AI agents break the assumptions that SOC 2 was built on. Traditional compliance frameworks assume static controls, deterministic system behavior, and human-mediated access. But autonomous agents generate and execute code at runtime, modify their behavior based on training data, and make decisions without human authorization. This creates compliance gaps that existing controls can't address.
This guide covers how AI changes SOC 2 requirements, the specific risks your compliance program must address, and how to implement SOC 2 for AI without building custom infrastructure from scratch.
What is SOC 2?
SOC 2 (System and Organization Controls 2) is a compliance framework developed by the American Institute of Certified Public Accountants (AICPA). It reports on controls at a service organization relevant to security, availability, processing integrity, confidentiality, or privacy.
Organizations design controls appropriate to their operations, then external auditors verify those controls exist (Type I) or operate correctly over time (Type II). Enterprise customers typically require Type II reports because they demonstrate sustained operational efficacy.
The framework evaluates organizations against five Trust Services Criteria:
- Security: Protects systems and data against unauthorized access through access management, encryption, and incident response. This criterion applies to every SOC 2 audit.
- Availability: Ensures systems are accessible as committed. Organizations with specific uptime guarantees or SLAs should include this criterion.
- Processing Integrity: Ensures system processing is complete, valid, accurate, and timely, which is critical for organizations involved in financial reporting or data processing.
- Confidentiality: Addresses protection of information designated as confidential, including intellectual property and sensitive business data.
- Privacy: Addresses collection, use, retention, disclosure, and disposal of personal information in conformity with the entity's privacy notice.
These criteria provide the foundation for demonstrating security controls to enterprise customers, which increasingly drives procurement decisions.
Why do you need SOC 2 compliance?
Many enterprise procurement teams now require SOC 2 reports as a prerequisite for vendor selection, particularly in SaaS and cloud services. This creates a barrier for organizations without certification, who face challenges competing for enterprise customers.
Industries with the highest documented SOC 2 requirements include:
- Technology and SaaS: The highest requirement concentration. Enterprise procurement increasingly requires SOC 2 Type II as a baseline for contract qualification.
- Financial services: Banks and fintech companies require SOC 2 for vendors handling transaction data or customer financial information.
- Healthcare technology: Organizations handling protected health information use SOC 2 to provide assurance beyond HIPAA regulatory requirements.
- Professional services: Consulting, legal, and accounting firms processing client data increasingly require vendor compliance as a procurement condition.
- E-commerce: Payment processors and customer data management systems face SOC 2 requirements from retail clients.
These requirements reflect the trust that SOC 2 compliance signals to potential customers. Enterprise buyers view the certification as evidence that a vendor has invested in security infrastructure and operational discipline.
You should budget 6 to 12 months for SOC 2 Type II certification. But don’t wait to start enterprise conversations until certification is complete. For most startups, the better approach is to pursue SOC 2 and enterprise sales in parallel. So open up your pipeline, be transparent about your compliance roadmap and current controls, and plan to deliver the report by the time you reach late‑stage security review.
How does SOC 2 compliance in AI work?
Traditional SOC 2 assumes three things that AI systems violate. First, controls can be documented and tested at a point in time, remaining effective until intentionally changed. But AI agents produce non-deterministic outputs and drift over time as they process new data.
Second, systems process information "as intended" based on documented logic. However, AI systems exhibit emergent behaviors (actions arising from complex interactions rather than explicit programming) that can't be predicted using traditional change management.
Third, humans make decisions about system access. But if AI agents make access decisions, they could potentially combine permissions in unexpected ways. So static role-based access control can't inspect agent behavior or enforce context-aware policies.
These fundamental shifts affect each of the five Trust Services Criteria differently. Below, we examine how AI transforms compliance requirements these core areas your SOC 2 program must address.
Security requirements
Traditional SOC 2 security controls govern who can deploy code. AI agents generate and execute code at runtime without human authorization for each execution instance. The traditional controls that protect against unauthorized human access can't protect against an agent that writes and runs its own code.
Perpetual sandbox platforms address code execution isolation through hardware virtualization. Unlike containers that share the host kernel, microVMs use hardware virtualization to create air-tight boundaries between execution environments. Each sandbox environment runs as a dedicated process with its own virtual CPU and memory resources. So if an agent generates malicious code, the blast radius stays contained within that sandbox.
Availability
Agents executing dynamically generated code introduce unpredictable failure modes. If an AI agent misclassifies a system or fails to stop an attack, it is difficult to determine which one was responsible, which creates gaps in your traditional availability controls.
