Privacy work pays off when it is specific, boring, and repeatable. Treat prompts and outputs like personal data by default. Keep them encrypted, limit access, and store less for less time. Place the endpoint close to your users so data stays in‑region by design. Ensure compliance with regulations and meet specific requirements for data governance and privacy standards. Privacy-by-design principles require that data protection measures be integrated into technology from the earliest project stages, ensuring compliance and reducing risks.
- Residency and location:
- Choose a region that aligns with your compliance needs.
- Document how API requests are handled and routed within the selected region to ensure data residency and security.
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Launch a vLLM inference server on Compute in France or UAE. You get a dedicated HTTPS endpoint that works with OpenAI SDKs. Choose the region that matches your data residency goals and keep traffic close to users. Deploy in the cloud and manage data residency with confidence.
Introduction to LLM inference
LLM inference is when computers use large language models to understand and create human-like text. It's the tech that runs your chatbots, translation tools, and automated writing helpers. Customer support systems rely on it too. When you're using LLM inference in your organization, data protection becomes crucial—especially with sensitive information. You need clear policies about how long to keep data, how to protect it, and when to delete it safely. European Union regulations demand this. GDPR's core data processing principles apply to each stage of an LLM's lifecycle, from training to deployment. Build strong data protection into every step of your LLM process. This reduces risk and shows you handle sensitive data responsibly. However, the 'black-box' nature of LLMs complicates the ability to explain how personal data influences their outputs, making compliance with data subject rights difficult. The right of access under GDPR allows individuals to know if their data is being processed, but the complex structure of LLMs complicates this further. Additionally, LLMs can perpetuate biases or produce inaccurate outputs, which can violate principles of fair processing under GDPR.
Core principles (keep it simple)
- Data minimization. Collect only what you need to serve the request.
- Purpose limitation. Use prompts/outputs only to deliver the response and improve reliability, unless you have clear consent or contract terms for more. When you're collecting data under GDPR, you need a clear, legal reason that's tied to what you're actually going to do with it.
- Storage limitation. Keep logs and traces for the shortest useful period.
- Security by default. Enforce TLS, scoped keys, and least‑privilege access.
- Putting these principles into practice is essential for effective data privacy and compliance. Organizations must conduct Data Protection Impact Assessments (DPIAs) before implementing LLMs that may pose high risks to individuals' rights. Additionally, organizations need to conduct risk assessments to identify privacy risks throughout the AI development lifecycle. AI technologies increase privacy risks by enhancing data collection and analysis.
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Residency and location
- Place the endpoint in an EU region to keep packets local; note that requirements may differ by country and should be reviewed accordingly.
- Document data flows (client → gateway → inference → storage).
- Avoid cross‑region backups of logs or traces unless necessary and covered by your contracts. Data residency further enhances data control for organizations operating in Europe.
Logging and retention
- Log counts and timings, not raw text. Prefer: prompt_tokens, output_tokens, TTFT, TPS, error codes. Only retain data that is necessary for operational purposes.
- If you must log text for debugging, sample sparingly, redact, and store separately with tighter controls. Retain records only when required for debugging, and ensure secure storing of such records.
- Set a default retention (e.g., 7–30 days) and auto‑delete. Logs should be stored and retained for the minimum period necessary, with a focus on storing data securely. Data retention policies should be reviewed at least annually to ensure they remain effective and compliant with regulations.
- Tag logs by region and environment; keep EU logs in EU storage. Tag logs to track when and how data was collected. AI models can inadvertently expose sensitive data, leading to accidental data leakage.
Data subject rights (DSRs)
- Build a simple process to locate and delete records tied to a user ID or key, empowering users to manage their own data. Developing methods to locate and remove personal data from LLMs may require retraining the model. GDPR establishes the rights of individuals to access their personal data and request its deletion.
- Keep request IDs and hashed user IDs in logs so you can find entries without exposing content.
- Document who approves deletions and how long they take.
Inputs, outputs, and redaction
- Treat prompts and outputs as personal data unless proven otherwise.
- Redact obvious PII before storage; avoid pasting secrets into prompts.
- Blocklist dangerous patterns (access keys, card numbers) at the gateway when possible.
- Train staff never to reuse customer prompts as public examples without consent, especially if the example contains sensitive information. Organizations must use synthetic or anonymized data in LLM training whenever possible to mitigate privacy risks.
Sensitive data handling
You're handling sensitive data when you deploy LLM inference systems, and that's a big responsibility. These models process personally identifiable information, confidential business records, and other sensitive data that need strong protection. You'll want to put strict safeguards in place. Encrypt your data when it's stored and when it moves between systems. Set up detailed access controls so only the right people can see what they need. Use secure storage that you can trust. Here's what's crucial: create clear rules for how long you keep different types of sensitive data. Define specific timeframes, then delete that data securely when you no longer need it. Sensitive information is increasingly collected to create and fine-tune AI and machine learning systems. LLMs can memorize personal information from training data, which raises privacy risks. When you build and follow these practices for handling sensitive data, you'll reduce risks, protect your business, and stay compliant with the regulations that matter to you.
