I’ll come right out and say it: Protecting our clients’ data privacy is my biggest concern about using AI.
Nonprofits keep a lot of sensitive data:
- Donor information: Names, addresses, email contacts, and financial contributions.
- Beneficiary details: Personal and sometimes highly sensitive information such as health records, income levels, or family circumstances.
- Volunteer data: Identification details, availability, and areas of expertise.
This information is critical for our clients to carry out their missions, whether it’s through fundraising, program delivery, or volunteer coordination.
But . . . AI systems are not transparent, which makes it difficult to understand how their data is being used and shared. Plus, the “tech bro” industry hasn’t historically been forthcoming about their misuse of private information.
OK, yeah, I have some trust issues.
To make matters riskier, AI processes data in cloud environments. This could expose our clients’ sensitive information to cyber threats. A data breach not only jeopardizes individuals’ privacy but can also severely damage the reputation of the nonprofit.
This makes AI usage problematic for nonprofits. Their stakeholders demand accountability.
This doesn’t mean we won’t use AI. But it does mean that we need to be super careful in deciding which information is shared.
I asked AI what it recommended that nonprofits should do. Here are its (somewhat generic) answers (in italics):
Best Practices for Nonprofits
To adopt AI responsibly while safeguarding data privacy, nonprofits should consider the following best practices:
- Conduct a Data Privacy Audit
Before implementing AI, nonprofits should assess their existing data collection, storage, and protection practices. Identifying vulnerabilities early can help mitigate risks.
- Prioritize Data Minimization
Collect and retain only the data that is absolutely necessary for the organization’s operations. This not only reduces the risk of breaches but also aligns with privacy regulations.
- Implement Robust Security Measures
Use advanced encryption, regularly update systems, and conduct penetration testing to ensure the security of sensitive information. Data stored in the cloud should be safeguarded with strong access controls.
- Choose Transparent AI Tools
Select AI vendors that prioritize transparency and accountability, providing clear documentation on how their tools work and how data is used.
- Train Staff in Data Privacy
Educate employees and volunteers about data protection regulations and ethical considerations. Ensure they understand their roles in safeguarding sensitive information.
What does this all point to? Well, implementing AI is not going to be simple. We can’t rush into this (so don’t go uploading data into ChatGPT). It’s going to take a lot of pre-planning. I address this in my next blog in this series, The Complexity of Implementing AI.