When we started the SOC 2 certification process at Bolor Intelligence, we discovered that existing SOC 2 guidance is written for traditional SaaS companies. The Trust Service Criteria — security, availability, processing integrity, confidentiality, and privacy — make sense for conventional software, but they do not directly address the unique challenges of AI systems. How do you demonstrate processing integrity when your system uses probabilistic models? How do you ensure availability when your infrastructure depends on third-party model providers? How do you maintain confidentiality when training data might be memorized by models? We had to figure out the answers, and this guide shares what we learned.
The first and most fundamental challenge is model governance. SOC 2 requires that your processing is accurate, complete, and timely. For a traditional SaaS product, this means your code does what it is supposed to do and produces correct results. For an AI product, this is much harder to demonstrate because model outputs are probabilistic, not deterministic. The same input can produce different outputs on different runs. We addressed this by implementing a model registry that tracks every model version, its evaluation metrics, approval status, and deployment history. Every model in production has documented accuracy benchmarks on standardized test sets. Every model change goes through a formal approval process with automated regression testing. Our audit trail shows exactly which model version produced which output, and our test results demonstrate that model accuracy meets defined thresholds.
The second challenge is data lineage. SOC 2 confidentiality requirements mean you need to know where your data comes from, where it goes, and who has access to it. For AI companies, this extends to training data: what data was used to train or fine-tune your models, where did that data come from, did you have the right to use it, and could any of it be reproduced from model outputs? We implemented comprehensive data lineage tracking that follows every piece of data from ingestion through processing, storage, model training, and output generation. We can show auditors exactly which data sources contributed to any model version and demonstrate that appropriate data handling procedures were followed at every step.
The third challenge is algorithmic accountability. If an AI system makes a decision that affects a customer — approving a loan, flagging a transaction, recommending a treatment — you need to be able to explain why that decision was made. SOC 2 processing integrity requires that processing is authorized, complete, and accurate. For AI systems, we interpret this to mean that decisions should be explainable and that the decision-making process should be auditable. This is where neuro-symbolic architecture really proves its value. Our symbolic reasoning layer produces explicit reasoning chains for every decision: which rules were applied, which knowledge was retrieved, which conditions were evaluated. These chains are logged and can be reviewed by auditors or presented to affected parties.
Change management for AI systems requires special attention. SOC 2 requires formal change management procedures, but AI systems change in ways that traditional software does not. Models are retrained or fine-tuned. Knowledge bases are updated. Routing rules are adjusted. Prompt templates are modified. Each of these changes can alter system behavior in significant ways, and each needs to be tracked, tested, and approved. We implemented tiered change management: changes to model weights require full regression testing and multi-person approval, changes to knowledge bases require semantic diff review, changes to prompts require A/B testing before full deployment, and changes to configuration require standard code review.
Incident response for AI systems has unique considerations. When a traditional system has an incident, you know what happened because the system is deterministic. When an AI system produces incorrect output, the root cause might be a model error, a data quality issue, a prompt injection, an unexpected input distribution, or a combination of factors. Our incident response process includes model-specific diagnostic procedures: capturing the exact model version, input data, intermediate reasoning steps, and output for every incident. We maintain a searchable incident database that helps identify patterns — if a specific type of query consistently produces incorrect results, that signals a model issue rather than a one-off error.
Third-party model providers add another layer of complexity. If you use OpenAI, Anthropic, or other providers, their systems are part of your SOC 2 scope. You need to assess their security posture, understand their data handling practices, and manage the risk that their service disruptions affect your availability. We addressed this by treating model providers as critical vendors with formal risk assessments, SLA monitoring, and failover procedures. Our multi-model routing architecture with OrchestrAI means that if one provider goes down, we automatically failover to another, maintaining our availability commitments.
Privacy requirements for AI are evolving rapidly, and SOC 2 privacy criteria are a moving target for AI companies. We took a conservative approach: all customer data is encrypted at rest and in transit, customer data is never used for model training without explicit consent, and we have implemented technical controls to prevent model memorization of sensitive data. Our data processing agreements explicitly address AI-specific privacy considerations, and our privacy impact assessments include model-specific analysis of data exposure risks.
The tooling gap for AI compliance is real, which is why we built ComplianceGraph as both an internal tool and a product. Most GRC (governance, risk, and compliance) platforms are designed for traditional software compliance. They do not have concepts for model versions, training data lineage, algorithmic explanations, or AI-specific incident categories. ComplianceGraph fills this gap by providing compliance rule engines, audit logging, and policy management specifically designed for AI systems. We use it internally for our own SOC 2 compliance, and we offer it to our customers who face the same challenges.
If you are an AI startup beginning the SOC 2 journey, here is our advice: start early, document everything from day one, implement model governance before you think you need it, and invest in explainability as a core architecture principle rather than an afterthought. The companies that treat compliance as a feature rather than a burden will have a significant advantage as enterprise buyers increasingly require SOC 2 (and eventually SOC 2 + AI-specific certifications) from their AI vendors.