AI and machine-learning systems support use cases including LLM chatbots, fraud detection, clinical workflows and automated decision-making. These systems introduce attack paths across models, prompts, training data, APIs, tools and third-party components.
NSI Global tests AI and ML systems for direct and indirect prompt injection, sensitive-information disclosure, excessive agency, insecure output handling, model extraction, inference attacks, data poisoning and supply-chain compromise. Findings document the affected component, reproduction evidence, likely impact and prioritised remediation.
Where applicable, testing is mapped to the OWASP GenAI LLM Top 10 2026 and MITRE ATLAS, with governance findings referenced to the NIST AI Risk Management Framework and its Generative AI Profile.