Summary

Security is mandatory. For ML companies, strongly consider adding:

SOC 2 for Machine Learning: How to Get Certified and What You Need to Know

Machine learning companies handle sensitive data at scale — training datasets, model outputs, customer inputs, and inference logs. If you’re building ML-powered products and selling to enterprise customers, you’ve almost certainly heard the question: “Do you have SOC 2?” Getting SOC 2 certified as a machine learning company comes with unique challenges that standard compliance guides don’t address. This article walks you through exactly what you need to do.

What Is SOC 2 and Why Do ML Companies Need It?

SOC 2 (System and Organization Controls 2) is an auditing framework developed by the American Institute of CPAs (AICPA). It evaluates how a company manages customer data across five Trust Services Criteria:

Security (required)
Availability
Processing Integrity
Confidentiality
Privacy

For machine learning companies, SOC 2 is increasingly non-negotiable. Enterprise buyers run security reviews before signing contracts, and a SOC 2 Type II report is often the minimum bar. Beyond sales enablement, SOC 2 forces you to build real security discipline around your ML pipelines — which reduces your actual risk.

The Two Types of SOC 2 Reports

Before starting, understand which report you’re pursuing:

SOC 2 Type I evaluates whether your controls are designed appropriately at a single point in time. It’s faster to obtain (typically 2–3 months) and useful for early-stage companies that need to show customers something quickly.

SOC 2 Type II evaluates whether your controls operated effectively over an observation period, typically 6–12 months. This is the gold standard that most enterprise customers require.

Most ML companies should target Type II, but starting with Type I is a legitimate stepping stone.

Unique SOC 2 Challenges for Machine Learning Companies

Standard SOC 2 guidance assumes a relatively static software environment. ML systems don’t work that way. Here’s what makes ML compliance harder:

Dynamic Data Pipelines

Training data flows through multiple systems — data lakes, feature stores, labeling platforms, and model registries. Each handoff is a potential control gap that auditors will scrutinize.

Third-Party Data and Model Dependencies

Many ML teams use pre-trained foundation models, third-party datasets, or external APIs for inference. Your SOC 2 scope must account for vendor risk across all of these.

Experiment Tracking and Versioning

Model experiments generate enormous volumes of logs and artifacts. You need policies governing what gets retained, who can access it, and how long it lives.

Inference Infrastructure

Production ML systems often run on auto-scaling GPU clusters. Your access controls, logging, and change management processes must cover this infrastructure — not just your application layer.

Step-by-Step: How to Get SOC 2 for Your ML Company

Step 1: Define Your Scope

Scope is everything. Work with your team to identify:

Which systems process customer data (training pipelines, APIs, databases, dashboards)
Which ML-specific tools fall inside scope (MLflow, Weights & Biases, SageMaker, Vertex AI, etc.)
Which third-party vendors are subprocessors

Narrowing scope reduces audit cost and timeline. Don’t include systems that don’t touch customer data.

Step 2: Choose the Right Trust Services Criteria

Security is mandatory. For ML companies, strongly consider adding:

Processing Integrity — ensures your models produce accurate, complete outputs. This is especially relevant for ML systems where model drift or data quality issues can silently corrupt results.
Confidentiality — critical if you train on proprietary customer data or handle sensitive business information.
Privacy — required if your ML system processes personal data, especially under regulations like GDPR or CCPA.

Step 3: Conduct a Readiness Assessment

Before engaging an auditor, perform an internal gap assessment. Compare your current controls against the SOC 2 criteria and identify what’s missing. Common gaps for ML companies include:

No formal model change management process
Training data access not logged or reviewed
No vendor risk assessments for ML tool providers
Incomplete incident response procedures covering model failures

Step 4: Build and Document Your Controls

This is the most time-intensive phase. You need written policies, technical controls, and evidence that both exist. Key controls for ML environments include:

Access Control

Role-based access to training data and model artifacts
Privileged access reviews for ML infrastructure
MFA enforced across all ML platforms

Change Management

Documented model deployment process with approvals
Version control for training code and model weights
Rollback procedures for failed model deployments

