Ensuring Data Privacy in the Generative AI Era

The Generative Artificial Intelligence (GenAI) era has unraveled as a promise delivered enabling myriad innovative opportunities and efficiency across all sectors and it continues to transform businesses and the technology landscape. Data fed into and processed by GenAI to generate contents remain a part of the AI cloud and the applications continuously use these datasets and learning patterns to create different contents with reduced human intervention.
While artificial intelligence presents great opportunities for creativity and innovation, there seems to be a thinning of the gap between data generation on one hand and data privacy on the other. This raises concerns of privacy and intellectual property (IP) rights and a demand for due consideration and planning in the deployment of AI tools and applications. The deployment of GenAI, despite the vast opportunities it presents, requires a responsible and careful use of its tools as well as a commitment to heightened data security and privacy. This is because of the potential of exposing sensitive data such as personally identifiable information (PII), protected health information (PHI), and financial data. Furthermore, the accessibility to AI tools is often abused by malicious actors to exfiltrate sensitive information for ill-intents.

Data Privacy and Generative Artificial Intelligence

Since AI tools rely on different datasets to operate and generate results, there are concerns about how this information is collected, processed, and stored (Torm, 2023). Enforcing data privacy in the exploration of generative AI is pivotal to ensuring ethical and regulatory compliance while ascertaining trust and confidence in the handling of sensitive information. Organisations, businesses and governments have developed different laws to guide the ethical use of GenAI tools in accordance with regulatory standards and to ensure the protection of data and data owners. For instance, New Zealand’s “Interim Guidance for Generative AI in Public Service” offers advice about using GenAI tools in public service in such a way that the tools are safely deployed without the violation of clients’ privacy and confidence.

Practical Steps to Ensuring Data Privacy while Deploying GenAI

The opportunities inherent in the use of GenAI are vast; businesses enjoy such benefits as improved efficiency and productivity from the appropriate use of these tools, however, the risks of an inappropriate deployment can be quite devastating resulting in financial loss and reputational damage. To maximize the benefits of these applications and mitigate risks especially in relation to data privacy and non-violation of standards, these steps can be considered.

1. Be Aware of Potential Risks
Do not be caught in the frenzy of deploying generative AI applications that you lose caution of the potential risks involved in using the technologies. One of the privacy concerns with the use of artificial intelligence is the potential for unauthorized access to personal information and its unscrupulous use by malicious actors. An awareness of the risks associated with using GenAI will guide how data is handled and what kind of data is fed, processed and stored in the AI systems. GenAI tools differ in relation to their modalities, outputs, security protocols and risks, consequently, it is safe to understand and acknowledge these risks in order to know how to avoid and mitigate them.

2. Ensure Regulatory Compliance
To achieve data privacy while engaging with AI tools is to ensure that applicable regulations such as the GDPR and the EU AI Act which is expected to come into effect in 2026 following a provisional agreement are understood and adhered to in order to prevent litigations and repercussions. Torm (2023) recommends that a structured set of instructions, responsibilities, transparency and accountability should be established to ensure that everyone involved in the use of AI technologies do so responsibly and in compliance with set standards.

3. Maintain Privacy with Training Data
Since AI trains with a whole lot of data, it is appropriate to use anonymized or synthetic data in this process to prevent data theft and a breach of trust especially when confidential data such as personal identifiable information and financial data are involved. The concern with privacy is heightened with GenAI because unlike traditional AI, the latter utilizes the “black box” where data processing, result generation and everything in between occurs. Another means of ensuring privacy is to encrypt data. Data encryption has been considered the most reliable and effective means of ensuring the privacy of all collected data. This is also applicable to protecting data within the context of AI usage. Advanced encryption protocols can be deployed to ensure all data at rest, in transit, and in use is appropriately protected from unauthorized access and damage (Baig and Rehan, 2024).

4. Avoid Using GenAI for Sensitive Data
I believe that the easiest way to ensure data privacy is to simply not use sensitive data on third-party GenAI tools. However, if such datasets have to be used, they should be used with obtained consent and only for applications within an organization’s internal network (Baig and Rehan, 2024). The fallouts of contravening this guidance include but are not limited to data theft for malicious motives or financial gain, breach of trust and litigations from non compliance, all of which outweigh the potential benefits of using GenAI.

5. Regular Training of Employees
The Generative Artificial Intelligence terrain is a dynamic one with evolving regulations and developments. Hence, organizations must be intentional about regularly training their employees and providing them with the resources to keep them abreast of the constant developments and updates related to GenAI. This will also equip them with the best practices to ascertain the responsible use of AI. Furthermore, these trainings should be role-specific and they must reflect the changes in the AI terrain. This will provide employees with the skills to identify threats, understand data classifications, avoid feeding AI with sensitive data and prevent insider threats.

