Sharing data with external Large Language Models (LLMs) such as OpenAI, Claude, and Gemini raises substantial security concerns for your business, particularly when you share your and your customers’ data.
We did several experiments and asked ChatGPT and Claude to guess a customer from data masked and anonymized content.
Both were able to accurately reverse engineer a customer name from a finite list of customers, between 89% to 94% of the time.
We asked to generate a Python script and with a little bit of back and forth, we had a future-ready script.
LLM Masking does not work.
We will share how we solved, but let's look at a comprehensive list the what customer-facing and security leaders are already doing.
Protecting your proprietary business information, such as your vision, unreleased products, sensitive sales projections, strategic growth plans, and customer data such as usage, adoption, ROI analytics, is of utmost importance. Data leaking can lead to more than just money loss. It can harm a company’s reputation, give competitors an edge, and break customer trust.
Understanding the full spectrum of security concerns — especially for customer-facing leaders — is critical to safeguarding the company’s and customers’ data assets and ensuring business continuity.
This deep dive into the serious security risks of sharing sensitive company data with LLMs, backed by compelling examples, highlights the urgent need to address these threats.
1. Data Privacy — Sharing sensitive data, such as product roadmaps or financial forecasts, risks exposing information that could undermine competitive advantage or violate privacy laws.
Imagine a company accidentally shares details about future product launches with an LLM provider. If this information leaks or is accessed by competitors, they could launch similar products first, completely disrupting the company’s market strategy.
2. Data Security — Data may be intercepted, hacked, or improperly stored, leading to unauthorized access to critical business information.
Think of a scenario where hackers breach an LLM provider’s servers and gain access to sensitive pricing strategies. Competitors could use this information to undercut prices, costing the company revenue and market share.
3. Regulatory and Compliance Risks — Sharing data with LLMs could violate data protection regulations such as GDPR, CCPA, or industry-specific standards like HIPAA.
Consider a healthcare company using an LLM to analyze patient data. If they unknowingly fail to comply with HIPAA regulations, it could result in hefty fines and significant reputational damage.
4. Intellectual Property Risks — Proprietary algorithms, trade secrets, or innovative product designs shared with LLMs could inadvertently be exposed, learned, or replicated.
A company might share parts of its proprietary code with an LLM for debugging. Later, that code could unintentionally influence the model’s training, enabling competitors to generate outputs resembling the company’s unique solutions.
5. Model Training Risks — Data shared with LLMs might be used to improve the model, potentially making sensitive insights accessible to others indirectly.
Picture a retailer sharing anonymized customer insights with an LLM for trend analysis. If those insights are integrated into the model, another business could query the LLM and inadvertently gain access to patterns specific to the retailer’s proprietary data.
6. De-Anonymization Risks — Even if data is anonymized, advanced models can correlate patterns and re-identify individuals or organizations. Note: Although technically possible, the likelihood of an LLM cross-referencing anonymized data to re-identify individuals or organizations might seem far-fetched unless specific examples or real-world cases support it.
A company might share anonymized customer data with an LLM, but the model could cross-reference public information to identify key customers and their preferences, breaching confidentiality and potentially damaging trust.
7. Breach Notification Delays — LLM providers might delay notifying businesses of data breaches, increasing the time sensitive information remains vulnerable or you may miss the notification.
Imagine a breach in an LLM provider’s system that exposes details about a pending merger. By the time the company is informed weeks later, competitors have already exploited the information, causing financial and strategic harm.
8. Output-Based Data Leakage — LLM outputs can unintentionally reveal sensitive information, leading to unintended exposure.
An employee might use an LLM to draft an internal report. Without realizing it, the generated text includes confidential details about an upcoming acquisition, which are then shared externally during a presentation.
9. Data Sovereignty — Data may be stored or processed in jurisdictions with weaker data protection laws, increasing the risk of unauthorized access by foreign entities.
A company could share customer data with an LLM provider that stores information in a foreign country. Local government agencies might demand access under their laws, exposing the company to potential breaches.
10. Hallucinations and False Outputs — LLMs may generate fabricated or inaccurate information, causing reputational or operational harm.
