How do I ensure my similarity matching workflow with an embedding API is compliant?

To make a similarity-matching workflow using an embedding API compliant, focus on data rights, user consent, minimization, retention, security, and vendor terms. Compliance usually depends on your jurisdiction and use case, so treat this as a practical checklist rather than legal advice.

1) Define what data you’re embedding

• Identify whether inputs contain:
  • personal data
  • sensitive data
  • regulated data (health, finance, education, biometrics, etc.)
• Classify use cases:
  • search/retrieval
  • deduplication
  • fraud detection
  • recommendations
  • identity matching

2) Check your legal basis

Depending on your region, you may need a lawful basis such as:

• consent
• contract necessity
• legitimate interests
• legal obligation

If embedding personal data, ensure you can justify:

• why you need it
• why embeddings are necessary
• why a less intrusive method won’t work

3) Minimize what you send

• Only embed the minimum text needed
• Remove or mask:
  • names
  • emails
  • phone numbers
  • account IDs
  • addresses
  • free-text sensitive disclosures
• Avoid sending raw documents if snippets or extracted features are enough
• Prefer pseudonymized or redacted inputs

4) Tell users clearly

Update:

• privacy policy
• terms of service
• consent flows if applicable

Explain:

• what data is processed
• why it’s processed
• whether a third-party model/API is involved
• whether data is stored or only processed transiently
• how users can exercise their rights

5) Review vendor terms and data handling

For the embedding API provider, verify:

• data processing agreement (DPA) availability
• whether they use your data for training
• retention period
• logging practices
• subprocessors
• cross-border transfer mechanisms
• security certifications or controls

If the provider retains prompts/inputs, make sure that aligns with your obligations.

6) Put security controls in place

• encrypt data in transit and at rest
• restrict access with least privilege
• log access to embedding pipelines
• separate identifiers from content where possible
• protect vector stores, which can still reveal sensitive info
• monitor for re-identification risks

7) Handle special categories carefully

If you embed sensitive or regulated data:

• do a DPIA/PIA if required
• use stronger access controls
• consider on-prem or dedicated deployment options
• get explicit consent where required
• avoid using such data unless truly necessary

8) Set retention and deletion rules

Define:

• how long source text is kept
• how long embeddings are kept
• when embeddings must be deleted
• whether deleted source data requires embedding reindexing or purge

Remember: embeddings may still be considered derived personal data if they can be linked back to an individual.

9) Support user rights

Build processes for:

• access
• correction
• deletion
• objection/opt-out
• portability, if applicable

Also consider how you will:

• locate embeddings tied to a user
• delete them from vector indexes, caches, backups, and logs

10) Avoid using embeddings for prohibited purposes

Be cautious with:

• employment decisions
• credit decisions
• face recognition or biometric identification
• sensitive profiling
• automated decisions with legal or similarly significant effects

These often trigger extra obligations or restrictions.

11) Document your workflow

Keep records of:

• data categories
• purpose
• lawful basis
• vendors
• security measures
• retention schedule
• risk assessments
• deletion procedures

This helps demonstrate accountability.

12) Get legal/privacy review for high-risk cases

You should involve counsel or a privacy professional if you are:

• processing personal data at scale
• handling children’s data
• using sensitive categories
• transferring data internationally
• making automated decisions
• building identity or matching systems

Quick compliance checklist

Before production, confirm:

• data is necessary and minimized
• lawful basis established
• privacy notice updated
• vendor DPA/terms reviewed
• no unintended training use
• retention/deletion defined
• user rights supported
• security controls implemented
• sensitive data reviewed separately
• risk assessment completed

If you want, I can turn this into:

1. a GDPR-focused checklist,
2. a HIPAA-focused checklist, or
3. a technical architecture pattern for compliant embedding + vector search.