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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.