156: Privacy and AI: Risks and Solutions for Technical Writers

Summary:

In this episode, Ellis Pratt explores the critical issue of data privacy for technical writers using AI tools and chatbots. He delves into the potential risks, from data leaks and copyright infringement to compliance violations and intellectual property concerns. The episode also provides practical solutions and strategies for mitigating these risks, empowering technical writers to leverage AI responsibly and ethically.

Key Discussion Points:

• The Promise and Peril of AI: AI offers significant productivity gains for technical writers (content creation, first drafts, automation of tasks), but introduces critical privacy risks.

• Potential Risks of Using AI:

  • Data Leaks: Inputted data becoming part of the AI model, accessible to others.

  • Copyright Infringement: AI generating content based on competitor data.

  • Data Breaches: Risk of AI providers being hacked.

  • Data Sovereignty: Data stored in different countries potentially conflicting with regulations.

  • Compliance Violations: Risks related to regulated industries (healthcare, finance).

  • Intellectual Property Rights: Ambiguity over who owns AI-generated content.

• Practical Solutions and Mitigation Strategies:

  • Sanitising Content: Replace sensitive data (API keys, product names) with placeholders.

  • Generic Examples: Use generic rather than actual customer data.

  • Limiting Data Input: Provide only the minimum amount of data required.

  • Review and Redact: Carefully review content before inputting to AI.

  • Check Public Domain Status: Determine if the content is already publicly available.

  • AI Provider Privacy Policies: Review data retention policies and opt-out options.

  • Choosing Secure Tools: Select tools with better data deletion options (e.g., Google GeminiAI Studio, Claude).

  • Managing Data Controls: Understand how to control data collection settings (e.g., ChatGPT).

  • Private/Managed LLMs: Consider private, self-hosted, or managed AI models for sensitive data.

  • Develop Policies and Procedures: Create guidelines for team use of AI, tiered approaches based on document sensitivity.

  • Content Filters: Implement filters to check for sensitive information.

  • Audits and Assessments: Engage IT security for impact assessments and security audits.

Actionable Takeaways:

• Prioritise Data Sanitisation: Make it a core practice before using any AI tool.

• Review Privacy Policies: Understand the data handling practices of your AI providers.

• Implement Security Measures: Protect proprietary and confidential information through policies, technology, and human oversight.

• Collaborate with Security and Legal: Engage relevant internal teams to ensure compliance and minimize risk.

• Start Small and Stay Informed: Gradually introduce AI with low-risk documentation and keep up to date on the latest privacy risks and solutions.

Quotes:

• "AI and chatbots offer in technical writing…a huge promise of a way to be more efficient and more effective in what we do. But…we do need to be aware that there is a privacy risk, and we need to address that."

• "AI…is both a powerful productivity tool and a potential risk. So we need to think about those two aspects and manage it."

• "So we're going to be on a tightrope, a privacy tightrope."

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CC Flickr image: Stock Catalog