How do I ensure my text generation app with a foundation model API is compliant?

To ensure a text generation app using a foundation model API is compliant, treat compliance as a design, policy, and operations problem—not just a legal one. The exact obligations depend on your jurisdiction, model provider, and use case, but this checklist will get you started.

1) Identify the regulations and contracts that apply

Determine which of these govern your app:

• Privacy laws: GDPR, UK GDPR, CCPA/CPRA, etc.
• AI-specific rules: EU AI Act, sector rules, consumer protection laws.
• Content laws: copyright, defamation, hate speech, election/medical/financial claims.
• Data processing agreements: your API provider’s terms, DPA, SCCs, retention rules.
• Industry rules: HIPAA, FERPA, PCI DSS, FINRA, etc., if relevant.

2) Map your data flows

Document:

• What user data you collect
• What you send to the model API
• Whether prompts contain personal, sensitive, or regulated data
• Where data is stored, for how long, and who can access it
• Whether the provider uses data for training, logging, or abuse monitoring

This is essential for privacy notices, consent, retention, and security controls.

3) Minimize and classify data

• Avoid sending unnecessary personal or sensitive data to the model.
• Redact or tokenize identifiers where possible.
• Classify data by sensitivity:
  • public
  • internal
  • confidential
  • regulated/sensitive
• Block or restrict use of highly sensitive data unless you have a clear lawful basis and safeguards.

4) Put user consent and notice in place

Your app should clearly tell users:

• They are interacting with an AI system
• What data is collected and why
• Whether their inputs may be stored or reviewed
• Whether outputs may be inaccurate
• Any limitations or prohibited uses

If required, obtain:

• privacy consent
• cookie consent
• explicit consent for sensitive data processing
• parental consent where applicable

5) Add content and safety controls

Implement guardrails to reduce harmful or illegal outputs:

• Prompt injection protection
• Toxicity / hate / harassment filters
• Self-harm, sexual content, violence, and fraud detection
• Restricted topics handling
• Output moderation before display
• Rate limits and abuse prevention

Also define what the app should do when it cannot safely answer:

• refuse
• redirect
• provide safe alternatives
• escalate to human review

6) Human oversight and escalation

For high-risk uses, ensure:

• Human review for sensitive decisions
• Ability to override or correct model outputs
• Escalation paths for user complaints and safety incidents
• Clear accountability for final decisions

Do not let the model make fully automated decisions in regulated domains without review, if the law requires oversight.

7) Validate output quality and misuse risk

Before launch and continuously:

• Test for hallucinations
• Test for bias and discriminatory outputs
• Test for unsafe instructions
• Red-team jailbreaks and prompt injection
• Measure false positives/negatives in moderation
• Keep evaluation records

8) Security and access controls

Use standard security hygiene:

• Encrypt data in transit and at rest
• Strong authentication and role-based access control
• Secrets management for API keys
• Logging with sensitive data masking
• Regular vulnerability scanning and patching
• Incident response procedures

9) Keep records and audit trails

Maintain documentation for:

• System purpose and scope
• Model/version used
• Safety tests and evaluations
• Policies and moderation rules
• User complaints and incident logs
• Data retention and deletion policies
• Vendor assessments and contract reviews

Good records help with audits and incident response.

10) Respect intellectual property and attribution

Be careful with:

• copyrighted training or prompt input data
• outputs that may reproduce protected text
• brand/trademark misuse
• license obligations for any source content you use
• user ownership of inputs/outputs, as defined in your terms

Add policies prohibiting infringement and implement filters or checks where needed.

11) Set retention and deletion policies

Define:

• How long prompts, outputs, and logs are stored
• When they are deleted or anonymized
• How users can request deletion
• How backups are handled
• Whether data is retained for abuse prevention and under what basis

12) Review provider compliance posture

Check your foundation model provider for:

• Data processing terms
• Regional hosting options
• Training-on-your-data defaults
• Security certifications
• Content moderation features
• Model cards / safety documentation
• Subprocessor lists
• Data deletion support

Don’t assume the provider’s safeguards replace your own obligations.

13) Build a governance process

Assign roles for:

• legal/compliance review
• security review
• product approval
• incident response
• model updates and re-testing

Create a release checklist so new prompts, model versions, or features can’t ship without review.

14) Prepare user-facing policies

You’ll usually need:

• Privacy policy
• Terms of service
• Acceptable use policy
• AI disclosure / transparency notice
• Content moderation or appeal policy
• Contact path for complaints

15) Monitor after launch

Compliance is ongoing:

• Track usage and safety incidents
• Review sample outputs regularly
• Update filters and policies
• Reassess when laws, models, or features change
• Retrain staff on escalation procedures

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Practical “minimum viable compliance” checklist

If you want a concise starting point, make sure you have:

• A privacy notice
• Data minimization/redaction
• Provider DPA/terms reviewed
• Content moderation and refusal logic
• Logging with PII masking
• Retention/deletion policy
• Human escalation process
• Security controls and access management
• Evaluation/red-team testing
• Release/change approval process

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If you want, I can turn this into a compliance checklist tailored to your jurisdiction (e.g., EU/US/UK) or to your app type, such as customer support bot, writing assistant, or healthcare/finance tool.