Data Management

Data classification policy covering training datasets
Encryption at rest and in transit for all data pipelines
Data retention and deletion schedules

Monitoring and Logging

Centralized logging for all ML infrastructure
Model performance monitoring with alerting thresholds
Security event monitoring with defined response SLAs

Vendor Management

Vendor risk assessments for all ML tool providers
Review of subprocessor SOC 2 reports annually

Step 5: Select a SOC 2 Auditor

Only licensed CPA firms can issue SOC 2 reports. When evaluating auditors:

Ask specifically about experience auditing ML or AI companies
Compare pricing (Type II audits typically range from $15,000–$50,000+)
Understand their evidence collection process and timeline
Check references from similar-stage companies

Step 6: Complete the Observation Period (Type II)

Once your controls are in place, the clock starts on your observation period. During this time:

Controls must operate consistently — auditors will pull samples
Any exceptions need to be documented and explained
Avoid major infrastructure changes that could disrupt evidence continuity

Typical observation periods are 6 months for a first-time Type II audit.

Step 7: Support the Audit and Receive Your Report

Your auditor will request evidence — screenshots, logs, access review records, policy documents, vendor assessments. Having organized documentation dramatically reduces the burden here.

After reviewing evidence and conducting interviews, your auditor issues the SOC 2 report. The report includes an opinion letter and a detailed description of your controls and any exceptions.

How Long Does SOC 2 Take for an ML Company?

Path	Timeline
Type I only	3–5 months
Type I then Type II	10–14 months total
Type II directly	9–14 months

These timelines assume you start with significant gaps. Well-prepared companies with existing security infrastructure can move faster.

Tools That Can Help

Several compliance automation platforms can accelerate SOC 2 preparation:

Vanta — continuous monitoring and evidence collection
Drata — automated control testing with integrations for AWS, GCP, and Azure
Secureframe — popular with startups, includes policy templates

These tools reduce manual effort but don’t eliminate the need for well-written policies and procedures.

FAQ: SOC 2 for Machine Learning Companies

Can we use open-source models and still get SOC 2 certified?

Yes. Using open-source models doesn’t disqualify you from SOC 2. However, you need to document your process for vetting open-source dependencies, managing model updates, and ensuring the models don’t introduce security vulnerabilities into your environment.

Does SOC 2 cover model bias or AI fairness?

SOC 2 does not directly address AI fairness or algorithmic bias. However, the Processing Integrity criteria does evaluate whether your system produces accurate and complete outputs, which can partially overlap with model quality concerns. AI-specific frameworks like the NIST AI RMF address bias more directly.

What if we use customer data to train models?

This is a significant scope consideration. If you train on customer data, you need strong controls around data isolation, consent documentation, and data deletion upon request. The Privacy criteria becomes highly relevant, and you should work with legal counsel to ensure your data processing agreements align with your SOC 2 commitments.

How much does SOC 2 cost for an ML startup?

Total costs typically range from $30,000 to $100,000+ for a first-time Type II audit, including auditor fees, compliance tooling subscriptions, and internal engineering time. The investment pays off quickly when it unblocks enterprise deals.

Do we need SOC 2 if we only use our own data for training?

If your product’s inference API processes customer inputs — even if training data is proprietary — enterprise customers will still require SOC 2. The question is whether their data touches your systems, not just whether you trained on external data.

Start Your SOC 2 Journey With the Right Foundation

The biggest bottleneck for most ML companies isn’t the audit itself — it’s arriving at the audit without proper documentation. Writing policies from scratch is slow, inconsistent, and expensive when done with legal or consultant time.

Our ready-to-use SOC 2 compliance template library is built specifically for technology and ML companies. You get:

Pre-written policy templates covering all five Trust Services Criteria
ML-specific controls for data pipelines, model management, and inference infrastructure
Vendor assessment templates for ML tool providers
Evidence collection checklists organized by control category
Gap assessment worksheets to identify your starting point

Skip months of writing and get audit-ready faster. Browse our SOC 2 template packages today and give your team the documentation foundation they need to get certified with confidence.