Imagine an LLM providing a fabricated statistic during a customer-facing presentation. This could lead to loss of trust and damage client relationships.
11. Insider Risks — Employees using LLMs might unknowingly share sensitive information, bypassing internal data protection policies.
An employee drafting a sales proposal with an LLM could unknowingly upload confidential financial forecasts, which might then be misused by the provider or exposed to others.
12. Third-Party Integration Risks — Many LLM providers integrate with third-party tools or rely on third-party infrastructure, introducing additional attack surfaces and potential vulnerabilities.
An LLM provider might use a third-party API for storage. A vulnerability in the third-party system could lead to unauthorized access to sensitive company data.
13. Shadow IT Usage — Employees may use LLMs without authorization or proper oversight, bypassing IT and security policies.
A sales representative analyzing customer data with an unauthorized LLM could expose this data if the provider experiences a breach.
14. Model Behavior Exploitation — Malicious actors could exploit the LLM’s behavior to extract sensitive data unintentionally included in its training set.
An attacker might craft specific queries to an LLM, leading it to inadvertently reveal confidential details embedded in the model’s training.
15. Lack of Data Retention Policies — Providers might store shared data indefinitely without clear retention policies, increasing the risk of future breaches or misuse.
A company could share proprietary data for a project, assuming it will be deleted afterward, only to find it exposed years later during a breach at the LLM provider.
16. Misinterpretation of Output — The LLM may generate outputs that are misinterpreted as factual, potentially causing financial or strategic errors.
Imagine an LLM predicting market trends based on outdated data, leading a company to make poor investment decisions.
17. Insider Threats at the LLM Provider — Employees at the LLM provider could intentionally or unintentionally misuse access to shared data.
A disgruntled employee at the LLM provider might access sensitive company data and sell it to competitors.
18. Cross-Tenant Data Leakage — Multi-tenant architectures in cloud-based LLMs might inadvertently expose data between tenants due to configuration errors. This concern is technically valid but standard security practices usually make sure configuration errors are handled.
A misconfigured database at the LLM provider could allow another client to access your company’s sensitive data.
19. Adversarial Attacks — Attackers may use adversarial inputs to manipulate LLM behavior, extracting sensitive data or producing harmful outputs.
An attacker might craft input queries that trick the LLM into revealing confidential data about an unreleased product.
20. Licensing and IP Ownership Ambiguity — Data shared with the LLM provider might become subject to ambiguous licensing agreements, risking loss of intellectual property rights.
A company might share a proprietary algorithm with an LLM for debugging purposes. Ambiguities in the provider’s terms of service could allow the algorithm to be used in model training or resold as part of another solution. This could result in direct financial losses, reduced competitive advantage, or lengthy legal battles.
21. Cumulative Risk from Multiple Data Sources — LLMs aggregate data from multiple inputs, increasing the risk of unintended insights or patterns that reveal confidential strategies.
Picture a company feeding separate inputs about sales projections, R&D budgets, and marketing strategies into an LLM. The model (or a bad actor with access to models) could combine this information and infer future business plans, unintentionally exposing them to external users.
22. Phishing and Social Engineering Risks — LLMs could be used to enhance phishing or social engineering attacks, using shared data to create convincing messages.
After accessing customer details through a breach, attackers could use an LLM to generate highly tailored phishing emails targeting key clients.
23. Compromised Authentication — Weak authentication mechanisms for accessing LLM APIs could lead to unauthorized use or data theft.
An attacker might gain access to an LLM API with stolen credentials and use it to extract proprietary company information.
24. Exposure of RAG (Retrieval-Augmented Generation) Databases — When LLMs interact with RAG systems, sensitive information stored in structured or unstructured databases could be inadvertently exposed if proper safeguards are not in place.
RAG works well for simple implementations, where the LLM retrieves data from one source to generate responses. However, when the LLM must query multiple RAG databases in sequence to answer a complex question, it can cause significant latency. This delay can impact user experience and increase the risk of accidental data leakage, especially if sensitive information from multiple systems is aggregated in unintended ways.
For a deeper dive into the limitations and evolution of RAG systems, explore “From RAGs to Riches”.
