For AI security: implement Personally Identifiable Information redaction (PII)

»PII redaction« or »Personally Identifiable Information redaction«) seems over-relevant to me w/ view to Capacities as private knowledge/note space – and in light of current developments around extended AI integration

– aside from the known more discussions around Mythos from Anthropic, there are some relevant data already on different risks involved in AI-chat info leaking 🚨

See e.g.: https://elephas.app/resources/ai-data-security-risk-cloud-tools

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*PII redaction explainer (LLM summary):*

**» What is PII Redaction?**

PII redaction is the process of identifying and removing or obscuring sensitive information—such as personally identifiable information (PII)—from data to prevent data breaches and protect an individual's identity.

In practice, PII redaction means you can hand someone a file and they cannot learn who the person is, how to contact them, or how to link the data back to a real identity or account. This is different from "hiding" because true redaction removes underlying content rather than covering it visually.

**Common Examples of PII**

• Full names

• Addresses

• Phone numbers

• Email addresses

• Social Security Numbers (SSNs)

• Bank account information

• Government-issued IDs

• etc.

**Where PII Redaction is Applied**

PII redaction can be applied to various file formats including:

• Documents and PDFs

• Images and scanned documents

• Audio files and call recordings

• Video transcripts

**Why It Matters**

PII redaction is essential for:

• Safeguarding customer data during interactions

• Ensuring compliance with data protection regulations (like GDPR, CCPA)

• Maintaining trust with customers and stakeholders

• Enabling safe data sharing for purposes like machine learning training or analytics

• Redaction is used to mask or remove sensitive content so it can be used and distributed while still protecting confidential information.