Our AI Risk Simulation approach gives organisations a controlled, repeatable method to expose, score, and resolve high-impact GenAI vulnerabilities through a comprehensive GenAI Vulnerability Scan.

From phishing prompt generation to policy misalignment and hallucinated compliance advice, we simulate realistic adversarial behaviours that regulators and attackers increasingly focus on.

This enables clients to prioritise the right guardrails, governance updates, and escalation paths before reputational or regulatory damage occurs.

• Robustness
  Tests whether the AI system can handle unexpected, adversarial, or noisy inputs without failing or producing unsafe outcomes. Robustness testing ensures stability under stress and edge cases.
• Privacy
  Evaluates whether the system protects personal or sensitive data. This includes checking for data leakage, re-identification risks, and compliance with privacy standards like GDPR.
• Accountability
  Assesses mechanisms for tracing decisions back to responsible actors (developers, deployers, vendors). Testing ensures auditability, logging, and governance structures are in place.
• Transparency & Explainability
  Tests the system’s ability to provide understandable, interpretable outputs and rationale for its decisions. Ensures stakeholders can comprehend why the AI behaved a certain way.
• GenAI Accuracy + Hallucination
  Measures the correctness and reliability of generative AI outputs. Tests whether the model fabricates (“hallucinates”) false information and how often it aligns with ground truth or verified data.
• Data Bias
  Examines whether training or input data introduces unfair patterns that disadvantage certain groups or distort outcomes. Testing focuses on representativeness and balance.
• ML Fairness (highlighted in red in your diagram)
  Evaluates whether machine learning models produce equitable results across demographic or protected groups. Goes beyond data bias by also testing the model’s decision-making pipeline for discrimination.
• Transparency & Explainability (appears twice, possibly a duplication)
  You may want to collapse this into one category to avoid redundancy. Same as definition above.
• Jailbreakers (highlighted in red in your diagram)
  Tests for vulnerabilities in generative AI that allow users to bypass safeguards (e.g., prompt injection, adversarial prompting). Ensures that harmful or disallowed outputs cannot be coerced from the model.
• Security
  Evaluates resilience against cyber threats, model extraction, poisoning, or adversarial attacks. Ensures both the AI system and underlying infrastructure are hardened against exploitation.