Understanding Third-Party AI Vendor Risks: AI Governance, Security, and Compliance

In today’s enterprises, generative AI tools like ChatGPT, Microsoft 365 Copilot and Claude are double-edged swords — unlocking innovation but also exposing sensitive data and compliance gaps. When employees paste proprietary code or customer lists into external AI services, they risk leaking PII or trade secrets to third-party vendors. In fact, recent surveys show 77% of organizations have inadvertently shared confidential data with AI chatbots, and 32% of unauthorized data exfiltration now occurs via AI tools. As CISOs and compliance officers grapple with AI’s strategic value, the key question is: How do we harness generative AI safely while preventing data breaches and regulatory fines? This article explores the hidden third-party AI vendor risks and outlines a modern approach — centered on real-time GenAI DLP and automated compliance — to secure enterprise AI use.

The Expanding Landscape of Third-Party AI Risks

Third-party AI vendor risk refers to the dangers organizations face when relying on external AI services. Many companies integrate off-the-shelf AI models (ChatGPT, Copilot, Claude, etc.) or use SaaS apps powered by AI, often without full visibility into how these tools handle data. As one AI governance platform explains, “third-party AI tools introduce unknown risks… Many organizations rely on external vendors without knowing how models…process data or comply with regulations”(centraleyes.com). In other words, your data may leave your network before any breach is detected.

Image: A computer screen displaying the word “Security” with a cursor, highlighting data protection concerns in AI interactions.

In highly regulated industries (banking, telecom, healthcare, public sector), these risks multiply. For example, an engineer copying proprietary source code into a free ChatGPT prompt can unknowingly expose IP and violate GDPR or HIPAA. In early 2023, Samsung’s developers did just that — leaking confidential code and notes into ChatGPT — and were forced to ban the tool on corporate devices. This “shadow AI” usage (unsanctioned AI apps) is rampant: 90% of IT leaders are worried about AI-related privacy and security, and 80% have already experienced an AI-driven data incident. Critically, many LLMs lack audit trails: once data is entered, it may be cached or used to train models, permanently leaving the organization’s control. Gartner even warns that “unchecked AI experimentation is emerging as a critical enterprise risk,” urging structured AI governance.

Actionable Takeaways: Conduct thorough AI vendor assessments. Inventory all external AI tools (official or shadow), and classify the data you share with them. Update vendor contracts to include data protection clauses and require auditability. Centralize a registry of approved AI services — transforming AI usage from shadow IT into managed “assets” with clear stewardship.

Shadow AI and Data Exposure in the Enterprise

One of the biggest threats is shadow AI: employees using unsanctioned AI tools that IT and security teams don’t know about. This mirrors the old “shadow IT” problem, but with higher stakes. As CIO.com notes, “the first and most immediate risk is data exposure”: sensitive information often gets pasted into public AI chatbots without protection. In a 2025 survey, 90% of respondents feared shadow AI’s privacy threats, and 13% said such AI incidents already caused them financial or reputational harm.

Image: A person in a hoodie at a computer, symbolizing the hidden dangers of shadow IT and unauthorized AI use.

Shadow AI thrives because AI tools have a low barrier to entry. A marketing analyst drafting content in ChatGPT or a finance user forecasting with an open LLM may seem harmless, but cumulatively these actions bypass formal security controls. The challenge is visibility: traditional security tools and IAM systems can’t easily detect browser-based or personal-account AI use. In one industry survey, 73% of executives admitted AI adoption revealed new visibility gaps, and 82% said AI risks forced them to modernize governance processes.

For example, a retail company might allow Copilot in Microsoft Office only on sanitized data, but an employee could still use their personal ChatGPT account on unsanitized data, creating a blind spot. Since most generative AI platforms do not log prompt histories in an enterprise-accessible way, security teams have no audit trail to investigate incidents. Every AI query becomes a potential data leak.

Actionable Takeaways: Improve AI visibility. Extend monitoring tools (CASBs, EDR) to flag unusual API calls to LLM providers. Use specialized detection to find AI-related traffic (e.g. browser alerts for known AI endpoints). Encourage a culture of disclosure: train employees to report AI tool usage as they would new software. Create an AI sandbox for testing, so that any sensitive data use is done in a controlled, monitored environment.

Compliance Pitfalls: GDPR, HIPAA, PCI and More

Generative AI multiplies compliance headaches in regulation-heavy sectors. Any personal, financial or health data fed into a third-party AI can violate laws. For instance, GDPR requires data minimization and strict control over personal data. If an EU customer’s data is sent to an AI model outside approved systems, companies can face fines up to 4% of global revenue. Similarly, Turkey’s KVKK (Personal Data Protection Law) has comparable penalties for data misuse. In healthcare, HIPAA mandates that protected health information (PHI) must be shielded; even asking ChatGPT for patient-related insights could trigger a breach. PCI DSS forbids exposing payment card data externally.

Image: A robotic hand pointing at a digital network, illustrating the need to secure AI-driven data flows across networks and cloud.