Processing integrity
AI systems evolve continuously through exposure to new data. When one AI agent's incorrect output becomes input for another, errors compound in ways traditional processing integrity controls can't detect. Organizations need explainable AI controls and continuous drift detection.
Confidentiality and privacy
Traditional audit trails don't capture how training data influences model behavior. So you can't demonstrate that sensitive information wasn't inappropriately used in training or that models won't leak training data through outputs.
Controls must include output filtering to detect potential data leakage, training data sanitization, and differential privacy techniques during model training.
What are the AI-specific risks SOC 2 must address?
Beyond adapting the Trust Services Criteria, your compliance program must account for risks that didn't exist when SOC 2 was originally designed. The following AI-specific threats exploit gaps between traditional controls and autonomous system behavior.
Agent-generated code execution
AI agents can be manipulated into generating and executing malicious code at runtime, including reverse shells. According to the OWASP Agentic Security Initiative, Unexpected Code Execution (RCE) represents one of the highest-impact threats in autonomous AI ecosystems.
Traditional SOC 2 change management requires changes be authorized, designed, documented, tested, and approved before deployment. Agent-generated code bypasses all these stages by executing at runtime without human authorization.
Real incidents demonstrate this vulnerability. CVE-2025-34291 in the Langflow AI platform allowed attackers to bypass authentication and execute arbitrary Python code. Research analyzing LLM-generated code patches found that patches introduce new security vulnerabilities in 9.5% of cases while fixing the original issue.
Addressing this risk requires AI sandbox isolation that assumes all agent-generated code is potentially hostile. MicroVM architectures provide hardware-enforced boundaries where each execution environment runs its own kernel, so a compromised guest can't affect the host or other sandboxes.
Training data as a security perimeter
AI systems introduce training data as a new attack surface. CyLab research from 2025 found that manipulating as little as 0.1%of a model's pre-training dataset is sufficient to launch effective data poisoning attacks.
Traditional SOC 2 data controls govern who can access training data but don't address data content integrity. An attacker with read-only access who can submit documents to training datasets can compromise the model without needing write permissions to production systems. Poisoned documents pass all traditional integrity checks while containing subtle semantic manipulations designed to alter system behavior.
Ephemeral compute with persistent state requirements
AI agents need consistent security boundaries, not necessarily short‑lived compute. Persistent sandboxes can be just as safe as ephemeral ones when isolation, patching, and monitoring are solid. The risk comes from an uncontrolled state, not from how long a microVM or container exists. Agents still require persistent state to maintain conversation history, authentication tokens, and security context across sessions.
Traditional role‑based access control assumes users have static, predefined roles. But AI agents need more granular and adaptive access control that can change at runtime based on context and task.
For security‑sensitive use cases like financial services, zero data retention (ZDR), where no user data or filesystem state is persisted between runs, can be the right model. ZDR is even more effective when combined with strong isolation. If those are your requirements, look for platforms that enforce both ZDR and strong isolation, like Blaxel.
For high‑context coding agents and other workloads that benefit from keeping dependencies, caches, and prior analysis, perpetual sandboxes make more sense. Sandbox platforms like Blaxel implement this perpetual model: sandboxes enter a standby state with sub‑25ms resume times without continuous compute charges. They also maintain a complete filesystem and memory state without paying for always‑on infrastructure. This removes the classic cold‑start penalty that forces teams to choose between deep security scanning and acceptable latency. It also preserves audit trail continuity because the sandbox identity and state persist even when compute is dormant.
Real-world examples of SOC 2 compliance in AI-first companies
OpenAI maintains SOC 2 Type II compliance for ChatGPT business products and API. Their security approach integrates SOC 2 controls with their Preparedness Framework v2, which incorporates risk assessments specifically designed for AI system safety.
Anthropic achieved SOC 2 Type II compliance and integrated their compliance program with NIST 800-53. Their AI Safety Level 3 standards address AI model misuse prevention and model weight protection.
Cohere maintains SOC 2 Type II compliance with annual audits and developed the Secure AI Frontier Model Framework to specifically address frontier AI model security.
Scale AI achieved SOC 2 Type II compliance with emphasis on training data handling, labeling workflows, and customer data separation, plus DoD IL4 and FedRAMP High Authorization.
Hugging Face holds SOC 2 Type 2 certification for its Hub and Inference Endpoints.
