Data security and privacy precautions for Using Third-Party LLM APIs in Enterprise

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Table of Contents

• Data security and privacy precautions for Using Third-Party LLM APIs in Enterprise

• Encryption in Transit and Data Security Measures

• On-Premise and Self-Hosted LLM Options

• Contractual Safeguards and Data Usage Policies

• Handling Sensitive Data: Minimization, Masking, and Monitoring

• Industry-Specific Considerations

  • Finance Industry

  • Healthcare Industry

• Regulatory and Compliance Frameworks

• Conclusion

Encryption in Transit and Data Security Measures

All data sent to a cloud LLM API should be encrypted in transit to prevent eavesdropping or tampering. Reputable providers use HTTPS/TLS encryption for API calls by default. For example, OpenAI encrypts all data at rest using AES-256 and in transit using TLS 1.2+ for its enterprise services (Enterprise privacy at OpenAI | OpenAI). This ensures that prompts and responses cannot be intercepted or read during network transmission. Enterprises should verify that any third-party API endpoints require TLS and ideally are certified for strong security controls (e.g. SOC 2 compliance) . Data at rest on the provider side should also be encrypted and access tightly controlled (as OpenAI and others do with strict access controls and 24/7 security monitoring ).

For especially sensitive use-cases, organizations may add extra layers of encryption. One emerging approach in research is to use cryptographic techniques like homomorphic encryption or secure multi-party computation so that the cloud never sees raw data (A Practical and Privacy-Preserving Framework for Real-World Large Language Model Services). In theory, a user could encrypt parts of a prompt such that the LLM can still process it without decrypting (using advanced methods). However, current methods for fully encrypted LLM inference remain impractical due to heavy computational overhead . As a result, the more feasible practice today is to ensure standard encryption in transit is always enabled and limit exposure of especially sensitive fields (see “Data handling” below). If available, enterprises can also use virtual private network connections or private endpoints to the API service, so traffic doesn’t traverse the public internet at all (on top of being encrypted). In summary, use strong encryption for all data in transit and verify the provider’s data security measures to guard against network-based threats.

On-Premise and Self-Hosted LLM Options

One fundamental precaution is to avoid sending sensitive data to third-party environments in the first place. Enterprises with strict data governance may prefer on-premises or self-hosted LLMs instead of public APIs. Running an LLM within the company’s own infrastructure (data center or cloud VPC) ensures full control over data and circumvents external exposure. Advances in open-source models and frameworks have made this more viable. For example, Meta’s LLaMA and other open models can be deployed using libraries like PyTorch or TensorFlow on enterprise hardware. Tools such as PyTorch TorchServe allow serving large models internally (LLM Deployment with TorchServe — PyTorch/Serve master documentation), and PyTorch’s edge runtime even enables privacy-aware on-device inference . Recent research in the healthcare domain underscores the importance of local deployments: one 2024 study presented a compact LLM framework for local use on electronic health records, explicitly to meet “strict privacy requirements” in hospitals (A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications). By keeping the model and data on-prem, organizations eliminate the risk of data in transit to an outside party.

That said, on-prem LLMs come with trade-offs – notably the computational cost and engineering effort to maintain them. Not all enterprises can train or host a 100B-parameter model internally. A middle ground is using vendor-provided private instances: for instance, Azure OpenAI Service hosts OpenAI models in a customer’s Azure tenant with network isolation. In such a setup, prompts stay within the enterprise’s cloud environment, and the provider (OpenAI) itself does not see the data (Data, privacy, and security for Azure OpenAI Service - Azure AI services | Microsoft Learn). This approach provides many benefits of on-prem (data residency and isolation) while leveraging third-party model quality. In summary, if the data is highly sensitive or regulated, strongly consider on-premises LLM solutions or cloud offerings that keep your data isolated, rather than calling a public API over the open internet. This eliminates many privacy concerns by design.

