How do I ensure my orbit prediction with an orbital data platform is compliant with customer data policies?

To keep orbit prediction usage compliant with customer data policies, treat the orbital data platform like any other data-processing system: identify what data you ingest, what you compute, where it’s stored, who can access it, and what you can disclose or retain.

Here’s a practical checklist:

1) Classify the data first

Determine whether the orbital data includes any of the following:

• Customer proprietary mission data
• Personally identifiable information (PII)
• Export-controlled technical data
• Confidential operational data
• Third-party licensed ephemeris or telemetry

If the platform receives only public TLEs or open datasets, the policy burden is lower, but not zero if your outputs are combined with customer context.

2) Map allowed use cases

Check the customer policy for:

• Permitted purposes: operations, analytics, simulation, maintenance, etc.
• Prohibited purposes: resale, training public models, sharing with affiliates, competitive use
• Geographic restrictions: data residency or cross-border transfer limits
• Retention limits: how long raw data and predictions may be stored
• Derivative-data rules: whether predictions/counts/alerts are considered customer data

3) Minimize what you ingest

Only send the platform the data needed for orbit prediction:

• Prefer anonymized or tokenized identifiers
• Avoid unnecessary telemetry fields
• Redact mission-sensitive metadata if not required for the computation

4) Control where data goes

Confirm:

• Hosting region and backups
• Subprocessors or third-party APIs
• Whether the provider uses data for its own model improvement or service analytics
• Encryption in transit and at rest
• Whether customer data is logically isolated or mixed with other tenants

5) Define ownership of outputs

Customer policies often treat derived outputs as customer data if they are created from customer inputs. Clarify:

• Who owns predicted ephemerides, conjunction alerts, covariance outputs, and reports
• Whether outputs can be shared outside the customer organization
• Whether the platform can retain or reuse outputs

6) Put retention and deletion in writing

Ensure you can:

• Set retention periods
• Delete raw inputs, intermediate artifacts, and outputs
• Verify deletion on request
• Preserve audit logs without retaining sensitive payloads longer than allowed

7) Log access and changes

Maintain auditable records of:

• Who accessed the data
• What was sent to the platform
• Which model/version generated the prediction
• When the prediction was produced
• Any manual overrides or exports

8) Review legal and security requirements

Depending on your customer and jurisdiction, you may need:

• Data Processing Agreement (DPA)
• Standard Contractual Clauses or cross-border transfer mechanism
• Export-control review
• Security review or vendor assessment
• Incident response and breach notification terms

9) Validate vendor terms

Read the platform’s:

• Terms of service
• Privacy policy
• Data use policy
• API terms
• Enterprise agreement
• SLA and support policy

Look specifically for language about:

• Training on your data
• Sharing with affiliates/subprocessors
• Data ownership
• Deletion commitments
• Audit rights

10) Create an internal usage policy

Write a short internal standard that says:

• What orbital data may be uploaded
• Who may access the platform
• Approved regions/vendors
• Required redaction steps
• Approval process for new data types
• Retention and deletion rules

11) If needed, separate sensitive and non-sensitive processing

A common pattern is:

• Use the orbital data platform only for non-sensitive propagation
• Keep customer-specific mission planning or sensitive analytics in an internal environment
• Recombine only approved outputs

12) Get formal approval

Before production use, obtain sign-off from:

• Legal
• Security
• Privacy/compliance
• Customer representative if required

Quick compliance questions to ask

• Is this data customer-owned or customer-derived?
• Does the platform have the right to reuse it?
• Where is it stored and processed?
• Are predictions considered customer data?
• Can we delete everything on request?
• Are there export-control or residency issues?
• Do we have a signed DPA/vendor agreement?

If you want, I can help you turn this into a customer data compliance checklist or a vendor review questionnaire for an orbital data platform.