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P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 5 49 Data privacy is paramount in the legal sector. Feeding client contracts or case details into a third-party AI could inadvertently expose sensitive information. Many public AI tools, especially early versions of ChatGPT- style services, retain and learn from user input, raising serious concerns about confidentiality. In one cautionary example, Italy's data protection watchdog temporarily banned ChatGPT over privacy violations, later fining its maker €15 million for the improper use of personal data. This exemplifies why the GDPR and the upcoming EU AI Act require strict controls on the handling of personal data by AI. The EU AI Act will even impose fines of up to 7% of a company's global revenue for AI misuse, reflecting the high stakes of compliance. For lawyers, this means any AI- powered solution must prioritize confidentiality by design. Strategies to achieve this include data anonymization and redaction (removing names, addresses, and other identifiers before the data leaves your possession) and using encrypted channels for AI queries. Some solution providers now assure users that their data will not be used for training without consent. For instance, Anthropic's Claude model does not default to learning from client prompts. Even so, prudent lawyers treat AI like a junior associate: valuable for a first pass but never send it sensitive info without safeguards. International practitioners also need to navigate cross-border data rules. Data residency, GDPR compliance, and forthcoming laws necessitate that AI tools adhere to jurisdictional limits on data storage and processing. In short, the future of legal AI lies in solutions that deliver productivity gains while upholding privacy and ethical responsibilities. A FLEXIBLE, FUTURE- PROOF APPROACH TO LEGAL AI INFRASTRUCTURE As AI technology evolves at a rapid pace, the legal industry faces a critical question: How can systems be built that accommodate solo practitioners and global legal teams while preserving data integrity, security, and adaptability? A modular, security-first architecture offers a compelling solution. Legal professionals differ widely in their needs: a single general counsel at a startup has vastly different operational and budgetary realities than a multinational legal department. Modern AI platforms are increasingly recognizing this, offering configurable deployments that balance cost, control, and compliance. For instance, multi-tenant cloud environments enable small firms to quickly adopt AI tools without incurring heavy infrastructure investment. At the same time, private or hybrid deployments offer larger organizations the option to isolate AI processing entirely within their ecosystem. In especially sensitive use cases, a layer of pre-processing—such as automated redaction—can help ensure that identifying details, like names, emails, or contract values, are masked before any AI model is engaged, even in a cloud-based setting. This local anonymization adds a vital security buffer for confidentiality-critical workflows.

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