(source: The Edge)
As artificial intelligence (AI) continues to advance, the distinction between public and private AI becomes increasingly important. Public AI refers to AI applications that are trained on publicly accessible datasets, while private AI utilizes data exclusive to a specific user or organization. With public AI, data privacy concerns may arise, as user-contributed data might not remain entirely confidential. In contrast, private AI provides organizations with exclusive control over their data, preventing competitors from leveraging it.
Public AI can also involve algorithms that use datasets that aren’t exclusive to a specific user or organization. That means if you’re using public AI, you should be aware that your data might not remain completely private.
Private AI is when algorithms are trained on data that’s unique to a particular user or organization. So, if you use machine learning systems to train a model with a specific dataset, let’s say invoices or tax forms, that model will stay exclusive to your organization. The companies providing the AI platforms won’t use your data to train their own models. This way, private AI ensures that your data won’t be used to benefit your competitors.
Let’s talk about integrating AI into training programs and policies. If you’re a cybersecurity staff member looking to experiment, develop, and integrate AI applications while following best practices, here are some policies you should consider:
User awareness and education are key. Make sure to educate users about the risks associated with AI usage and encourage them to be cautious when sharing sensitive information. Promote secure communication practices and advise users to verify the authenticity of the AI system they’re using.
Data minimization is important. Only provide the AI engine with the minimum amount of data necessary for the task at hand. Avoid sharing unnecessary or sensitive information that isn’t relevant to the AI processing.
Anonymization and de-identification are also crucial. Whenever possible, remove personally identifiable information (PII) or any other sensitive attributes from the data before inputting it into the AI engine. This step helps protect privacy.
Secure data handling practices involve establishing strict policies and procedures for handling sensitive data. Limit access to authorized personnel only and use strong authentication mechanisms to prevent unauthorized access. Train employees on data privacy best practices and implement logging and auditing mechanisms to track data access and usage.
When it comes to data retention and disposal, define clear policies and securely dispose of data when it’s no longer needed. Use proper data disposal mechanisms, such as secure deletion or cryptographic erasure, to ensure that the data can’t be recovered after it’s no longer necessary.
Don’t forget about legal and compliance considerations. Understand the legal implications of the data you’re using with the AI engine. Make sure that the way users employ AI complies with relevant regulations, like data protection laws or industry-specific standards.
If you’re using an AI engine provided by a third-party vendor, it’s important to assess their security measures. Make sure the vendor follows industry best practices for data security and privacy and has safeguards in place to protect your data. Validations like ISO and SOC attestation can provide valuable insights into a vendor’s adherence to recognized standards and their commitment to information security.
Lastly, consider formalizing an AI Acceptable Use Policy (AUP). This policy should outline the purpose and objectives of using AI, emphasizing responsible and ethical usage. It should define acceptable use cases and establish boundaries for AI utilization. The AUP should encourage transparency, accountability, and responsible decision-making when it comes to AI usage, creating a culture of ethical AI practices within the organization. Regular reviews and updates ensure the policy stays relevant as AI technologies and ethics evolve.
By following these guidelines, program owners can effectively leverage AI tools while safeguarding sensitive information and upholding ethical and professional standards. It’s crucial to review AI-generated material for accuracy while also protecting the inputted data used to generate response prompts.
