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AI Security Engineer Foundations + Certificate — Micah Silverman, Snyk

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youtube.com
Author
AI Engineer
Date
Why it matters

Makes the point that a does not protect data the model can reach; a restricted view does. Gives a checklist for securing agentic development: isolation, scoped credentials, human approval, verified packages.

Key takeaways · AI-distilled
  • In Silverman's grades chatbot exercise, the assistant's refusal itself revealed that it could reach the confidential grades. His fix limits the student assistant's data access, so the data is no longer in the model's reachable at all.
  • A live inventory demo in Snyk's EVO catalogs the models, datasets, servers and skills in a project; adding a policy immediately flagged a prohibited model already in use. Silverman proposes turning these AI bills of materials into CI build gates.
  • The MCP section separates malicious tool descriptions (tool poisoning) from ordinary code flaws, such as a command injection bug in a Figma MCP server, and recommends pairing -focused scans with dependency and source code analysis.
  • In the closing capture-the-flag, several audience prompts failed to extract the grades. Silverman uses that to stress that model behavior varies between attempts, so red-team tests need repeating rather than trusting a single pass.
Terms in this piece · Glossary
  • MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
  • system prompt — The standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.
  • context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
  • AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Read the source www.youtube.com
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