TL;DR

AI is powerful—but it's not perfect. Protect yourself by: not sharing private info, double-checking important answers, being aware of bias, and setting simple usage policies for your team or family.

Why it matters

AI tools can leak data, amplify biases, generate misinformation, and fail spectacularly if misused. A few simple rules keep you safe and productive.

Rule 1: Protect your privacy

Don't paste sensitive information into AI tools unless you control the data.

What counts as sensitive?

  • Passwords, API keys, access tokens
  • Personal health information
  • Financial records (credit cards, bank details)
  • Proprietary company data (code, designs, strategy docs)
  • Personally identifiable information (PII): names, addresses, social security numbers

Why it's risky

  • Your conversation might be stored and reviewed (by humans or bots)
  • Data could be used to improve the model (meaning it might show up in others' responses)
  • You might not know where the data is processed or stored
  • Breaches happen—even at big companies

Safe alternatives

  • Use AI tools with strong privacy policies (check for "we don't train on your data")
  • Anonymize data before pasting (replace names, redact numbers)
  • Use local/private AI deployments if you handle sensitive data regularly
  • Assume everything you type could become public

Safety note: Privacy-first AI
Some tools (like Claude for Work, ChatGPT Enterprise) offer privacy guarantees. Read the fine print—don't assume all AI is private by default.

Rule 2: Verify before you trust

AI can hallucinate—confidently state false information. Always verify important facts.

When to double-check

  • Medical advice
  • Legal guidance
  • Financial decisions
  • Technical instructions (especially with code or infrastructure)
  • Historical facts or statistics
  • Citations or sources (AI sometimes makes up fake references)

How to verify

  • Cross-check with reputable sources
  • Ask for evidence or reasoning ("Why do you say that?")
  • Use AI as a starting point, not the final answer
  • Consult a human expert for high-stakes decisions

Example of a hallucination:

Prompt: "Who won the Nobel Prize in Medicine in 2025?"

AI might confidently invent a name and backstory—but the prize hasn't been awarded yet.

Rule 3: Watch for bias

AI learns from human-created data, which means it absorbs human biases—about race, gender, culture, politics, and more.

Where bias shows up

  • Hiring tools that favor certain demographics
  • Loan approval systems that discriminate
  • Image generators that default to stereotypes
  • Language models that echo societal prejudices

How to reduce bias impact

  • Be critical of AI outputs—don't assume neutrality
  • Diversify your sources (don't rely on one AI tool)
  • Test AI with different phrasings to see if answers change
  • When building AI systems, audit for bias and use diverse training data

Jargon: "Bias"
Systematic errors in AI outputs that unfairly favor or harm certain groups. It's not malicious—it's a reflection of patterns in the training data.

Rule 4: Set usage policies

If you're using AI in a team, school, or family, create simple guidelines.

Sample policy (for a small team)

  1. Don't share: Customer data, proprietary code, passwords, or confidential plans
  2. Do verify: Any facts or recommendations before acting on them
  3. Use for: Drafting, brainstorming, summarizing, learning—not final decisions
  4. Disclose: If AI was used to create content for clients or public use
  5. Ask questions: If unsure, check with a manager or privacy officer

For families

  • Don't share personal details (address, school, phone number)
  • Ask a parent before using AI for homework or research
  • Verify facts with trusted sources (books, teachers, official websites)
  • Be kind—don't use AI to create mean, harmful, or fake content

Rule 5: Understand what AI can and can't do

AI is good at:

  • Summarizing long documents
  • Drafting emails, reports, or code
  • Answering common questions
  • Translating languages
  • Generating ideas
  • Finding patterns in data

AI struggles with:

  • Nuance and context (it's a pattern-matcher, not a mind-reader)
  • Original, creative thinking (it remixes what it's seen)
  • Moral or ethical judgments (it has no values)
  • Real-time or recent information (unless connected to search)
  • Explaining its reasoning (often a "black box")

Know the limits, and you'll avoid disappointment (and danger).

Rule 6: Keep humans in the loop

AI should assist, not replace, human judgment.

  • Don't: Let AI make final decisions on hiring, loans, medical treatment, or legal matters
  • Do: Use AI to surface options, draft proposals, or flag issues—then have a human review

Example: Medical AI

AI can suggest a diagnosis based on symptoms, but a doctor should verify, order tests, and make the final call.

Common risks and how to avoid them

Risk How it happens How to avoid it
Data leak Pasting secrets into public AI Use private tools; anonymize data
Hallucinations AI invents facts Verify important info with real sources
Bias AI reflects training data biases Be critical; test with diverse prompts
Over-reliance Trusting AI blindly Keep humans in the loop for decisions
Misinformation AI generates plausible lies Cross-check facts; use AI as a draft, not truth

Teach kids about AI safety

If your children use AI (for homework, fun, research), teach them:

  • Privacy first: Never share your full name, address, or personal details
  • Verify facts: Check AI answers with books, teachers, or trusted websites
  • Think critically: AI isn't always right, even when it sounds confident
  • Be kind: Don't use AI to cheat, bully, or create harmful content
  • Ask for help: If something seems wrong or creepy, talk to a parent or teacher

For teams: AI governance checklist

  • Define what AI tools are approved for use
  • List what data can and cannot be shared with AI
  • Require verification for high-stakes outputs
  • Audit AI outputs for bias and errors
  • Disclose AI use in client deliverables or public content
  • Train team members on safe AI practices
  • Review AI usage quarterly and update policies

What's next?

  • Prompting 101: Learn to get better answers from AI
  • Evaluating AI Answers: Spot hallucinations and check for accuracy
  • Guardrails & Policy Design (coming soon): Advanced safety for organizations
  • Privacy & PII Basics (coming soon): Deep dive into data protection