According to LayerX security research, 40% of files uploaded to AI tools contained PII or PCI data, and 22% of pasted text included regulated info. For firms bound by GDPR, HIPAA, SOX or PCI, this is a ticking compliance time bomb. Regulations are also evolving: NIST’s new AI Risk Management Framework (AI RMF) and ISO/IEC 42001 (AI management system standard) explicitly expect organizations to demonstrate responsible AI use. Sorn Security’s platform, for example, “logs all activity to verify data integrity and ensure compliance with NIST AI-RMF, ISO 42001, GDPR, and more”(sornsecurity.com). In practice, enterprises must map AI data flows and enforce policies (e.g. disallow PHI or PCI prompts) to stay audit-ready.

Actionable Takeaways: Align AI governance with existing compliance frameworks. Incorporate AI risk into ISO 27001 ISMS processes and follow NIST AI RMF guidance. Classify data (e.g. patient, financial, personal) and explicitly prohibit its exposure to unsanctioned AI. Perform third-party risk audits for each AI vendor: verify if they offer enterprise controls like data residency, encrypted storage and GDPR/PCI compliance features.

Why Traditional DLP Falls Short in the AI Era

Conventional data loss prevention (DLP) tools were built for emails, file shares and endpoints. They match patterns or signatures in documents leaving the network, but generative AI leaks are semantic — occurring through free-form prompts. As one Varonis expert observes, “Traditional DLP solutions… primarily monitor data moving through email, file transfers and endpoint activities. The rise of AI tools like ChatGPT demands a more sophisticated approach”. Indeed, as soon as a user pastes a client report into an LLM, a legacy DLP system sees nothing: it never scanned a network file or blocked a USB copy.

Image: A small caution cone on a laptop keyboard, symbolizing the need for caution and new safeguards in AI-driven data usage.

Enterprises often try to bolt on existing tools (CASBs, network DLP, EDR) but encounter limitations. EDR can flag a new AI app install, but not the data it ships. CASBs may spot unauthorized SaaS, but many AI tools work through browser or plugins. Even new “ChatGPT DLP” solutions (like content filters in Microsoft Copilot) often rely on regex rules. They lack context: AI can infer protected information that wouldn’t match a keyword list. Varonis summarizes the gap: “Traditional DLP focuses on email, file transfers, and endpoints. ChatGPT DLP extends protection to generative AI tools, ensuring sensitive data isn’t exposed during prompt submissions”.

In short, intrusion detection and legacy DLP create blind spots. An advanced attacker (or even a naive insider) could use ChatGPT as an exfiltration channel, posting stolen data bit by bit. Since free tools won’t log or restrict outbound content, this becomes an invisible leak. Shadow IT detection helps find unknown AI apps, but by itself isn’t enough to stop data loss.

Actionable Takeaways: Extend DLP to the data, not just the network. Deploy AI-aware DLP that performs inline semantic analysis of prompts and documents. For example, Sorn Security’s solution inspects every user input in real time, “detects and blocks confidential information… before it’s exposed to ChatGPT, Claude, Copilot, or any generative AI tool”(sornsecurity.com). Integrate DLP with security gateways and proxies so that every AI endpoint (browser, plugin, app) is monitored. Update data-handling policies: require that high-risk data never enters unapproved AI. Train your DLP and IR teams to recognize “llm data leakage” patterns — e.g. unusual chunks of text or data fields being copied into chat interfaces.

Embracing Real-Time GenAI DLP and Automated Governance

To address these challenges, leading security teams are adopting real-time GenAI Data Leak Prevention (DLP) and automated AI compliance enforcement. Unlike legacy tools, a GenAI DLP works inline: it intercepts every prompt or file upload to an AI model and checks it against policies. In practice, when a user attempts to input sensitive data, the system can block, mask or warn before the data leaves the corporate perimeter. For example, if a banker tries to ask ChatGPT about a client’s account details, the prompt interceptor would redact or cancel the request. This real-time guardrail turns the AI “front door” into a controlled chokepoint.

At the same time, automated policy frameworks ensure consistent governance. Sorn Security’s platform exemplifies this approach. It provides “full visibility into every GenAI interaction — detecting real-time data exfiltration, managing third-party AI risk, and enforcing policy across your organization”. In other words, it creates a centralized AI governance layer. Security teams can classify their data (PII, PHI, PCI, IP) and set policies accordingly; the system then automatically enforces those rules across all AI vendors. Logs and alerts are generated for compliance audits.

Importantly, this strategy aligns with industry frameworks. By logging all AI activity, enterprises can meet NIST and ISO 42001 requirements for traceability. The platform’s controls map to regulations like GDPR and HIPAA: for instance, preventing personal data from leaving the EU boundary or alerting on any PHI prompt. As one CISO testimonial puts it, they “finally see how GenAI is used across our company — and stopped data leaks before they happened.” Sorn’s customers say the AI compliance workflows save them hours and tighten internal controls, effectively governing every AI interaction securely and automatically.

