AI TRAINING
SOC 2 Compliance for AI and ML Products
Equip your team to design, evidence, and defend SOC 2 controls across AI and ML pipelines.
What it covers
This practitioner-level programme covers how SOC 2 Trust Services Criteria apply to AI and ML systems, including model training pipelines, inference infrastructure, and third-party AI vendor integrations. Participants learn to map AI-specific risks to SOC 2 controls, build audit-ready evidence packages, and engage confidently with auditors on topics like data provenance, model drift, and automated decision-making. Sessions combine control design workshops with real audit scenarios drawn from AI product environments. By the end, teams can assess gaps, assign control ownership, and produce documentation that withstands Type II scrutiny.
What you'll be able to do
- Map each SOC 2 Trust Services Criterion to concrete controls within your organisation's AI and ML pipelines
- Produce an audit-ready evidence package covering model lifecycle, data handling, and access controls
- Conduct a structured third-party AI vendor risk assessment aligned to SOC 2 vendor management requirements
- Design monitoring and alerting procedures for model drift and inference anomalies that satisfy auditor expectations
- Identify and remediate the top five control gaps that cause AI companies to fail or receive qualified SOC 2 Type II opinions
Topics covered
- Mapping SOC 2 Trust Services Criteria (Security, Availability, Confidentiality, Processing Integrity, Privacy) to AI/ML contexts
- Control design for model training pipelines and feature stores
- Third-party AI vendor risk assessment and due diligence
- Data provenance, lineage, and retention controls for training data
- Model change management, version control, and rollback evidence
- Monitoring and alerting for model drift and anomalous inference behaviour
- Audit evidence collection: logs, dashboards, and approval records for ML workflows
- Incident response and breach notification obligations in AI-enabled products
Delivery
Delivered as a 2–3 day instructor-led programme, available in-person or live virtual. Each session is roughly 60% hands-on: participants work on their own control matrices and evidence templates using provided AI-specific SOC 2 workbooks. A pre-work questionnaire captures participants' current tech stack and auditor relationship status so examples are tailored. Remote delivery uses Miro for collaborative control mapping and a shared document workspace for evidence artefact drafting. Physical delivery includes printed workbooks and a half-day tabletop audit simulation on day two.
What makes it work
- Assign a named control owner for each AI pipeline stage before the audit window opens
- Automate evidence collection from CI/CD and model registry tools so logs are audit-ready without manual effort
- Conduct an internal readiness review at the 60-day mark of the observation period using the same criteria an auditor would apply
- Maintain a living third-party AI vendor inventory updated at every contract renewal or model version change
Common mistakes
- Treating AI vendors as generic SaaS vendors and failing to assess model training data access and output data retention separately
- Using generic SOC 2 control templates that never mention ML pipelines, leaving auditors unable to test against actual system behaviour
- Assigning all AI-related controls to engineering without involving compliance or legal, creating ownership gaps that surface during fieldwork
- Collecting point-in-time screenshots as evidence rather than continuous log exports, which fails Type II coverage requirements
When NOT to take this
This programme is not the right fit for teams that have not yet chosen a cloud infrastructure or AI stack — the control design exercises require a concrete system to map against, and teams still in ideation will derive little actionable value.
Providers to consider
Sources
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