3 min read

The Legal Risks of Using Generative AI

The Legal Risks of Using Generative AI

The Legal Risks of Using Generative AI

While generative AI tools offer immense benefits, they also come with legal risks. Insurance brokers, producers, and account executives need to be aware of these risks and take steps to mitigate them. The biggest risk is data protection

Are administrative tasks holding back your insurance brokerage firm’s growth potential? The era of Artificial Intelligence (AI) is here to bring transformative change to these monotonous chores and allow you to shift your energy on core revenue-generating activities. 

The likes of ChatGPT, Anthropic, and Perplexity have begun to carve a new landscape in the realm of AI, offering an end to labor-intensive admin duties. As an insurance agent, you might recognize how prevalent such tasks are; consuming up to 60-70% of your workday. This is where AI can make its mark and pave the way for efficiency and productivity. 

An important factor to consider while dealing with modern tech tools is the risk of data security. In the insurance sector, your data often includes Personally Identifiable Information (PII). Care needs to be taken when choosing tools to manage this data, ensuring they prioritize data safety and confidentiality.

When we say “care”, we believe that agents and brokers should look to implement the following as they explore and roll out AI based solutions:

  1. Rely on a single source of truth:
    • The agency management system is the best option here. 
  1. Create personalized experiences without exposing the PII
    • This requires an automated data quarantine system that ensures data protection
    • The AI system MUST be able to constantly scan, detect and report sensitive information
    • You can apply the scanning and detection to your current structured data set and for unstructured data like your communications with clients
    • Data Loss Prevention (DLP) tools such as data masking and tokenization technologies are a must have

DLP features are highly beneficial, providing valuable data insights and helping significantly reduce data-associated risks. De-identification strategies like masking and tokenization are employed to keep client data anonymous and secure.

In simpler words, it’s like having an built-in AI guard that monitors the safe passage of information through your structured (such as excel data or your CRM) AND unstructured (such as emails and documents) systems! 

Here is an example of how AI can scan and protect sensitive data:

Example

Source text

QUOTE SUMMARY, SNHO3

 

Mail To:

John Smith

137 BROADWAY

SAN CARLOS, CA 94070

 

Named Insured(s):

John Smith

 

Agency:

AIS (Auto Insurance Specialists)

PO Box 10160

Santa Ana, CA 92711-0730

 

Proposed Term Effective Date:

12/08/2023, 12:01AM Standard Time

 

Proposed Term Expiration Date:

12/08/2024, 12:01AM Standard Time

 

Covered Property – 137 BROADWAY – SAN CARLOS CA 94070 – San Mateo County

Tokenized text

QUOTE SUMMARY, SNHO3

 

Mail To:

[PERSON_NAME_1]

[LOCATION_1]

[LOCATION_2], [LOCATION_3] 94070

 

Named Insured(s):

[PERSON_NAME_1]

 

Agency:

AIS (Auto Insurance Specialists)

PO Box 10160

[LOCATION_4], [LOCATION_3] 92711-0730

 

Proposed Term Effective Date:

[DATE_1] [TIME_1] Standard Time

 

Proposed Term Expiration Date:

[DATE_2] [TIME_1] Standard Time

 

Covered Property – [LOCATION_1] – [LOCATION_2] [LOCATION_3] 94070 – San Mateo County

As insurance agents you have a unique opportunity to lead the way by adopting AI to revamp mundane tasks and improve the efficiency of our operations. By doing so, you can ensure stronger client relationships with quicker responses and faster processing times, in a safe and protected way. 

If this has got you curious, you can try it yourself! GoodReps has made a free-to-use tool available at https://csr.goodreps.ai/pii-playground – simply copy and paste your text there, and the app can provide you with a tokenized version of that text.

About Olexandr Prokhorenko

Olexandr Prokhorenko is a seasoned technology product leader with an extensive background in AI and tech innovation, having worked with companies such as Facebook/Meta, Splunk, and Zuora. Leveraging his expertise and applied knowledge in generative AI, LLM, he founded a company aimed at automating repetitive and time-consuming, data-intensive service and support tasks through the integration of engagement and record systems. Olexandr runs a dynamic consulting practice, specializing in navigating high ambiguity and pioneering AI applications to help insurance companies secure a competitive advantage. For insight into his innovative AI solutions. For insight into his innovative AI solutions, reach out via LinkedIn https://www.linkedin.com/in/white or email olexandr@prokhorenko.us

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Olexandr Prokhorenko is a seasoned technology product leader with an extensive background in AI and tech innovation, having worked with companies such as Facebook/Meta, Splunk, and Zuora. Leveraging his expertise and applied knowledge in generative AI, LLM, he founded a company aimed at automating repetitive and time-consuming, data-intensive service and support tasks through the integration of engagement and record systems. Olexandr runs a dynamic consulting practice, specializing in navigating high ambiguity and pioneering AI applications to help insurance companies secure a competitive advantage. For insight into his innovative AI solutions. For insight into his innovative AI solutions, reach out via LinkedIn https://www.linkedin.com/in/white or email olexandr@prokhorenko.us

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