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2. Privacy & Security3 - Other

Legal challenges

Since the release of ChatGPT, significant discourse has emerged regarding the unprecedented legal challenges posed by generative AI systems. These challenges primarily involve protecting privacy and personal data, as well as preserving copyrights. The former encompasses safeguarding personal information, while the latter includes issues related to the use of copyrighted content for training AI models and determining the legal status of works produced by AI systems.

Source: MIT AI Risk Repositorymit744

ENTITY

3 - Other

INTENT

3 - Other

TIMING

3 - Other

Risk ID

mit744

Domain lineage

2. Privacy & Security

186 mapped risks

2.1 > Compromise of privacy by leaking or correctly inferring sensitive information

Mitigation strategy

1. **Implement Stringent Data Minimization and Confidentiality Protocols** Mandate the filtering and exclusion of all personal, client-confidential, and proprietary data from input into generative AI systems, especially those lacking specific contractual data protection guarantees. This includes employing techniques like anonymization, pseudonymization, or utilizing synthetic datasets for development, thereby mitigating the risk of inadvertent data leakage, unauthorized data use for model training, or inferring sensitive information. 2. **Mandate Comprehensive Vendor Due Diligence and Contractual Safeguards** Prioritize the selection of enterprise-grade AI tools and platforms that offer robust contractual protections, specifically Zero Data Retention policies and explicit assurances that user input data will not be used to train vendor models. A thorough due diligence process, including assessment of vendor security measures, IP ownership terms, and alignment with regulatory requirements (e.g., GDPR), is essential before deployment. 3. **Establish and Enforce a Formal Internal AI Governance Framework** Develop and continuously enforce a formal, cross-functional internal policy—involving legal, compliance, and IT professionals—that clearly defines acceptable use cases for generative AI. The framework must include ongoing mandatory training for all personnel on data privacy obligations, confidential information handling, and the imperative to verify all AI-generated output for compliance and potential data leakage.