Using AI for Secure Code Creation: Enhancing Software Security
summary
Claims AI reduces defects ~40% and boosts productivity ~60% when used with disciplined prompt engineering; a pipeline is built using GPT‑4 for boilerplate, Claude for fast iteration, Gemini for crypto/QA and Amazon for cloud, with prompts kept under version control. Generates secure React code from a 17‑point machine‑readable rule set, runs static/dynamic scans and LLM reviews, enforces metrics (code coverage target >90%, low cyclomatic/cognitive complexity) and reports that a two‑week build replaces ~20,000 hours of manual work.
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chapters (14)
- 0:00Introduction
- 2:15Transforming AI into a superhuman coding tool
- 3:40Raw AI prompts fail security
- 5:04Training AI like junior dev
- 8:54Multi-engine AI pipeline
- 12:21Prompt engineering lifecycle
- 16:15Storing AI prompts as assets
- 19:20Security validation of AI code
- 23:37Precise prompts for requirements
- 27:31AI explanations and best practices
- 31:06SOLID principles for AI code
- 32:31Evaluating AI code reliability
- 37:49Case study: Secure React code
- 49:35Q&A
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