By adopting these strategies, companies can minimize the financial, operational, and reputational risks associated with sharing sensitive data with LLMs while maximizing the value derived from these powerful tools.
1. Data Minimization
Sharing only essential data and avoiding the inclusion of sensitive or proprietary information reduces the likelihood of leaks or misuse. This approach minimizes financial and competitive risks, protecting the organization from potential breaches and safeguarding its market position.
2. Anonymization and Masking
Stripping personally identifiable information (PII) and sensitive details from data ensures compliance with privacy regulations and mitigates reputational risks. Even in the event of a breach, anonymized data significantly lowers the chance of exposing critical business or customer information.
3. Contractual Protections
Including clear clauses in contracts that specify data use restrictions, breach notification timelines, and indemnification ensures accountability from LLM providers. This reduces liability, protects against financial losses, and reinforces trust in vendor relationships.
4. Vendor Due Diligence
Vetting LLM providers for strong security practices, compliance certifications (e.g., SOC 2, ISO 27001), and breach histories helps ensure partnerships with reliable vendors. This proactive measure minimizes financial risks and strengthens the overall security posture.
5. Encryption
Using end-to-end encryption for data in transit and at rest prevents unauthorized access during transmission and storage. This practice safeguards sensitive business information, maintaining confidentiality and protecting against potential data breaches.
6. Role-Based Access Controls
Limiting employee access to LLMs and restricting usage to approved use cases minimizes insider risks. These measures ensure data protection policies are followed, reducing the chances of accidental or intentional misuse.
7. Monitor Outputs
Regularly reviewing LLM outputs for accuracy and ensuring no sensitive data is included in generated content helps prevent reputational damage. This step ensures client trust is maintained by avoiding accidental leaks.
8. Regular Security Audits
Conducting frequent security assessments of LLM integrations and data-sharing processes identifies vulnerabilities early. This proactive approach reduces the risk of costly breaches and enhances overall security measures.
9. Establish Data Governance Policies
Implementing strict policies and training employees on the appropriate use of LLMs aligns data-sharing practices with business objectives. This reduces compliance risks and fosters a culture of data security within the organization.
10. Consider On-Premise LLMs
Deploying LLMs on secure, company-controlled infrastructure eliminates external dependencies and minimizes data exposure risks. This solution ensures full control over sensitive data and maintains compliance with regulatory requirements.
Beyond Basic Mitigation Strategies that you could use, at Cast, we understand the significant risks posed by sharing sensitive company data with external LLMs. Our solution is designed to ensure your data remains secure while enabling you to leverage the full power of AI.
Since data masking and anonymization (e.g. HPE is Customer2324) shared with LLMs can be easily reverse engineered, we developed a unique and effective solution that bypasses the need of masking and anonymization.
We did several experiments and asked ChatGPT and Claude to guess a customer from data masked content. Both were able to accurately reverse engineer a customer name from a finite list of customers, between 89% to 96% of the time.
Both even generated a Python script that could be further enhanced if we chose to do so.
Since Cast internalizes the information it learns from all your customer-facing data sources and products, instead of always exposing a RAG interface, the Cast solution is much safer. This matters to businesses with competitors as it eliminates the risk of exposing sensitive information to third-party providers, protecting your business’s competitive advantage and maintaining compliance with data protection regulations.
Sharing content is unavoidable to access an external LLM’s specialized capabilities, however Cast approach does not require explicit masking and unmasking of your data, your customer data, personally identifiable information (PII), and obfuscation. Ensures that even if data is intercepted or improperly used, sensitive information is fully protected.
AI Agents That Work Without Raw Data Sharing
Cast AI Agents leverage secure data pipelines that allow them to perform complex tasks—like generating personalized insights, onboarding presentations, and scaling customer success—without requiring raw data to be shared externally.
Key Takeaway
Why This Matters: Helps businesses achieve high ROI from AI without sacrificing security.
By eliminating data-sharing risks and refraining from sharing sensitive information, Cast empowers companies to securely harness the potential of AI without exposing themselves to the 25 critical risks highlighted in this blog post. Cast stands as your reliable partner in scaling revenue and safeguarding your data.
