Data privacy in AI due diligence

Introduction

Artificial Intelligence (AI) is transforming many industries, including healthcare and life sciences, by enabling innovations in diagnostics, treatment personalisation, and operational efficiency. However, these advancements require extensive data usage, particularly concerning sensitive information. Before entering into contractual agreements, organisations must conduct thorough due diligence to ensure compliance with data protection regulations, such as the General Data Protection Regulation (GDPR), and to mitigate potential risks. This article explores essential considerations for data privacy in pre-contractual AI due diligence, focusing on legal obligations, risk mitigation strategies, and ethical concerns.

Why data privacy matters in AI pre-contractual due diligence

Ensuring regulatory compliance and robust data governance is crucial before finalising AI-related contracts. Inadequate pre-contractual due diligence can expose organisations to legal liabilities, financial penalties, and reputational risks. By assessing an AI vendor's data privacy policies and security frameworks in advance, contractual parties can define clear terms, allocate responsibilities, and establish trust in data-driven collaborations.

Key aspects of AI pre-contractual due diligence in data privacy

Defining data processing roles and responsibilities

Clearly defining data processing responsibilities within pre-contractual negotiations is essential for compliance and accountability:

• Data controllers: entities determining how and why personal data is processed, ensuring compliance with legal requirements.
• Data processors: organisations handling data on behalf of controllers, requiring explicit contractual obligations on security and data handling.
• Sub-processors: third-party entities assisting in data processing, requiring additional contractual safeguards and oversight.
• Technology providers: suppliers of AI systems, cloud services, or hardware that may influence data processing and must adhere to data privacy regulations.

Ensuring these roles are clearly articulated in agreements mitigates compliance risks and fosters transparent data governance between contractual parties.

GDPR compliance and contractual safeguards

During pre-contractual due diligence, organisations must verify that AI vendors comply with GDPR’s core principles and integrate necessary contractual safeguards:

• Lawful basis for processing: establishing explicit legal grounds for data processing, whether through consent, contractual necessity, or legitimate interest.
• Data protection impact assessment (DPIA): evaluating potential privacy risks associated with AI-driven analytics and automated decision-making.
• Data processing agreements (DPAs): drafting contracts that outline security obligations, liability clauses, and compliance mechanisms.
• International data transfers: ensuring that any cross-border data processing complies with GDPR’s standard contractual clauses (SCCs) or other legal frameworks.

Properly structured contractual safeguards prevent disputes and ensure AI vendors adhere to regulatory and ethical data practices.

Security measures and incident response protocols

A key component of AI pre-contractual due diligence is assessing an AI vendor’s data security framework, including:

• Anonymisation and pseudonymisation techniques: implementing methods to minimise re-identification risks while preserving data utility.
• Penetration testing and security audits: conducting assessments to identify system vulnerabilities before finalising contractual agreements.
• Breach notification and incident response plans: verifying vendors have procedures in place to comply with GDPR’s 72-hour or, in certain cases, immediate, breach reporting requirement.
• Encryption and access controls: ensuring data is protected through encryption and strict role-based access management.

Pre-contractual due diligence in security minimises the risk of data breaches and ensures vendors implement industry-standard security measures.

Data retention and minimisation policies

Organisations must establish clear data minimisation and retention policies before entering into AI services related contracts:

• Purpose limitation: ensuring data collection and processing are restricted to predefined, necessary purposes.
• Retention and deletion schedules: setting timeframes for data storage, ensuring compliance with GDPR’s rights of data subjects.
• Training data restrictions: prohibiting vendors from using identifiable personal data for AI model training without explicit contractual approval.
• Compliance audits on data deletion: conducting periodic reviews to ensure proper data disposal and compliance with retention policies.

Pre-defining these policies in contracts ensures that vendors comply with legal and ethical data-handling practices.

Transparency, explainability, and ethical considerations

Pre-contractual due diligence must also include evaluating an AI vendor’s approach to transparency, fairness, and ethical AI deployment:

• Respect for data subject rights: ensuring compliance with GDPR’s requirements on access, rectification, portability, and erasure.
• Consent management frameworks: establishing clear guidelines on obtaining and managing user consent.
• Algorithmic explainability: ensuring AI decision-making is interpretable for regulators and stakeholders.
• Bias detection and fairness audits: assessing AI models for potential biases that may impact different demographic groups.

Defining these elements before contract finalisation ensures ethical AI deployment and regulatory alignment.

Conclusion

Pre-contractual due diligence in AI partnerships is a critical step in ensuring data privacy compliance and minimising legal and operational risks. Organisations must thoroughly assess AI vendors' adherence to GDPR, security measures, and contractual obligations before entering into agreements.

By proactively addressing data privacy considerations, organisations can establish clear expectations, foster trust, and ensure that AI-driven innovations operate within ethical and regulatory frameworks.