What is Privacy (in the Context of AI)?
Privacy, in the context of AI, refers to the policies, mechanisms, and legal frameworks designed to govern the collection, storage, processing, and use of personally identifiable information (PII) to prevent unauthorized access, disclosure, or misuse by AI systems.
AI poses a unique challenge to privacy because Large Language Models (LLMs) and machine learning (ML) algorithms are designed to find patterns in vast, often anonymized, datasets. A key risk is membership inference, where an AI model can deduce whether a specific individual’s data was included in the training set, or data leakage, where an AI hallucination accidentally outputs fragments of confidential training data in response to a prompt. For any organization, maintaining robust data governance and adhering to specific AI policy on data handling is paramount to legal compliance and member trust.
Think of it this way: Privacy is the absolute rule that what happens in the vault stays in the vault. If your organization’s AI is the accountant, it can process all the sensitive data, but it is never allowed to disclose any specific individual’s income, contact info, or confidential notes. The risk with AI is that it might accidentally “remember” and reveal a confidential detail, like a unique member ID or a specific donor amount. Protecting that line is a non-negotiable part of responsible AI user behaviour, eh.
Why Privacy Matters for Your Organization
For a leader managing member data and community trust, Privacy is the highest-stakes ethical and legal responsibility when deploying AI.
Community organizations handle highly sensitive information, from membership lists and financial records to confidential applications. Failing to protect this data through clear AI policy and secure AI tool usage can lead to severe penalties under Canadian law (like PIPEDA) and, more importantly, a catastrophic loss of member trust.
Using AI responsibly means implementing the human-in-the-loop (HITL) approach to double-check that no confidential information is ever inadvertently entered into a public-facing AI or exposed in an AI-generated report.
Example
A Chamber of Commerce staff member is using a large language model (LLM) to summarize the comments received from a confidential survey about a controversial local policy.
Weak Approach (Privacy Risk): The staff member copies and pastes the raw, unstructured comments, including names and email addresses (PII), directly into a public-facing AI tool for summarization. The tool’s terms of service allow it to use this input for training, and the PII is instantly compromised.
Strong Approach (Privacy Protocol): The staff member first follows the AI policy mandate: all personally identifiable information (PII) is manually redacted or anonymized before the data is fed into the LLM. The staff member uses a secure, private-instance AI tool that guarantees the data will not be used for model training, thereby protecting the privacy of the survey respondents.
Key Takeaways
- PII Protection: Focused on safeguarding personally identifiable information from exposure by AI systems.
- Unique Risks: AI presents unique threats like membership inference and data leakage via
AI hallucinations. - Legal Requirement: Strict adherence is mandatory for compliance with Canadian laws like PIPEDA.
- Governance Priority: Requires robust AI policy and secure AI tool usage protocols.
Go Deeper
- The Guardrail: Review the internal rules required to protect this information in our definition of AI policy.
- The Mechanism: See the core intelligence that is trained on this data in our guide on machine learning (ML).
- The Counterpart: Contrast this with publicly shared information in our guide on open dataset.