Large Language Models (LLMs) are not all designed for the same trust, security, and governance context. A critical distinction exists between enterprise-grade LLM platforms, which are built to operate within regulated, contractually governed environments, and consumer-grade public LLM services, which are optimized for ease of use and broad adoption by individuals. Understanding this distinction is essential when evaluating data protection, privacy, and compliance risks.
This overview compares Google Vertex AI as a representative enterprise-grade LLM platform with ChatGPT (Free, Go, Plus, and Pro) as a representative consumer-grade public LLM service. Vertex AI has been selected because it is the default LLM implementation used by audad and is deeply integrated into Google Cloud’s enterprise security, compliance, and governance framework. ChatGPT has been selected because it currently holds the highest market share among consumer-facing LLMs and is widely used in public and professional contexts, often outside formal enterprise controls.
A key focus of this comparison is the role of third-party processors and how data is handled once it leaves the organization’s direct control. In enterprise platforms such as Vertex AI, the cloud provider acts as a clearly defined data processor under contractual agreements, with explicit limitations on data usage, retention, and access. In contrast, consumer-grade LLMs like ChatGPT operate as standalone SaaS offerings, where user data is processed within the provider’s platform under consumer privacy terms, with less granular control, fewer contractual assurances, and broader internal access for operational and safety purposes.
By contrasting these two approaches, this overview highlights how architectural intent, contractual structure, and third-party processing models directly influence security posture, regulatory compliance, and organizational risk. The comparison is not intended to assess model quality or capability, but rather to clarify where enterprise-grade and consumer-grade LLMs fundamentally differ in their suitability for handling business, confidential, or regulated data.
| Enterprise-Grade AI Vertex AI |
Consumer-Grade AI ChatGPT (Free, Go, Plus, and Pro) |
|
|---|---|---|
| Training on customer data | No training on customer data by default (no opt-out needed) | Training on customer data by default, manual opt-out needed |
| Compliance | ||
| GDPR | Compliant | Possible, but requires internal policy acceptance |
| ISO 27001 / 27017 / 27018 | Compliant | Compliant |
| SOC 1 / SOC 2 / SOC 3 | Compliant | No |
| HIPAA | Compliant | Not suitable |
| Financial regs (FINRA, SEC) | Compliant | High Risk |
| Government classified data | Compliant | Not acceptable |
| Data Confidentiality | ||
| Customer-managed encryption keys (CMEK) | Yes | No |
| Isolation | Virtual Private Cloud (VPC) Isolation, prompts and outputs are associated with your Google Cloud project and thus isolated from other tenants. Authentication and IAM policies control access. | No isolation, shared environemnet |
| Encryption | Strong cloud encryption by default | User data (account, chats, files) is protected by TLS/in-rest encryption under OpenAI’s Privacy Policy. |
| Data Access Control | ||
| IAM | Strong IAM on project level, role separation, and least-privilege access | No, user level only |
| Data Accessability by Provider | Governed by Google Cloud Terms and can be limited or disabled under enterprise agreements | Accessible internally for safety, abuse prevention, and legal reasons. Data may be accessed by OpenAI employees, contractors, safety staff, or service providers for debugging, moderation, or legal compliance even with training off. |
| Data Accessability by Third-party Processors | Governed by Google Cloud Terms and can be limited or disabled under enterprise agreements | Some personal data may be shared with third-party service providers involved in operations, analytics, customer support, etc., under contractual safeguards. |
| Data Retention | ||
| Chats | “Zero data retention” configuration where prompt and response caching can be disabled. No lasting storage beyond what’s essential for serving the request. | Stored in OpenAI systems until deleted manually |
| Meta data | “Zero data retention” configuration where logs can be disabled, | Timestamps, device info, etc. may be collected for operational, security, and abuse-prevention purposes. |
| Data Residency | You can choose exactly where your data is processed (e.g., exclusively in the EU or the US) to comply with local laws like GDPR | You cannot choose where your data is stored |
| Audit | Vertex AI supports audit logging, enabling enterprise regulatory compliance | No |
| Summary | Enterprise grade security, designed for compliance, strong contractual guarantees, Full governance stack, Predictable legal posture | Lacks enterprise-grade governance; relies on individual user settings with potential for data exposure and training on sensitive inputs. Lacks robust compliance certifications and centralized administrative oversight. |