SOC 2 Cost
SOC 2 Cost for AI/Data
SOC 2 budgets for AI/data platforms handling sensitive datasets, model pipelines, and third-party data sources.
Cost range and timeline snapshot
- •Typical AI/Data range: ~$40k–$105k depending on data sensitivity and pipeline complexity.
- •Tooling: logging/monitoring for data pipelines, access control/SSO, vulnerability scanning, vendor/data source reviews.
Timeline bands
- •Readiness: 8–14 weeks if data flows and access patterns are documented.
- •Type I: 3–6 weeks once evidence is consistent.
- •Type II: add 3–9 months observation with sampling across pipelines and environments.
Assumptions
- •Data pipelines and model training environments in scope.
- •Mixed proprietary and third-party data; clear data lineage needed.
- •Type I first; Type II once evidence across pipelines is stable.
Common scope
- •Data ingestion/storage, model training/serving environments.
- •CI/CD for data/ML, feature stores, monitoring/alerting for model services.
- •Vendors/data sources providing datasets or model services.
Top cost drivers
- •Data classification and retention for training/serving data.
- •Access control and approvals for model/code and data stores.
- •Logging/monitoring for pipelines and model endpoints.
- •Vendor/data source due diligence and contracts.
What auditors focus on
- •Access reviews for data stores, feature stores, and model repos.
- •Change control for pipelines and model deployments.
- •Monitoring for drift/incidents and evidence of response.
- •Vendor/data source controls and contractual coverage.
What changes cost most
- •Unclear data lineage or retention requiring rework.
- •Late-added data sources/vendors needing review.
- •Sparse logging/monitoring on pipelines or model endpoints.
Example scenarios
ML API serving customer data
Access control and monitoring depth drive evidence; mid-to-upper budget depending on data sensitivity.
Analytics platform using third-party datasets
Vendor/data source reviews and contracts add effort; cost tied to data classification and retention.
Internal-only model training with limited PII
Leaner scope if data is controlled; logging and access evidence still required for lower cost band.
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