Large language models (LLMs) are attractive for privacy and data protection teams because so much of their work is text‑heavy: policies, contracts, questionnaires and assessments.

At the same time, these teams handle some of the most sensitive information in the organization. Any automation must be carefully designed.

Summary and key takeaways

• 1LLMs can speed up drafting, summarization and triage.
• 2Input and output handling must respect confidentiality and retention rules.
• 3Guardrails and review workflows are essential.
• 4Internal models can reduce reliance on third‑party data processing.
• 5Success depends on pairing models with structured processes.

High‑value use cases for LLMs

Practical examples include: - Summarizing long DPIAs for executives. - Highlighting risky clauses in data processing agreements. - Categorizing vendor responses into standard topics. - Suggesting mappings between free‑text answers and controls.

Each of these reduces manual reading time but still benefits from human review.

Designing prompts that respect context

Good prompts: - Clearly explain the role of the model (assistant, classifier, summarizer). - Define what information is in and out of scope. - Ask for structured outputs that can be checked or ingested by systems. - Encourage the model to state uncertainty.

Poor prompts mix many goals, provide ambiguous instructions and make it harder to spot where the model is guessing.

Handling sensitive data

Privacy teams should work closely with security and engineering to: - Decide which categories of data may be sent to which model endpoints. - Configure logging, retention and access settings. - Mask or tokenize data where possible before processing. - Monitor for unexpected patterns in prompts or outputs.

These safeguards need to be documented and reflected in your own DPIAs.

Human‑in‑the‑loop patterns

LLMs work best when: - They propose draft outputs, not final decisions. - Reviewers can quickly accept, edit or reject suggestions. - Feedback is captured to tune prompts or examples. - Complex or high‑risk items automatically bypass automation.

This keeps control with experts while still capturing efficiency gains.

Communicating your approach

As regulators and customers pay more attention to AI, be ready to describe: - Which workflows use LLMs and why. - How you protect data in those workflows. - How you evaluate quality and guard against bias.

This transparency will increasingly be part of due diligence and trust discussions, especially for organizations handling sensitive data.