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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. Local Tools, Hosted Tools, and Security Review

Everyday Engineering Assistance · Beginner to Intermediate

Local Tools, Hosted Tools, and Security Review

Local versus hosted tradeoffs, least privilege for assistants, and how to ask the security team.

On this page

    Local versus hosted

    Local and hosted describe execution arrangements, not automatic approval. A nominally local application may call a remote model, send telemetry, or sync logs. A hosted deployment may be company-operated or provider-operated. Inspect the actual data flow, configured account, and applicable terms.

    An approved local-only configuration can narrow external exposure but still needs policy, updates, and access control. Both local and hosted configurations require checks of retention, logging, training use, and any external services against the actual approval conditions.

    Dimension

    Local tool

    Hosted service

    Where input travels

    Verify model endpoint, telemetry, plugins, and sync; local UI alone proves nothing.

    Verify the approved processing boundary, endpoints, and subprocessors.

    Retention and logging

    Check local storage, backups, telemetry, and access.

    Check current contractual terms and configured retention and training-use settings.

    Upkeep

    Local install, update, and capacity work.

    Maintained by the provider under stated terms.

    Permitted use

    Only classes and tasks approved for this configuration.

    Only classes and tasks approved for this deployment and account, including proprietary data when explicitly permitted.

    The block below compares prompt sizes with stated example values. Units are fictional and checkable by hand.

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    assume 1 million input tokens cost 3 units (stated example parameter)
    sanitised prompt = 2000 tokens, full dump = 20000 tokens (stated example sizes)
    cost = tokens / 1000000 x 3 units
    sanitised: 2000 / 1000000 x 3 = 0.006 units
    full dump: 20000 / 1000000 x 3 = 0.06 units
    saving = 0.06 - 0.006 = 0.054 units per request
    100 requests: 100 x 0.054 = 5.4 units

    Least privilege and human approval

    Assistants and agents receive the smallest access that completes the task. Narrow files, read-only mounts, and short-lived credentials limit what a mistaken action can reach.

    Files and logs can carry injected instructions that steer an assistant. Human approval stays required before risky actions such as writes, submits, or access changes.

    • Scope: only the files and directories the task names.

    • Mode: read-only by default; writes need explicit approval.

    • Time: short-lived access that ends with the task.

    How to ask the security team

    Security review is a normal step, not an exception. A short note with the facts gets a faster answer.

    • Tool: name and version plus the approval entry sought.

    • Data: class of input; attach a synthetic sample or a sample permitted for the review channel.

    • Flow: where input travels and how long outputs are kept.

    • Ask: one clear question plus the date the answer is needed.

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    schematic transcript (illustrative, not from any real system)
    engineer: internal simulator logs through tool X, synthetic sample attached, retention question
    security: approved for internal class with conditions Y (stated example reply)
    engineer: records the approval reference and follows conditions Y

    Common mistakes

    Common pitfall

    Approval for reading is not approval for an agent that writes. Agentic actions need their own approval with explicit bounds.

    Interview

    Likely probes: deployment choice and approval scope.

    • Both local and hosted configurations need verified data flows and class-specific approval.

    • Least privilege bounds files, mode, and time for every run.

    • Human approval precedes writes, submits, and access changes.

    Check yourself A hosted assistant and a local assistant run the same redacted prompt. Which output can be trusted without verification?

    Neither. Both outputs are drafts until tests, simulation, or review confirm them.

    Check yourself An agent asks to update an access list to unblock a test. What happens next?

    A human reviews and approves first. Access changes stay outside autonomous actions.

    Related topics

    • Policy and approved tools

    • Verifying AI output

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    See also

    • What Never to Paste
    • Policy and Approved Tools