Contractual Safeguards and Data Usage Policies

When using any third-party AI API, it’s critical to establish clear contractual and policy safeguards around data usage. Review the provider’s terms and privacy policies to ensure they will not store, mine, or resell your data. Most enterprise-focused LLM providers now pledge not to use customer inputs for training or any purpose beyond fulfilling the request. For example, OpenAI’s policy states that business data submitted via their API (and ChatGPT Enterprise) is not used for training models by default (Enterprise privacy at OpenAI | OpenAI). Similarly, Google Cloud’s generative AI terms include a clause that “Google will not use Customer Data to train or fine-tune any AI/ML models without Customer’s prior permission” (Solved: Re: Privacy of Data on LLM - Google Cloud Community). Anthropic also assures that they do not train the Claude model on your conversations or content when you use their Claude Enterprise plan (Claude for Enterprise \ Anthropic). These commitments, often backed by a Data Processing Addendum, safeguard your intellectual property and sensitive info from ending up in the provider’s model or datasets. As a precaution, enterprises should opt for enterprise plans or contracts (rather than the free or consumer version of an AI service) because they typically come with stronger data privacy guarantees. Always turn off any data logging or training opt-in features – for instance, OpenAI allows opting out of data retention beyond 30 days, and by default does not retain or review API inputs after 30 days unless for abuse monitoring .

In addition to policy promises, get formal agreements in place. A Data Processing Agreement (DPA) is essential if the data includes personal information (to meet GDPR and other laws) . The DPA should enumerate the security controls and affirm the provider’s role as a data processor only acting on your instructions. For healthcare data, a Business Associate Agreement (BAA) is required under HIPAA; some LLM API providers are willing to sign BAAs for enterprise clients (OpenAI confirms it can sign BAAs to support HIPAA compliance ). Verify the provider’s compliance certifications too – SOC 2 Type II is a minimum in many enterprise procurement checklists, as it signifies the vendor has been audited for security, confidentiality, and integrity controls . Providers like OpenAI have achieved SOC 2 compliance , and Microsoft’s Azure OpenAI is covered by Azure’s ISO 27001, HIPAA, and FedRAMP compliance as well. These certifications and agreements provide assurance that the vendor has baseline safeguards and will handle data in accordance with privacy regulations.

Finally, clarify data retention and deletion policies in the contract. The enterprise should retain ownership of inputs and outputs, with rights to delete data upon request. Ideally, use services that offer zero or minimal retention of your prompts. Also consider negotiating clauses about breach notification and liability. Some providers even offer indemnification for data leaks or IP issues in their enterprise contracts (for example, Google Cloud provides certain indemnities for generative AI usage (Protecting customers with generative AI indemnification)). In short, don’t rely on verbal or marketing promises – ensure data use limitations, privacy commitments, and security standards are codified in your contract and that the provider’s infrastructure meets your compliance needs.

Handling Sensitive Data: Minimization, Masking, and Monitoring

A crucial best practice is data minimization – avoid sending any more data to the LLM API than is necessary for the task. Even with encryption in transit and contracts in place, the safest data is the data never transmitted. For highly sensitive fields (PII, financial account numbers, health identifiers), evaluate if they are truly needed by the model. Often, you can redact or anonymize certain details before sending a prompt. For example, an insurer using an LLM to summarize claim notes could remove patient names or replace them with pseudonyms. Some organizations implement pre-processing to mask secrets or personal info (e.g. replace “John Doe” with “[PERSON]”) and post-process the model’s answer to re-insert the masked info if needed. This way, raw personal data never leaves your premises unencrypted. Recent research is exploring automated ways to do this: one 2025 study introduced OneShield Privacy Guard, a framework to detect and scrub sensitive entities (names, dates, phone numbers, etc.) from LLM inputs and outputs with high accuracy (Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications). Such automated PII detection tools (e.g. Microsoft Presidio or OneShield) can be integrated into the prompt pipeline to enforce that no confidential token goes to the model unchecked. Implementing these guardrails helps ensure that even if an employee accidentally includes something sensitive, it gets sanitized before reaching the third-party API .

Beyond sanitization, monitor what data is being sent and returned. Logging and audit trails can help track that no unauthorized or out-of-policy data was transmitted. Some enterprise LLM platforms provide audit logs for prompts (ChatGPT Enterprise offers audit log capabilities to track usage (Claude for Enterprise \ Anthropic) ). Regularly review these to catch any misuse or oversharing of data. In addition, consider rate limiting or gateways: routing API calls through a corporate proxy that can scan for certain patterns (like Social Security Numbers) adds an extra checkpoint.

