Summary

Artificial intelligence companies face a unique compliance challenge. You’re building products that process sensitive data, make consequential decisions, and operate in ways that customers genuinely struggle to audit on their own. That’s exactly why SOC 2 Type II certification has become a near-mandatory trust signal for AI companies selling to enterprise customers. Security is mandatory. The other four criteria — Availability, Processing Integrity, Confidentiality, and Privacy — are optional but commonly expected for AI products. The single biggest bottleneck for most AI companies pursuing SOC 2 Type II is documentation. Writing policies, procedures, and control frameworks from scratch takes hundreds of hours and requires specialized expertise.

SOC 2 Type II for AI Companies: A Complete How-To Guide

This guide walks you through exactly what SOC 2 Type II means, why it matters specifically for AI businesses, and the concrete steps you need to take to earn — and maintain — your certification.

What Is SOC 2 Type II and Why Does It Matter for AI?

SOC 2 (System and Organization Controls 2) is an auditing framework developed by the American Institute of Certified Public Accountants (AICPA). It evaluates how a company manages customer data based on five Trust Services Criteria: Security, Availability, Processing Integrity, Confidentiality, and Privacy.

Type II is the more rigorous version. Unlike Type I, which is a point-in-time snapshot, Type II covers an observation period — typically 6 to 12 months — demonstrating that your controls actually work consistently over time.

For AI companies specifically, SOC 2 Type II matters because:

Enterprise buyers increasingly require it before signing contracts
Your models likely process confidential customer data used for training or inference
Regulators and customers want evidence that your AI outputs are governed by auditable processes
It differentiates you from competitors who only have self-reported security claims

How AI Companies Are Different from Traditional SaaS

Before diving into the process, it’s worth acknowledging what makes AI companies uniquely complex in a compliance context.

Data Pipelines Are More Complicated

AI products often ingest, transform, and store data in ways that span multiple environments — raw data lakes, feature stores, model training infrastructure, and inference APIs. Each layer is a potential control gap.

Model Behavior Is a New Risk Category

Traditional software either does something or it doesn’t. AI models produce probabilistic outputs, which introduces questions about processing integrity — one of the five Trust Services Criteria — that auditors are increasingly asking about.

Third-Party AI Services Add Scope

If you’re calling OpenAI, Anthropic, Google Vertex, or similar APIs, your auditor will want to understand how third-party data handling fits into your overall control environment.

Step-by-Step: How to Get SOC 2 Type II as an AI Company

Step 1: Define Your Scope

Start by clearly documenting which systems, services, and data flows are in scope for the audit. For AI companies, this typically includes:

Your core application and APIs
Data ingestion and preprocessing pipelines
Model training and fine-tuning environments
Inference infrastructure
Any customer-facing dashboards or portals
Cloud environments (AWS, GCP, Azure)

Scoping too broadly wastes resources. Scoping too narrowly creates audit gaps that damage credibility.

Step 2: Choose Your Trust Services Criteria

Security is mandatory. The other four criteria — Availability, Processing Integrity, Confidentiality, and Privacy — are optional but commonly expected for AI products.

Recommendations for AI companies:

Processing Integrity: Strongly recommended if your AI outputs drive customer decisions
Confidentiality: Essential if you handle proprietary customer data for model training
Privacy: Required if you process personal data, especially under GDPR or CCPA obligations
Availability: Include this if uptime SLAs are part of your customer contracts

Step 3: Conduct a Readiness Assessment

Before engaging a formal auditor, perform an internal gap analysis. This means comparing your current controls against the AICPA’s Trust Services Criteria and identifying where you fall short.

Common gaps found in AI companies include:

Informal access controls to model training data
No documented change management process for model updates
Missing vendor risk assessments for third-party AI APIs
Inadequate logging and monitoring of inference endpoints
Lack of formal incident response procedures

Step 4: Implement and Document Your Controls

This is where most of the real work happens. For each identified gap, you need to implement a control and — critically — document it.

Key control areas to address:

Access management: Role-based access, MFA, privileged access reviews
Encryption: Data at rest and in transit, key management procedures
Change management: Code review, deployment approvals, model versioning
Monitoring and alerting: SIEM tools, anomaly detection, log retention policies
Vendor management: Security assessments for all third-party AI providers
Incident response: Documented plan, tabletop exercises, communication procedures
Business continuity: Backup procedures, recovery time objectives, testing

Step 5: Select a CPA Auditing Firm

Only licensed CPA firms can issue SOC 2 reports. Choose a firm with experience auditing technology companies and, ideally, AI or ML-focused businesses.

