What are the special requirements for vertical AI search in terms of data privacy and security?

When vertical AI search processes sensitive data in industries such as healthcare and finance, its data privacy and security must meet special requirements including data minimization, compliance adaptation, and hierarchical access control. Data collection phase: The "principle of minimum necessity" must be followed, collecting only core data directly related to the search target to avoid redundancy of irrelevant information. For example, medical AI search only collects data related to the patient's condition, not the complete medical record. Processing phase: It needs to adapt to industry-specific regulations, such as HIPAA compliance in the medical field and GDPR or China's Personal Information Protection Law in the financial field, ensuring data desensitization and de-identification. Storage security: Sensitive data must be encrypted for storage (e.g., AES-256 encryption), and a regular security audit mechanism should be established to prevent unauthorized access. Model level: Training data must undergo strict cleaning to remove personal identification information; the inference process should avoid泄露原始数据特征. It is recommended that vertical AI search teams develop data security frameworks in conjunction with industry standards, regularly update privacy agreements, and reduce the risk of centralized data storage through technical means such as federated learning to enhance the privacy protection capabilities of vertical AI search.