Another aspect of sensitive data handling is to prepare for the worst-case: assume any data sent to an external API could be exposed (via a breach or bug) and classify it accordingly. Encrypting data in transit is non-negotiable, but also encrypt at rest on your side any records of the prompts or responses you store. Some companies even explore client-side encryption of the prompt content itself (beyond TLS) – for instance, encrypting a part of the prompt with a shared key and having the LLM operate on ciphertext. Current LLMs can’t natively handle encrypted text meaningfully, but hybrid approaches exist (one academic approach, PrivacyRestore, proposes stripping out private info from the prompt and later restoring it in the answer via controlled post-processing (PrivacyRestore: Privacy-Preserving Inference in Large Language ...)). While such techniques are experimental, the takeaway is to be creative in minimizing the exposure of real sensitive values. If an AI task can be solved with tokens or hashes representing the data rather than the raw data, that method should be preferred. At the very least, employees should be trained not to paste confidential information into prompts blindly. (Notably, there have been incidents where employees leaked proprietary code or data to ChatGPT, thinking it was private – leading companies like Samsung to ban such usage after discovering sensitive code in ChatGPT logs (Samsung bans use of AI like ChatGPT for staff after misuse of chatbot).)

Industry-Specific Considerations

Finance Industry

In finance, data security and privacy are paramount due to both regulatory requirements and the high stakes of leaks (e.g. insider info, customer financial data). Financial firms have been cautious with third-party AI tools – in fact, many banks initially banned or restricted employee use of ChatGPT and similar services when they emerged, fearing that confidential client information or code could leak (14 Companies That Issued Bans or Restrictions on ChatGPT). Goldman Sachs, Deutsche Bank, and others put blanket restrictions until policies were in place . This underscores the need for strict precautions: any AI usage must go through security review and risk assessment. For financial use-cases, strongly consider using private cloud instances or on-prem LLMs. For example, a bank might use an open-source model fine-tuned on its own data, deployed in-house, to answer questions about internal documents – thereby keeping all data within its firewall. If using a third-party API, limit it to non-sensitive queries (e.g. general market analysis, drafting emails) and never input client identifiers, account numbers, or non-public financial projections into the prompt.

Encryption and contractual measures discussed above are especially critical here. Financial data often falls under privacy laws like GDPR (if it includes personal data) and GLBA in the US, so a robust DPA is needed. Ensure the provider will store data in approved regions – e.g. EU customer data should stay in EU data centers to comply with GDPR data transfer rules (some providers let you specify region endpoints). Also, consider encryption of data at rest with customer-managed keys if the cloud provider supports it, so the enterprise can revoke access to its data. Auditing is another consideration: firms might require the AI provider to undergo audits or penetration testing. SOC 2 compliance is a baseline, but there may be additional financial industry standards (for instance, a provider’s alignment with ISO 27017 cloud security or FFIEC guidelines could be relevant).

Another key precaution in finance is monitoring outputs. LLMs can sometimes produce plausible-sounding but incorrect statements – which in trading or lending could be risky. But specific to security: monitor that the model isn’t inadvertently echoing back sensitive info. If you fine-tune an LLM on proprietary data, ensure you’ve scrubbed any customer PII from the training set or applied techniques to prevent memorization. Recent surveys on LLM security note that training data extraction attacks are a concern – models can regurgitate parts of their training data ( Security and Privacy Challenges of Large Language Models: A Survey). So a bank fine-tuning a model on transaction logs must ensure no actual account numbers can be regenerated from it. Techniques like differential privacy during training (as implemented in tools like TensorFlow Privacy (Introducing TensorFlow Privacy: Learning with Differential Privacy ...)) could be employed to mitigate memorization of specifics, though this is a more advanced measure. In summary, the finance sector should apply defense-in-depth: assume data is sensitive, restrict and anonymize it, use only vetted secure APIs, and continuously monitor for any sign of leakage or non-compliance.

Healthcare Industry

Healthcare data is heavily regulated and extremely sensitive, making the bar for using third-party LLMs very high. Patient health information is protected under HIPAA in the US, which mandates safeguards like encryption and access controls for any electronic protected health information (ePHI). If a healthcare provider or insurer wants to use an LLM API on clinical data, they must ensure the vendor is HIPAA-compliant and will sign a BAA (Enterprise privacy at OpenAI | OpenAI). Without a BAA, sending PHI to an external service is generally a violation of HIPAA. Even with a BAA, minimum necessary principles apply: only share the data needed for the task. For instance, if using an LLM to draft a summary of a doctor’s dictation, exclude identifying details that aren’t required for the summary.

One approach gaining traction is to use LLMs for healthcare in a de-identified or pseudonymized manner. That means stripping out names, SSNs, etc., from the prompt so that even if the data were somehow exposed, it wouldn’t directly identify the patient. There are NLP tools that can de-identify medical text, and these can be run in-house before sending data to the LLM. Additionally, HIPAA and healthcare ethics would encourage logging and auditing who accessed what data via the AI – ensuring an audit trail if something goes wrong. The LLM’s output should also be treated as potentially containing PHI if the prompt did, and thus protected/stored with the same care as the original records.