Look for auditors who:

Understand cloud-native infrastructure
Have experience with AI/ML data pipelines
Can articulate how they’ll evaluate processing integrity for probabilistic systems
Offer a readiness assessment phase before the formal audit

Step 6: Begin Your Observation Period

Once your controls are in place, your observation period begins. This is typically 6 to 12 months during which your auditor collects evidence that controls are operating effectively.

During this period, you must:

Consistently follow every documented procedure
Collect and retain evidence (logs, access reviews, change tickets, training records)
Conduct required periodic reviews (e.g., quarterly access reviews, monthly vulnerability scans)
Respond to any incidents according to your documented procedures

Do not wait until the audit to gather evidence. Build evidence collection into your operational workflows from day one of the observation period.

Step 7: Complete the Audit and Receive Your Report

At the end of the observation period, your auditor reviews all collected evidence and issues a SOC 2 Type II report. The report includes:

A description of your system
The auditor’s opinion on whether controls were suitably designed
The auditor’s opinion on whether controls operated effectively
Any identified exceptions or deviations

A clean report with no exceptions is the goal, but minor exceptions with appropriate management responses are common and not necessarily deal-breakers for customers.

Timeline and Cost Expectations

Phase	Typical Duration	Estimated Cost
Readiness Assessment	4–8 weeks	$5,000–$20,000
Control Implementation	2–6 months	Internal + tooling costs
Observation Period	6–12 months	Ongoing operational costs
Formal Audit	4–8 weeks	$20,000–$60,000+

Total first-year investment for a mid-sized AI startup typically ranges from $50,000 to $150,000, including personnel time, tooling, and auditor fees.

Maintaining SOC 2 Type II Compliance

SOC 2 Type II is not a one-time achievement. You’ll need annual audits to maintain your certification. Build these habits into your organization:

Assign a dedicated compliance owner or team
Automate evidence collection using tools like Vanta, Drata, or Secureframe
Conduct quarterly internal control reviews
Update your control documentation whenever systems or processes change
Train new employees on security and compliance procedures

Frequently Asked Questions

How long does it take to get SOC 2 Type II as an AI startup?

From starting your readiness assessment to receiving your final report, expect 12 to 18 months for most AI companies. The observation period alone is 6 to 12 months. If you’re under customer pressure, a SOC 2 Type I report (point-in-time) can be issued faster while you work toward Type II.

Do I need to include my AI model training environment in scope?

It depends on whether customer data touches that environment. If you use customer data — even anonymized — for training or fine-tuning, your auditor will almost certainly want it in scope. Be transparent with your auditor about all data flows from the beginning.

What happens if a third-party AI API I use doesn’t have its own SOC 2?

You’ll need to perform and document a vendor risk assessment for that provider. Obtain whatever security documentation they offer (penetration test summaries, security whitepapers, contractual data processing agreements) and document your risk acceptance decision. This is increasingly common as many AI API providers are newer companies without mature compliance programs.

Can a small AI startup realistically achieve SOC 2 Type II?

Absolutely. Many Series A and even seed-stage AI companies pursue SOC 2 Type II because enterprise customers require it. The key is starting early, using compliance automation tools to reduce manual effort, and leveraging pre-built policy templates rather than writing everything from scratch.

What’s the difference between SOC 2 Type II and ISO 27001 for AI companies?

SOC 2 is more common among US-based buyers and is attestation-based (an auditor’s opinion). ISO 27001 is a certification standard more prevalent in European markets. Many AI companies eventually pursue both. If your primary market is US enterprise, start with SOC 2. If you’re targeting European enterprise customers, ISO 27001 may need to come first or in parallel.

Start Your SOC 2 Journey Faster with Ready-to-Use Templates

The single biggest bottleneck for most AI companies pursuing SOC 2 Type II is documentation. Writing policies, procedures, and control frameworks from scratch takes hundreds of hours and requires specialized expertise.

Our SOC 2 compliance template library gives you a head start with:

Pre-written information security policies mapped to Trust Services Criteria
AI-specific addenda covering model governance, training data controls, and inference monitoring
Vendor risk assessment questionnaires
Incident response plan templates
Access review and change management procedure documents
Evidence collection checklists for the full observation period

These templates are written by compliance professionals, reviewed by former Big Four auditors, and used by AI companies at every stage from seed to Series C.

[Browse our SOC 2 Template Library →] and cut months off your compliance timeline today.