Access and keys
- Use per‑service API keys with least privilege and rotation, implementing a secure system for managing access and keys.
- Restrict SSH/Jupyter access to named engineers, with MFA and short‑lived credentials.
- Keep an allowlist for admin ports; keep inference to HTTPS only.
- Store keys in a secrets manager; never in code or chat logs. Data exfiltration from AI applications poses a significant privacy risk if sensitive data is targeted by attackers.
Subprocessors and contracts
- Sign a Data Processing Agreement (DPA) with vendors that touch prompts/outputs, as the organization is responsible for managing subprocessors and ensuring contractual compliance.
- If data leaves the EEA, ensure valid transfer mechanisms (e.g., SCCs) and document them.
- Keep a public list of subprocessors and change‑notification policy. The EU AI Act prohibits some AI uses outright and implements strict requirements for others.
Risk assessments
You need regular risk assessments when you're using LLM inference. They're vital. These checks help you spot and fix threats to your data privacy and security before they become problems. Look for weak spots like data breaches, unauthorized access, and gaps where your data retention policies don't quite work. Review how you keep records. Make sure retention periods match what the law requires and what your business needs. Can you access records when you need them? Can you delete them? You should be able to do both. Conducting audits is essential to understand personal data processed by LLMs and to ensure compliance with data minimization. When you identify risks step by step and put targeted security measures in place, you'll strengthen how well you meet compliance requirements. You'll reduce the chance of incidents happening. Your data retention practices will stay effective and current.
Transparency and consent
Transparency and consent matter most when you're protecting data in LLM systems. You need to tell people exactly what you're doing with their information—how you collect it, where you store it, and what happens during processing. This includes being upfront about sensitive data handling and storage timelines. Get clear consent before you touch any personal data. People deserve to know your retention policies too—how long you'll keep their data and why you need it. When you focus on transparency and get real consent, you're not just checking boxes for EU regulations. You're building trust with your customers and showing them you actually care about doing data work the right way.
Incident response
- Define what a privacy incident is for your LLM stack, and dedicate appropriate resources to incident response and compliance.
- Keep a 24/7 escalation path and run one tabletop per quarter.
- Pre‑draft customer notifications and regulator checklists to save time.
- After incidents, shorten retention or add gate checks where failures occurred.
Try Compute today
Deploy a vLLM endpoint on Compute in France to keep traffic in‑region. Set strict output caps, log token counts—not text—and measure TTFT/TPS from day one.
Data Protection Officer
You'll want to pick a Data Protection Officer when you're working with LLM systems, especially if you're handling sensitive information. This person keeps your data retention policies on track and makes sure you're following the rules. They also spot risks that come with machine learning. The DPO does regular check-ups on your data practices, puts strong protections in place, and talks to regulators when needed. When you choose someone who knows this stuff, you can handle the rules without stress, show you're taking responsibility, and keep your data practices where they need to be.
A practical GDPR playbook for LLM inference
Keep data in‑region, store less, and lock down access. Log numbers, not text. Set short retention and prove you can find and delete what you store. With those basics in place, you meet users’ expectations and give auditors a clear, repeatable story.
A robust data retention policy is essential for both businesses and consumers, as it addresses privacy concerns and ensures compliance with evolving privacy regulations. The European Commission plays a significant role in shaping regulation, such as the GDPR, which sets strict requirements for data handling and retention. Factors like business requirements, legal mandates, and risk analysis all influence decision making around enterprise data retention, requiring ongoing analysis to balance operational needs with regulatory obligations. Effective management of enterprise data helps businesses meet compliance standards and protect consumers' privacy rights.
Retention of internet data, including metadata and online activities, raises additional privacy concerns due to the involvement of national authorities, security services, and the criminal justice system in surveillance and law enforcement. For example, medical treatment data, such as patient records and photos, may be subject to GDPR requirements, and improper use in AI training datasets can lead to significant privacy concerns for individuals.
FAQ
Is running the endpoint in the EU enough for GDPR?
No. Residency helps, but you still need lawful basis, minimization, security controls, retention limits, and a DSR process.
Are prompts personal data?
Often yes. Prompts can include names, emails, or free‑text that identifies someone. Handle as personal data unless you are certain they do not.
Can we train or fine‑tune on customer prompts?
Only with a lawful basis (e.g., contract or consent) and clear terms. Offer an opt‑out and separate training data from operational logs.
How long should we keep logs?
Short by default—days or a few weeks. Keep longer only with a clear purpose and access controls.
Do we need SCCs if everything stays in the EU?
No, not for EU‑only processing. You need appropriate safeguards when data leaves the EEA.
How do we handle right to erasure with streamed logs?
Log IDs and counts, not content. Use hashed user IDs, keep a mapping table under strict access, and delete matching entries on request.
Do inference vendors act as processors or controllers?
Typically processors when they act on your instructions. Review contracts and document the roles explicitly.
Is this legal advice?
No. It is practical guidance for engineers. Work with counsel for your specific obligations.
