shouldn't
- Violates data privacy rules
- Provides financial advice without proper disclaimers
These aren't "hacks"—they're regulatory time bombs.
Choosing Your Approach
If you need comprehensive security coverage: Consider AI-native specialists like Robust Intelligence or HiddenLayer.
If you're in a heavily regulated industry: Look for providers with deep sector expertise who understand your specific compliance landscape and can test use cases that matter to your business.
If you're building cutting-edge AI: Academic partnerships might give you access to the latest research techniques.
If you're already in a big tech ecosystem: Their AI security services might integrate seamlessly with your existing setup.
The Bottom Line
NCC Group brings valuable traditional cybersecurity expertise, but LLM testing requires specialized knowledge that goes beyond conventional pen testing. The best choice depends on your industry, regulatory requirements, and specific AI use cases.
The key is finding a provider who understands that AI security isn't just about preventing attacks—it's about ensuring your AI systems behave reliably and compliantly in the real world, within the specific context of how you actually use them.
Want to understand your AI compliance risks? Take our 2-minute assessment to see how your AI systems measure up against regulatory requirements.
