When AI Convenience Becomes a Security Risk

Once you paste that customer claim form, internal memo, or strategic document into a public AI chat, you may have just handed your competitive advantage to your competitor.

Large Language Models are rapidly transforming how organizations operate. These AI systems help businesses analyze documents, automate customer support, process data, and improve operational efficiency. They have introduced new levels of speed and productivity across multiple industries. Public AI tools powered by LLMs, such as ChatGPT, have helped popularize this technology and make it accessible to millions of users worldwide. Their convenience and ease of use have accelerated AI adoption across sectors including insurance.

However, while these tools offer unprecedented convenience, they pose serious risks that many organizations fail to recognize until it's too late. What was once trade secret and company confidential information could now be common knowledge in the hands of your competitors. Sensitive customer data, proprietary business processes, and strategic plans can be inadvertently exposed through seemingly harmless interactions with public AI platforms.

As LLM adoption continues to expand, recent real world incidents have revealed a growing concern that organizations must address. Convenience can sometimes introduce significant security and privacy risks. The root of the problem lies in a fundamental misunderstanding of how public AI tools actually work.

The Misunderstood Reality of LLM Privacy

Many users assume that information entered into public AI tools is confidential by default. In reality, this assumption can be misleading. When users interact with publicly available LLM platforms, submitted content is processed within cloud environments to support system performance, safety monitoring, and compliance requirements. In some cases, user data may also be retained for operational or legal reasons.

Once you paste that customer claim form, internal memo, or strategic document into a public AI chat, you may have just handed your competitive advantage to your competitor. The data you thought was helping you work faster could be training the same AI that your competitor uses tomorrow.

Even when platforms allow users to opt out of data being used to train AI models, this does not automatically guarantee full confidentiality. Opting out of training generally prevents conversations from being used to improve future AI models, but the data may still be processed or temporarily stored to support system functionality and compliance obligations. Once sensitive information is entered into a public AI system, organizations may lose direct control over how that data is handled internally. For industries that depend heavily on confidentiality, this creates serious risk exposure.

When Private LLM Conversations Became Public

One widely discussed incident involved an experimental sharing feature that allowed users to make AI conversations searchable on the internet. Although users had to manually opt in, many misunderstood how visible their conversations could become. Thousands of conversations later appeared in public search results. Some included extremely sensitive discussions involving mental health struggles, addiction recovery, personal relationships, and identifying personal details such as names and locations.

The feature was eventually removed, but the incident demonstrated how easily confidential information can become public when users do not fully understand AI sharing and privacy settings. This situation reinforced an important reality. Technology is only secure when users understand how it operates.

When Cybersecurity Experts Made the Same Mistake

Another major warning came from a senior cybersecurity official who uploaded government documents into a public AI chatbot despite internal restrictions. The documents were marked for internal use only and triggered internal security alerts. Although the files were not classified, the incident raised serious concerns about what security professionals describe as Shadow AI. This refers to employees or executives using unauthorized AI tools outside organizational security controls.^[1]

Security experts warn that once sensitive data is entered into public AI systems, organizations can lose visibility and control over where that data might appear or how it could be reused. This issue is not limited to government agencies. Several multinational corporations have restricted public AI tools after employees unintentionally shared internal code, trade secrets, and confidential communications while attempting to improve productivity.

Why LLM Risks Are Especially Dangerous for Insurance Companies

The insurance industry relies heavily on confidential customer data. Every claim submission often includes sensitive information such as:

Medical records
Personal identification details
Financial information
Accident documentation
Legal and investigative reports
Personal customer narratives

Uploading this type of information into public AI systems introduces serious risks including regulatory violations, legal liability, reputational damage, and loss of customer trust. At the same time, insurance companies cannot ignore artificial intelligence. LLM technology has demonstrated how AI can significantly improve efficiency, customer interaction, and claims processing speed. The challenge is not avoiding AI. The challenge is using AI responsibly and securely.

The Growing Need for Safer AI in Insurance

Insurance providers are increasingly seeking novel solutions that improve workflow while ensuring strong regulatory and privacy protections. This is why we built GYE. GYE is a modular, end-to-end insurance claims processing and automation platform built by Summer Health. The platform is designed with a security and privacy-first approach, ensuring that insurers can leverage cutting-edge technology to rapidly accelerate workflow, while maintaining full control over sensitive customer information.

GYE enables insurance companies to automate and streamline claims operations through implementations such as Structured workflows, Intelligent document processing, Guided claim submissions, Fraud detection support and seamless integration across insurance systems. The platform is designed to operate within secure enterprise environments while aligning with data protection and regulatory compliance requirements across the insurance industry.

Summer Health Limited operates within a broader insurance ecosystem that supports the distribution and servicing of insurance products through digital platforms and partner networks. This helps insurers reach customers while maintaining strict privacy, compliance, and operational control. Organizations interested in modernizing their claims processing can learn more about GYE by contacting the Summer Health team.

Final Thoughts

LLM technology is reshaping how organizations interact with data and customers. It enables faster decision making, improved automation, and enhanced customer engagement. However, recent security incidents demonstrate that AI adoption without strong governance and privacy safeguards can introduce significant risk. Industries that rely on customer trust must balance innovation with responsibility. The insurance industry is at a turning point. Organizations that invest in secure and industry-focused platforms will gain operational advantages while protecting customer data and brand reputation.

Platforms such as GYE demonstrate that innovation, efficiency, and security can coexist without compromise. The future of insurance will not be defined by how quickly companies adopt AI. It will be defined by how responsibly they implement it.

References

1. ITPro. "CISA's interim chief uploaded sensitive documents to a public version of ChatGPT – security experts explain why you should never do that." https://www.itpro.com/security/data-protection/cisas-interim-chief-uploaded-sensitive-documents-to-a-public-version-of-chatgpt-security-experts-explain-why-you-should-never-do-that

When AI Convenience Becomes a Security Risk and Why Insurance Needs Safer AI Solutions

References