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SOC 1 for AI refers to the examination of controls over AI-enabled services that could affect a customer's internal control over financial reporting (ICFR). It applies the existing SOC 1 reporting framework to relevant AI systems rather than creating a separate AI-specific standard.
A SOC 1 examination is performed by an independent CPA firm under AICPA AT-C Section 320. A Type 1 report addresses the suitability of control design at a specified date; a Type 2 report also addresses operating effectiveness over a specified period.
An AI agent that processes invoices, updates accounting records, or initiates financial transactions may introduce risks relevant to customers' financial reporting. Controls over its access, actions, outputs, and changes may therefore fall within the scope of a SOC 1 examination.
SOC 1 is relevant when an AI-enabled service performs functions that could materially affect a customer's financial reporting controls. Using AI alone does not create a SOC 1 requirement.
Examples include AI-powered invoice processing, autonomous agents that initiate accounting transactions, AI-assisted payroll processing, and financial data transformation or reconciliation. The determining factor is the service's effect on financial reporting, not the technology used. For each workflow, assess which financial records the AI can create or change, who authorizes its actions, what independent checks detect errors, and what evidence is available to the customer and service auditor.
SOC 1 does not prescribe a universal checklist of AI-specific controls. Organizations identify control objectives and procedures based on their services, relevant financial-reporting risks, and system boundaries. A useful way to connect these ideas is to trace each financial-reporting risk to a preventive or detective control and then to evidence that the control operated. COSO's internal-control framework and the voluntary NIST AI Risk Management Framework can help teams organize risk assessments, but neither substitutes for the SOC 1 examination standard.
AI agents and connected services should have identifiable identities and appropriately restricted access to financial applications. Controls may include least-privilege permissions, access approvals, periodic access reviews, and timely revocation. An invoice-processing agent, for example, may be allowed to create draft payables but not to approve payments or change vendor bank details.
The ability to execute a transaction should not automatically confer authority to approve it. Approval workflows, transaction limits, and separation of duties help prevent unauthorized financial activity. Higher-risk transactions may require authorization by a separate person.
AI outputs can be incorrect or incomplete. Relevant controls include validating invoice totals, reconciling transactions against source records, checking for duplicates, and reviewing uncertain classifications. A model's confidence score alone is not proof that a transaction is accurate.
Audit records may identify the agent or service identity, affected application or financial record, requested operation, execution time, approval, outcome, and any exception. Logs should be protected against unauthorized modification and retained according to applicable requirements. Logging alone does not establish compliance: the evidence must support the relevant control objectives.
Changes to models, prompts, workflows, tool permissions, and connected applications may affect financial processing. Depending on risk, controls may require testing, approval, deployment records, and monitoring for unexpected behavior. NIST's AI RMF organizes voluntary risk-management activities under Govern, Map, Measure, and Manage; GAO's AI Accountability Framework emphasizes governance, data, performance, and monitoring. These are supporting AI-risk frameworks, not SOC 1-specific mandates.
SOC 1 Type 1 assesses whether controls are suitably designed at a specified date. For example, it may assess the design of an approval mechanism restricting an agent's payment actions.
SOC 1 Type 2 additionally assesses whether those controls operated effectively throughout a specified period. Neither report guarantees that an AI system will never make an error or perform an unauthorized action.
SOC 1 addresses controls relevant to customers' internal control over financial reporting. SOC 2 addresses controls against applicable Trust Services Criteria, including security and, depending on scope, availability, processing integrity, confidentiality, and privacy.
An AI platform may need SOC 2 assurance for enterprise security reviews without needing SOC 1. An AI-enabled financial processing service may have a clear SOC 1 use case. Organizations can undergo both examinations when customers need assurance over different risks. Neither report is a blanket certification of an AI model's accuracy or safety.
The controls below illustrate how an AI-enabled invoice-processing service could address financial-reporting risks. They are examples, not a universal SOC 1 checklist. Actual controls and audit evidence depend on the examination scope.
1. Define the system boundary. Identify relevant agents, applications, infrastructure, integrations, and subprocessors.
2. Identify financial-reporting risks. Assess unauthorized access, incorrect outputs, processing failures, and uncontrolled changes.
3. Establish control objectives. Specify the financial-reporting outcomes the controls must support.
4. Implement and document controls. Assign owners and document approvals, reconciliations, access restrictions, monitoring, and change procedures.
5. Collect evidence. Retain approval records, access reviews, transaction logs, testing results, and exception handling records.
6. Engage a qualified service auditor. Confirm the scope, examination period, and evidence requirements.
For example, consider an AI service that extracts invoice data and sends proposed entries to an accounting platform. The service may restrict the agent's permissions, validate invoice fields, log proposed entries, and route exceptions for review. The customer may still need to approve payments and reconcile its ledger. This example illustrates how control responsibility can be shared; it is not a prescribed SOC 1 control set.
The examination may identify complementary user entity controls: controls that customers must operate for the service organization's control objectives to be achieved.
SOC 1 for AI applies established financial-reporting control assurance to AI-enabled services that can affect customers' financial records. It is not a separate AI certification. Its scope depends on the financial processes involved and the controls customers rely on.
Organizations using AI agents in financial workflows should prioritize identifiable agent activity, restricted permissions, transaction authorization, processing accuracy, controlled changes, and reliable audit evidence. These controls help demonstrate that AI-enabled financial operations are appropriately governed.
No. SOC 1 is not universally required for AI companies. It is relevant when a service organization's activities affect customers' internal control over financial reporting and customers or their auditors need assurance over those controls.
No. SOC 1 examines specified controls at a service organization. It does not certify an AI model's accuracy, safety, or overall reliability.
Yes. AI agents can be included when their actions affect financial-reporting processes. Relevant controls may cover agent identities, permissions, transaction approvals, processing accuracy, and audit records.
A Type 1 report addresses the suitability of control design at a specified date. A Type 2 report also addresses whether those controls operated effectively over a specified period.
Not necessarily. SOC 2 addresses applicable Trust Services Criteria, while SOC 1 focuses on controls relevant to financial reporting. A provider may need both reports depending on its services and customers' assurance requirements.
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