How the AI Act treats testing and things going wrong: whether red teaming is required, regulatory sandboxes, real-world testing of high-risk systems before launch, and AI incident response across providers and deployers.
Where the AI Act calls for testing against attacks: pre-market testing against defined metrics, resilience to data poisoning, adversarial examples and model flaws, mandatory adversarial testing for systemic-risk models, and how NIST guidance describes red teaming.
What AI regulatory sandboxes offer and how to use them: the 2027 national deadline, the AI Office sandbox, sandbox plans and exit reports as compliance evidence, liability and fine protection, free access and priority for SMEs, and personal data use inside a sandbox.
The rules for testing high-risk AI with real users before launch: the testing plan and authority approval, registration, six-month limit, informed consent, deployer agreements, incident handling and liability, and the frameworks for machinery and other Section B products.
What an AI incident response process must handle under the AI Act: what counts as a serious incident, provider deadlines and investigation rules, deployer duties to suspend and inform, corrective actions, systemic-risk model incidents and the AI Office route.
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