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Course contents

  • Getting Started with AI for VLSI Engineers5
  • Just Enough AI: Models and Context15
  • Prompting13
  • Trust and Verification14
  • Using AI for Engineering Work17
    • RTL and Testbench Assistance
    • Debugging with AI
    • Scripts and Automation
    • Data, Privacy and IP
      • What Never to Paste
      • Policy and Approved Tools
      • Sanitise Before Sharing
      • Local Tools, Hosted Tools, and Security Review
  • Engineering Walkthroughs7
  • Practical Foundations for AI Workflows4
  • Engineering Data5
  • Retrieval-Augmented Generation6
  • Agents and Tool Use18
  • Agent Design8
  • MCP and Skills6
  • Multi-Agent Design6
  • Practical Capstone6
  • Tokens, Cost and Latency12
  • Agentic Workflows for Chip Teams18
  • Interview and Judgment10
  • AI Skills: Full Course Index
  • Working with AI Glossary
  • Working with AI Cheatsheet
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‹Data, Privacy and IP
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Using AI for Engineering Work
  4. Data, Privacy and IP
  5. Policy and Approved Tools

Everyday Engineering Assistance · Beginner to Intermediate

Policy and Approved Tools

Company policy first: approved tools, data classes, and the checks before any paste.

On this page

    Policy is the first rule

    Company policy decides what may be shared and through which tools. It outranks convenience, deadlines, and generic advice.

    Retention, logging, and training use vary by service. The policy states the approved handling for each tool.

    Approved tools and data classes

    Use your company's actual class names and handling rules. The table below illustrates possible categories, not a universal policy. Approval must cover the data, task, account, deployment, retention, access, and any required review conditions.

    Data class

    Handling

    Public (illustrative)

    Use only tools and accounts permitted for the task by company policy.

    Internal or proprietary (illustrative)

    Use a local or hosted deployment explicitly approved for this class and use, under its conditions.

    Restricted (illustrative)

    Follow the applicable prohibition or documented approval process; do not infer an exception.

    A short pre-paste check

    Four checks fit before every prompt. Any failure stops the paste.

    • Class: name the data class of every part of the input.

    • Tool: confirm the actual account, deployment, and use are approved for that class.

    • Minimise: cut to the smallest excerpt that keeps the question.

    • Verify: plan how the output will be checked before reuse.

    Common mistakes

    Common pitfall

    An approved tool for public text is not approval for proprietary text. Approval covers a tool plus a data class, and both must match.

    Interview

    Likely probes: what approval covers and what stops a paste.

    • Approval pairs a tool with a data class, never a tool alone.

    • Unknown class means stop sharing and seek classification under company policy.

    • Minimising first makes every later check easier.

    Check yourself The policy allows an assistant for public documentation. Does that cover a failing internal script?

    No. Approval pairs a tool with a data class; internal code needs its own approval entry.

    Check yourself Two tools seem equivalent and one is approved. Can the other be used for the same paste?

    No. Retention and logging vary by service, so equivalence must come from policy, not from features.

    Related topics

    • Sanitise before sharing

    • Verifying AI output

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    See also

    • Local Tools, Hosted Tools, and Security Review