Generative AI comes with unique challenges with regards to data privacy and security, which have spurred the creation of regulations like the AI Act from the EU. Cisco even cites that 27 percent of companies have banned its use for now.
But while there are challenges with implementing generative AI, just as there are with any form of emerging technology, there are several benefits to leveraging AI in insurance.
Rather than banning its use and hindering opportunities for innovation and business growth, insurers should develop a deep understanding of the critical data privacy and security issues related to generative AI and learn how to safeguard customer information.
Data Privacy and Security Concerns in Generative AI
Although data privacy and data security are similar areas of concern, they aren’t exactly the same. Data privacy refers to the handling of personal data in compliance with regulations to protect individual rights, while data security involves the measures taken to protect that data from unauthorized access and breaches.
Data Privacy Concerns
Here are a few ways generative AI poses a risk to insurers and their customers:
Data Privacy Regulations
While data privacy regulations may not specifically reference AI yet, they do reference the amount of data companies collect and how that data is obtained, how much of that data is considered sensitive, and what may constitute additional assessments and protections. These regulations mandate strict data protection measures and give individuals greater control over their personal data.
Data Collection and Usage
Generative AI models require extensive data to learn and generate new content. This data often includes personal and sensitive information from customers. The primary concern here is the potential for not obtaining consent for, the misuse of, or the over-collection of data, leading to privacy violations.
Data Storage and Transmission
Insecure storage or transmission methods can lead to data breaches, exposing sensitive personal information. Data transfers are so important, in fact, that up until recently, data transfers between the EU and the U.S. have been almost impossible as the GDPR’s guidelines have restricted the ability to do so.
Data Anonymization and De-identification
To protect privacy, data should be anonymized or de-identified so that bad actors can’t get ahold of sensitive data and trace it back to an individual. However, ensuring complete anonymization can be challenging, especially when dealing with large data sets. For example, if a person’s name is removed but their zip code, age, and area code remain visible, bad actors can still narrow them down and re-identify them based on other stored information.
Data Security Concerns
Though the data that generative AI uses is a massive concern in and of itself, AI models can be vulnerable as well, even when insurance companies make the effort to collect data responsibly. There are two primary types of security insurers should be aware of for their generative AI efforts.
Model Security Several generative AI models are open source as it is, including the immensely popular ChatGPT, which leaves companies vulnerable. Worse still, it also leaves the AI systems themselves open. Generative AI models can be targets for adversarial attacks, where malicious inputs are designed to manipulate the model’s output or reveal underlying data.
System Security Beyond the AI models, the broader AI system, including software and hardware components, must be secured. Vulnerabilities in these components can be exploited, leading to significant security breaches.
Ensuring compliance with data privacy laws, industry security standards, and best practices is essential for maintaining robust AI data privacy and security. These standards and regulations provide guidelines for managing risks systematically so that insurers can keep their customers safe.
Best Practices for Ensuring AI Data Privacy and Security
There are a few best practices that insurers can adopt to help keep their customers’ data private and secure in the face of generative AI.
Here are three fundamental best practices to help keep any data leveraged by generative AI tools safe:
1. Establish a Data Governance Framework
Forming robust data governance frameworks is critical for managing data privacy and security effectively. These types of frameworks help organizations develop standards around how internal teams manage data and protect it from security risks.
To do so, organizations should start by defining their data governance policies to ensure their data is both safe and secure and it follows the right data privacy regulations. From there, they should assign data stewardship roles to establish who is responsible for setting how the company collects and processes data. That person will also help implement data management processes.
Lastly, it’s critical that organizations create an ongoing review practice to ensure everything in the framework is still relevant and being practiced. When it’s not, insurers should update them to their current and future state needs.
2. Implement Advanced Security Measures
Advanced security measures such as encryption, access controls, and continuous monitoring are essential for protecting data. Encryption ensures that data is unreadable without the correct decryption key, while access controls limit data access to authorized personnel only.
As part of this process, organizations should adopt a security framework like System and Organization Controls (SOC) 2, the National Institute of Standards and Technology (NIST) cybersecurity framework, or the two certifications from the International Organization for Standardization (ISO). Each of these frameworks will help insurance organizations evaluate their IT and cybersecurity systems. On top of its cybersecurity framework, NIST recently released an AI Risk Management Framework to help people and companies understand better ways to manage the risk associated with AI.
Other advanced security measures include implementing encryption for data at rest and in transit, setting up multi-factor authentication and role-based access controls, establishing continuous monitoring and anomaly detection systems, and ensuring AI models use proper architecture to eliminate points of entry for things like prompt injection.
3. Perform Regular Audits and Assessments
Regular audits and assessments help ensure ongoing compliance with data privacy and security standards. In fact, several regulations and certifications require yearly audits, though performing more than once per year may be essential for some insurers. These audits identify potential vulnerabilities and provide insights into areas needing improvement.
To start, insurance companies should schedule regular audits based on what timing makes sense for their organization. As part of the audit, companies should review their external-facing policies as well as their internal policies for data collection and processing.
Then, they need to determine if they’re following those policies and whether there are any new regulations or updates to security frameworks to ensure they’re in compliance. If the organization does find any problems during its assessment, it should address any actions that need to take place immediately.
Conclusion
Data privacy and security are paramount to insurers’ success, and they are especially critical components of successfully incorporating generative AI into their workflows. By understanding key concerns and adopting best practices, carriers can protect sensitive data, ensure regulatory compliance, and maintain customer trust.
To learn more about defining modern data practices that prioritize privacy and security, read our whitepaper, “Guide to Data Modernization.”