Blaxel maintains SOC 2 Type II certification, ISO 270001 certification, and HIPAA compliance for its perpetual sandbox platform. Blaxel partnered with Probo to automate SOC 2 control implementation and evidence collection during the compliance process.
How to implement SOC 2 compliance for AI
Your implementation path determines whether you spend 18 months and $280,000 building custom infrastructure or achieve certification in 4 months at a fraction of the cost.
Build your own custom infrastructure
Building SOC 2 compliant AI infrastructure typically costs $35,000 to $150,000+ in the first year, with complex, enterprise‑readiness environments reaching $200,000 to more than $250,000. Timelines commonly range from six to 18 months depending on the scope and maturity of your company.
These costs include:
- Readiness and gap assessments (about $5,000 to $25,000)
- Infrastructure and security tooling and remediation ($10,000 to $100,000+)
- Compliance platforms for legal admin and documentation (often $10,000 to $40,000+ combined)
- External SOC 2 Type II audit fees ($20,000 to $50,000 for most startups and mid‑market teams or $30,000 to $100,000+ for enterprises)
- At least 200–400 hours of internal staff time (another $20,000 to $40,000 in opportunity cost)
This approach makes sense only when AI infrastructure is your core product or you have highly specialized requirements unsupported by available platforms. You'll need engineering budgets exceeding $5 million, dedicated security teams of at least three engineers, and timeline tolerance of 18 months or longer.
Partner with a SOC 2-certified infrastructure platform
Partnering with a SOC 2 automation or certified infrastructure platform typically brings total first‑year SOC 2 costs into roughly the $25,000 to $80,000 range for most startups and growth‑stage teams.
These platforms also shorten the journey to SOC 2: traditional timelines of six to 18 months can be reduced to well under a year by streamlining readiness, evidence collection, and control implementation. They are explicitly designed to get lean teams SOC 2 ready in tens of hours of focused work rather than hundreds.
Cost savings come from eliminated infrastructure development, reduced audit fees because controls are already implemented, and substantially reduced internal effort through automation. Your auditors can rely on the platform's existing controls rather than requiring you to build equivalent controls from scratch.
Achieve SOC 2 compliance for AI with a certified platform
SOC 2 compliance for AI platforms requires more than adapting traditional controls. AI agents generate code at runtime, evolve based on training data, and make autonomous decisions that existing frameworks never anticipated.
Blaxel, the perpetual sandbox platform, provides microVM-based isolation with hardware-enforced boundaries between execution environments. Sandboxes offer sub-25ms resume time from standby and automatic snapshot capability with warm storage, enabling rapid deployment while maintaining state persistence for audit trail continuity.
Achieving SOC 2 certification requires expertise beyond infrastructure choices. Expert-led, AI-powered compliance platforms like Probo guide teams through the audit process, from scoping trust service criteria to preparing evidence packages. Blaxel partners with Probo to accelerate the path to certification for teams building on the platform, combining compliant-by-design infrastructure with hands-on audit preparation.
Start building with Blaxel for free to explore the platform's capabilities, or book a demo to discuss your specific security requirements with our founding team.
FAQs about SOC 2 compliance for AI
What is the difference between SOC 2 Type I and Type II for AI companies?
SOC 2 Type I evaluates whether security controls are properly designed at a specific point in time. Type II evaluates whether those controls operate effectively over 3 to 12 months.
Enterprise customers almost universally require Type II because they demonstrate sustained operational effectiveness. For AI companies, Type II is particularly important because a Type I audit might capture controls that worked during assessment but fail to detect drift in AI model behavior over time.
How long does SOC 2 Type II certification take for AI startups?
Type I certification takes 1.5 to 3.5 months. But Type II takes 5.5 to 17.5 months because it requires a 3- to 12-month observation period. For organizations building custom AI infrastructure, add 3 to 6 months of development before the compliance process begins. Partnering with a certified platform reduces total timeline to 4 to 12 months because you can begin the observation period immediately.
Does SOC 2 certification cover AI model security?
SOC 2 covers infrastructure and processes supporting AI platforms but doesn't directly address risks inherent to AI models. Model-specific risks like training data poisoning, adversarial robustness, and emergent behaviors fall outside traditional IT control frameworks.
Organizations should layer AI-specific frameworks like the NIST AI RMF alongside SOC 2. The NIST AI RMF addresses model governance, explainability, and bias detection that SOC 2 doesn't cover.