Healthcare enterprises should consider on-premise LLM deployments more strongly than most. The cited 2024 study on a local LLM for EHR data (A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications) highlights that running models locally can help meet strict privacy requirements. Hospitals might deploy smaller specialized models on their own servers to analyze medical text, avoiding any external data transfer. If using cloud-based LLMs, many choose cloud providers who already handle their other health data (e.g. using an LLM service on a HIPAA-eligible cloud like Azure or GCP, which have the necessary compliance controls in place). Network isolation and encryption are a must – e.g. using a VPN or private link to connect to the LLM API, in addition to TLS encryption, to double-protect any PHI in transit.

Regulatory compliance goes beyond HIPAA. In the EU, health data is considered special category data under GDPR, requiring explicit consent or other legal basis and stringent protection. Thus, a healthcare org in the EU using an LLM API needs to ensure GDPR compliance – typically via a strong DPA, EU data residency, and possibly conducting a Data Protection Impact Assessment (DPIA) before deployment, given the novel risks of AI. There’s also the concern of medical data breaches; if an LLM provider suffered a breach that exposed patient data, the healthcare organization would have to report it under laws like HIPAA and GDPR. This is why vetting the security posture (SOC 2, ISO 27001, etc.) of the AI vendor is so important in healthcare.

Lastly, healthcare has an accuracy and safety component: if an LLM is used in any clinical decision support, not only privacy but bias and error become concerns. While outside our scope, it’s worth noting that any outputs affecting patient care need human verification and the AI should ideally be confined to non-critical tasks (or thoroughly validated). From a privacy standpoint, however, the main point is lock down patient data – use compliant services, minimal data sharing, encryption everywhere, and continuous oversight. The promise of LLMs in medicine is huge (summarizing notes, answering patient questions, etc.), but it must be balanced with absolute diligence in protecting confidentiality.

Regulatory and Compliance Frameworks

When deploying third-party LLM APIs, enterprises must navigate relevant data security and privacy regulations:

• GDPR (General Data Protection Regulation) – If any personal data from EU individuals is involved, GDPR applies. This means you need a valid legal basis to process the data with an LLM (often legitimate interest or consent), and you must uphold principles like purpose limitation and data minimization. A Data Processing Agreement is mandatory to bind the API provider as a processor (Enterprise privacy at OpenAI | OpenAI). GDPR also requires that personal data stay within approved jurisdictions or have proper safeguards for international transfer. Using an LLM service hosted outside the EU could be problematic unless standard contractual clauses or similar are in place. Additionally, individuals could exercise rights (access, deletion) on data that was fed to or generated by the LLM – the enterprise must be prepared to handle such requests, which in practice means keeping track of what personal data is sent to the model. Non-compliance with GDPR can lead to hefty fines, so any third-party AI handling personal data should be thoroughly assessed by privacy officers or DPOs. Recent research emphasizes that LLM deployments must contend with global privacy laws, and non-compliance (e.g. accidental inclusion of EU personal data in a prompt) is a serious risk (Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications). Always consider data anonymization or aggregation to reduce GDPR exposure.

• HIPAA (Health Insurance Portability and Accountability Act) – As discussed in the healthcare section, HIPAA sets the standard for protecting health information in the U.S. If an enterprise is a covered entity or business associate, and the LLM will see PHI, then the LLM provider must sign a BAA and implement required safeguards . Encryption (especially in transit) is an addressable implementation under the HIPAA Security Rule, which in practice means it’s expected that ePHI sent over networks is encrypted. HIPAA also requires access controls, audit logs, and breach notification. An enterprise using an LLM API should ensure they have a way to audit access (some API providers offer audit logs as noted) and that if the provider has any incident, they will notify you promptly so you can meet HIPAA breach reporting timelines. Even with a BAA, the covered entity remains responsible for evaluating the risk – for example, if the AI will output or transform PHI, how is that output used and secured? In short, HIPAA compliance for LLM usage means: use a compliant service with a BAA, encrypt all PHI in transit, minimum necessary data only, and maintain oversight of the PHI’s use. Organizations may also look to HITRUST certification as a sign the vendor meets healthcare security controls, although HIPAA itself has no official certification.