6. Organizational Transparency and Client Engagement
A rule of thumb in business and customer relations is that accountability and transparency be prioritized. Clients entrust their personal data and other sensitive information such as names, dates of birth, social security numbers and card numbers to their service providers and it is only right that businesses preserve this confidence and that they also inform their clients about how and for what purpose their data are being used. Today, customers are more aware about their rights and they know what to do when their rights have been violated. Consequently, organizations should display transparency and accountability by protecting customers’ data and by providing information about how the customers’ information is used.

7. Develop a Reliable Data Governance Framework
Organizations must develop a robust and reliable data governance framework for the management of AI deployment and for regulatory compliance. As innovative and interesting as GenAI is, it will only operate with the data that is fed into it, hence, there should be a structured guide for how data is handled with AI. A robust framework will ensure the following:

• Data Minimization: This will ensure that when using GenAI only the required data is used, processed, collected, and stored. This will prevent illegal access to unnecessary sensitive data and prevent the risks of data breach.
• Data Classification: Organizing data based on categories, sensitivity, and purposes provides guidance in data use on AI tools. It also ensures appropriate management and regulatory compliance in relation to the storage and retrieval of data.
• Data Retention: A data deletion strategy should be created to safely dispose of data that is no longer needed.
• Data Accessibility: The accuracy and relevance of data generated by AI technologies is dependent on the quality type of data that is fed into them. A robust data governance framework provides a strategy to access and use quality data when it is required.

Finally, it is important to conduct a regular internal and external evaluation, audits, and compliance checks of the AI applications in use within an organization to ascertain that they are deployed in compliance with ethical and regulatory standards. Organizations can also audit their governance structure to identify and correct weaknesses in their AI policies for effective implementation. This will promote transparency and trust as well as provide useful insights for future audits.

Benefits of Ensuring Data Privacy in GenAI Usage

Let’s take a look at some of the reasons you must enforce data privacy while you use GenAI tools:
● Data Protection: Prioritizing data privacy while using GenAI ensures that all sensitive data is protected from unauthorized access, theft, damage, or manipulation. This will also reduce the risks of data breaches, potential business and financial loss, reputational damage, and legal liabilities.
● Business Credibility and Trust: Businesses thrive not only on expertise, skills, capital and operational efficiency, they survive also on the strength of their credibility and customer trust. Organizations retain the trust of clients and partners when they display an accountable use of GenAI and guarantee the privacy of confidential data. This also translates to enhanced brand reputation, increased customer loyalty, competitive advantage, and business growth.
● Legal and Regulatory Compliance: Adherence to data privacy and protection laws, and regulatory standards saves businesses from fines, legal sanctions, financial and reputational damage. It also positions organizations for long-term success and sustainability.
● Reduced Security Risks: Data privacy reduces the threats of cyberattacks and insider threats even in GenAI usage. Implementing security measures such as encryption, access controls, and security monitoring reduces the chances of data breaches, downtime, diminished productivity, financial losses, and litigation.
● Improved Productivity and Efficiency: Since GenAI tool usage improves business production and efficiency, ensuring that data privacy is enforced further enhances these aspects of business operation and reduces bottlenecks. Data privacy compliance in the deployment of GenAI tools also enables the automation of security tasks, optimizes resource allocation, centralizes security control and drives cost savings.

Conclusion

Generative Artificial Intelligence (GenAI) has proven to be the most significant technology of contemporary times. It however calls for a responsible use to ensure that privacy, which is the priority concern of clients and the primary responsibility of organizations, is not violated. At Telliswall, we help businesses implement appropriate data access and control. We also provide training on responsible AI use in alignment with industry best practices and regulatory requirements. Get in touch with Telliswall to learn more about how you can leverage GenAI technologies to stay ahead in your business while complying with relevant data privacy and AI regulatory requirements.

References

Baig, A. and Rehan, A. (2024). Managing Privacy in the Data & Generative AI Era in New Zealand
https://securiti.ai/privacy-in-data-and-generative-ai-in-new-zealand/

Coleman, S. (2024). Guardians of data: Enhancing privacy and security in the age of
generative AI
https://medium.com/slalom-data-ai/guardians-of-data-enhancing-privacy-and-security-in-
the-age-of-generative-ai-37cf3abdd874

Jummy (2024). Improving Data Privacy and Security Compliance
https://telliswall.org/improving-data-privacy-and-security-compliance/

Torm, N. (2023). Steps to Safeguarding Privacy in the Gen AI Era
https://www.cognizant.com/dk/en/insights/blog/articles/steps-to-safeguarding-privacy-in-
the-gen-ai-era

University of Arkansas Little Rock (2024). Navigating the Era of Generative AI:
Safeguarding Privacy and Intellectual Property
https://ualr.edu/itservices/2024/03/05/navigating-the-era-of-generative-ai-safeguarding-
privacy-and-intellectual-property/