Actionable Takeaways: Adopt AI-native DLP and governance. Choose a solution that integrates with major AI tools (ChatGPT API, Copilot, Slack/GitHub integrations, etc.) and inspects prompts in context. Look for features like real-time access controls (blocking sensitive uploads) and automatic policy enforcement (digital rights management for prompts). For example, Sorn’s GenAI DLP “detects and blocks confidential information in real time — before it’s exposed”. Also establish an AI risk committee with cross-functional representation (security, legal, data) to approve new AI tools and update policies as models evolve. Finally, link these controls to your SIEM and SOC: now every AI prompt can generate an audit trail or incident report, ensuring you maintain audit-ready compliance at scale.

Actionable Steps for Risk-Conscious Organizations

Regardless of your industry, certain best practices will mitigate third-party AI risk:

• Inventory and Classify Data: Catalog where sensitive data resides (databases, endpoints). Classify it by sensitivity (public, internal, confidential, regulated) and tag it. Only allow the lowest-tier data to be used with external AI, if at all.

  
  

• Assess AI Vendors: Treat AI vendors like any third-party: perform security and compliance assessments. Check if their enterprise offerings support encryption, SSO, or data quarantine. Ensure contracts specify data usage and breach notification terms for AI models.

  
  

• Implement Tiered AI Access: Create a policy hierarchy: e.g. Tier 1 data (customer PII, financials) is never input to outside LLMs; Tier 2 (general internal docs) may be used in controlled test sandboxes; Tier 3 (public data) is safe for any AI tool. Automate these policies with DLP so users are guided in real time.

  
  

• Train and Certify Staff: Educate employees on what can’t be shared with AI (e.g., customer lists, health records). Simulate data handling scenarios in training. Encourage users to route high-risk queries through approved channels or synthetic data sandboxes.

  
  

• Continuous Monitoring: Use analytics and UEBA to flag unusual AI activity patterns (e.g. unusual frequency of API calls, or new user accounts using LLMs). Regularly review logs for trends. Integrate AI monitoring with your existing SOC processes.

Image: Two colleagues shaking hands in an office, illustrating partnership and collaboration in implementing AI governance and security solutions.

Key Takeaway: Implementing these steps in unison — data classification, vendor due diligence, real-time DLP controls, and employee awareness — creates a zero-trust approach to generative AI. This not only blocks data exfiltration but also fosters an environment where innovation can flourish under proper guardrails.

Conclusion: Secure Your GenAI Strategy with Confidence

Third-party AI vendor risk is a new frontier in cybersecurity and data compliance. The technologies and regulations are evolving rapidly, and high-stakes industries can’t afford to lag. By embracing a modern, integrated approach — combining AI governance frameworks (NIST AI RMF, ISO 42001), real-time GenAI DLP, and automated policy enforcement — organizations turn the “shadow” of risk into transparent, managed processes. This balanced strategy lets you harness AI’s power without sacrificing data security or compliance.

The next step for risk-conscious enterprises is clear: partner with experts who offer turnkey solutions. Request a demo of Sorn Security’s real-time GenAI DLP to see how it can protect your data across every AI interaction. You can also download Sorn’s AI Compliance Framework for a practical guide to aligning generative AI usage with GDPR, HIPAA, ISO, and other standards. Start now to turn generative AI from a liability into a competitive advantage — securely and compliantly.

Frequently Asked Questions (FAQ)

What are the main risks of using third-party generative AI tools in enterprise environments?

Third-party generative AI tools such as ChatGPT, Microsoft Copilot, and Claude can introduce serious risks including data leakage, non-compliance with regulations like GDPR and HIPAA, shadow AI usage, and lack of visibility into data handling. These risks are heightened in regulated industries like finance, healthcare, and government.

How can enterprises prevent data leaks when employees use generative AI tools?

To prevent GenAI data leaks, enterprises should implement real-time GenAI DLP solutions that monitor and control prompt-level interactions before data is exposed. These tools provide semantic analysis, context-aware filtering, and policy enforcement. Shadow AI detection and employee education also play key roles.

Is ChatGPT usage GDPR and HIPAA compliant by default?

No. ChatGPT is not inherently compliant with GDPR, HIPAA, or similar regulations. Unless strict controls are in place, any input of personal, medical, or financial data can violate compliance requirements. Enterprises must use AI governance policies and technologies that audit and restrict unsafe usage.

What frameworks should we follow to ensure responsible AI governance?

Enterprises should align with the NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001 for AI management systems. These frameworks guide organizations in identifying, measuring, and mitigating risks associated with AI, including those from third-party vendors.

How is real-time GenAI DLP different from traditional DLP?

Traditional DLP tools are designed to detect sensitive data in email, file transfers, or endpoint activity. Real-time GenAI DLP, on the other hand, inspects and controls data at the moment of interaction with AI tools—such as when users enter prompts—enabling proactive prevention of sensitive data exposure.

What industries are at the highest risk when adopting third-party AI tools?

Industries with strict regulatory oversight—such as banking, insurance, healthcare, telecommunications, legal services, and the public sector—face the highest risks. These sectors handle large volumes of PII, PHI, PCI, and other sensitive data that require strict governance when using external AI platforms.