• SOC 2 – Service Organization Control 2 is not a law but a compliance framework/audit report that many enterprises demand from SaaS and cloud providers. It evaluates a provider’s controls in areas of security, availability, processing integrity, confidentiality, and privacy. Using an LLM API that has a SOC 2 Type II report gives confidence that the provider follows industry best practices to safeguard data (e.g. access management, incident response, change management are all vetted). OpenAI’s API platform completing a SOC 2 audit (Enterprise privacy at OpenAI | OpenAI) is an example that can reassure customers of its maturity in security processes. SOC 2 alone doesn’t guarantee there will never be a breach, but it indicates an independent audit found the controls reasonable. Enterprises should request the SOC 2 report and review any noted exceptions or areas of risk. Additionally, other certifications like ISO/IEC 27001 (information security management) or SOC 3 (public report) are good to have. In financial services, SOC 2 + SOC 1 (for financial controls) might both be relevant. When integrating a third-party LLM, treat it as you would any critical vendor: ensure they meet your vendor risk management criteria and comply with frameworks relevant to your industry (for finance, possibly PCI DSS if credit card data is in scope; for government, FedRAMP; etc.). Also keep an eye on emerging AI-specific standards – for instance, the EU AI Act (still in progress) will impose additional requirements on “high-risk” AI systems which could include certain enterprise AI uses.

In summary, aligning with regulatory frameworks means doing due diligence and technical enforcement. Use encryption and access controls to meet the technical safeguard requirements (as both GDPR and HIPAA expect), and use contracts/DPAs to cover the organizational requirements. Ensure the LLM provider’s assurances and certifications map to your compliance obligations. By baking compliance into the design (privacy by design), enterprises can leverage third-party LLM APIs while respecting all legal and ethical duties related to data security and privacy.

Conclusion

Using third-party LLM APIs in an enterprise setting offers great power but also demands responsibility. Organizations should combine technical safeguards (encryption, on-prem or private deployments, data minimization, PII stripping) with administrative safeguards (contracts, policies, employee training, compliance checks) to protect their data. Both general best practices and industry-specific requirements must be accounted for before deploying an LLM on sensitive business data. The latest research and industry developments show a clear trend: focus on privacy-preserving AI, whether through better encryption, anonymization techniques, or frameworks to detect sensitive content (Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications). By following these precautions – encrypt in transit, carefully vet and contract with API providers, keep sensitive data out of unsanitized prompts, and adhere to all relevant regulations – enterprises can safely integrate the power of large language models into their workflows. The balance of innovation and compliance is achievable with a thoughtful, defense-in-depth approach to using LLMs. In doing so, organizations in finance, healthcare, and beyond can reap the benefits of AI while maintaining trust and security for their data and customers.

Sources:

1. OpenAI Enterprise Privacy FAQ (2024) – OpenAI’s approach to data security and compliance (Enterprise privacy at OpenAI | OpenAI) .

2. Azure OpenAI Service Data Privacy (2024) – Microsoft’s guarantees on customer data isolation and non-use for training (Data, privacy, and security for Azure OpenAI Service - Azure AI services | Microsoft Learn).

3. Anthropic Claude Enterprise Announcement (2024) – Claude’s enterprise security features and data protection commitments (Claude for Enterprise \ Anthropic).

4. Google Cloud Terms for AI (2023) – Stated policy that customer data will not be used to train models without permission (Solved: Re: Privacy of Data on LLM - Google Cloud Community).

5. Security and Privacy Challenges of Large Language Models: A Survey (Das et al., 2024) – Research overview of LLM privacy risks and defenses ( Security and Privacy Challenges of Large Language Models: A Survey).

6. Deploying Privacy Guardrails for LLMs (Asthana et al., 2025) – Study on the OneShield framework for detecting and removing sensitive data in LLM workflows (Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications) .

7. A Practical and Privacy-Preserving Framework for LLM Services (Mao et al., 2024) – Research on cryptographic techniques (HE, MPC) to protect user queries to LLMs (A Practical and Privacy-Preserving Framework for Real-World Large Language Model Services).

8. Local LLM for High-Privacy EHR Applications (Qu et al., 2024) – Proposal of an on-premise LLM framework for healthcare data to meet privacy requirements (A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications).

9. News reports on corporate policies for ChatGPT (2023) – e.g. companies like Deutsche Bank and Goldman Sachs banning ChatGPT to prevent data leaks (14 Companies That Issued Bans or Restrictions on ChatGPT).

10. TensorFlow Privacy Library Announcement (2019) – Google’s toolkit for training ML models with differential privacy (Introducing TensorFlow Privacy: Learning with Differential Privacy ...)