This post explains how “vibe coding,” meaning software development that relies materially on generative AI or AI coding assistants for drafting, revising, or testing code, should be addressed in a SOC 2 report. In general, a SOC 2 report should should describe the practice of vibe coding in operational terms and evaluate whether management has designed and operated effective controls over AI-assisted development, including governance, access, change management, secure development, data protection, third-party risk, and monitoring.

SOC 2 is technology-neutral. The existing Trust Services Criteria already provide a suitable framework for assessing AI-assisted development practices when those practices affect the security, availability, processing integrity, confidentiality, or privacy commitments of the service organization. Current guidance discussing AI in SOC 2 emphasizes incorporating AI-specific risks into the existing control environment rather than creating an entirely separate reporting model, while secure development guidance for generative AI highlights added risks such as unreviewed code generation, prompt or instruction manipulation, excessive tool permissions, insecure dependencies, and unintended disclosure of sensitive information.

How Vibe Coding Should Be Reflected in a SOC 2 Report

If AI-assisted coding is used within the in-scope system, the SOC 2 report should explain the role of those tools to the extent necessary for a user to understand relevant risks and controls. The report does not need to use informal terms such as “vibe coding,” but it should clearly disclose that developers use AI tools in the software development lifecycle, identify where those tools are used, and explain the related control objectives. The focus should remain on whether the use of AI changes the risk profile of code development, code review, testing, deployment, and support processes.

• Describe AI-assisted coding as part of the system only when it is relevant to understanding the services and related control environment.
• Focus on risks introduced or amplified by AI-assisted development, not on the terminology used internally.
• Evaluate whether standard controls were updated to address AI-specific failure modes.
• Present AI-assisted coding as subject to the same or stronger review, testing, approval, and monitoring requirements as traditionally written code.
• Address third-party tool dependency and data handling where prompts, source code, logs, or metadata may be shared with external model providers.

Key Control Considerations

Governance and policy. Management should define whether AI coding tools are approved, restricted, or prohibited for particular use cases; what classes of data may be entered into prompts; whether agentic or auto-executing features are allowed; and which teams may use which tools. Policies should also require training, acceptable use acknowledgments, and escalation paths for suspected misuse or data leakage. These expectations align with the broader SOC 2 emphasis on control environment, communication, and risk assessment.

Access and configuration controls. Access to AI coding platforms should be provisioned through formal authorization, limited to approved personnel, reviewed periodically, and removed promptly upon role change or termination. Higher-risk capabilities, such as autonomous code changes, shell execution, repository write access, package installation, and external network access, should be separately restricted and monitored because they expand the blast radius of misuse or error.

Secure development and human review. AI-generated code should never bypass established secure development lifecycle controls. Management should require documented human review, testing standards, peer review or pull request approval, and security validation proportionate to the risk of the change. The essential point for SOC 2 purposes is not whether code originated from a human or a model, but whether it was reviewed, tested, approved, and deployed through a controlled process. Guidance on AI-secure development underscores that AI outputs can be influenced by untrusted repository content, dependency metadata, tool integrations, or prompts, making review and verification especially important.

Change management and evidence retention. Because SOC 2 examinations depend on demonstrable evidence, organizations using AI-assisted development should preserve artifacts showing that changes were requested, reviewed, tested, approved, and deployed in accordance with policy. Depending on the environment, this may include pull requests, approval records, test results, issue tracking tickets, CI/CD logs, exception approvals, and records of tool configuration or usage restrictions. This is consistent with the existing SOC 2 change management focus, including segregation of duties and evidence that changes were tested and approved appropriately.

Confidentiality, privacy, and data leakage. A central SOC 2 issue is whether developers may expose confidential information, source code, secrets, customer data, or personal information to external AI providers through prompts, attachments, or tool context windows. Controls should address data classification, approved input restrictions, secret scanning, masking or redaction, contractual due diligence over providers, and, where relevant, logging and review of prompt activity. If the service organization’s commitments include confidentiality or privacy, these controls may be especially significant.

Vendor and software supply chain risk. AI coding tools introduce additional third-party dependencies, including model providers, plug-ins, package suggestions, and tool servers. Management should evaluate vendor security commitments, data handling terms, subservice organization considerations, model or plug-in provenance, and risks associated with hallucinated or malicious dependencies. From a SOC 2 standpoint, these matters should be incorporated into vendor management, risk assessment, and secure development controls.

Implications for the Description of the System and Controls

In management’s description of the system, the organization should disclose AI-assisted coding practices when those practices are relevant to understanding the services, infrastructure, software, people, procedures, or data involved in the in-scope system. The description should identify the nature of the tools used, the boundaries of their use, whether they are customer-facing or internal, and the principal controls that govern them. It is generally better to describe the actual practice, such as “developers use approved AI coding assistants subject to access, review, and testing controls,” than to rely on informal or potentially ambiguous terminology.

For a Type 2 report, the examination should then test whether those controls operated effectively over the review period. Examples may include testing approval of AI tool access, inspection of policies and training records, review of code changes for evidence of peer approval and testing, validation of restrictions on sensitive data entry, and evaluation of monitoring or exception handling for prohibited tool use. The stronger the organization’s reliance on AI-assisted development, the more important it becomes that the evidence trail be complete and consistent.

Practical Conclusion

In summary, SOC 2 reports should not attempt to audit “vibe coding” as a label. They should address AI-assisted coding as a development practice that may alter the organization’s risk profile and therefore requires clear governance, controlled use, human oversight, evidence-based change management, data protection, vendor oversight, and ongoing monitoring. If those controls are well designed and operating effectively, AI-assisted development can be incorporated into a SOC 2 report without changing the underlying reporting model. If those controls are absent or informal, the use of AI in development may create gaps in the suitability of design or operating effectiveness of the control environment.

Illustrative Control Statements

• Management approves AI coding tools before use in production development environments.
• Access to AI-assisted development tools is role-based, periodically reviewed, and promptly revoked when no longer needed.
• Developers are prohibited from submitting secrets, regulated data, or customer confidential information to unapproved AI tools.
• All AI-assisted code changes are subject to the same documented review, testing, and deployment approval procedures as other code changes.
• Higher-risk AI agent capabilities, including autonomous execution or repository write access, are restricted, logged, and monitored.
• Third-party AI tool providers are evaluated through the vendor risk management process.
• Exceptions to AI usage requirements are documented, approved, and retained for review.