Cybersecurity

AI Code Security: Enterprise Governance for AI Generated Code

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The article discusses the emerging field of AI Code Security, which aims to secure code generated through artificial intelligence (AI) technologies while also leveraging AI for security purposes. Here are key points and insights from the article:

Threat Modeling and Design Reviews:
- Security teams need to create a centralized repository for security use cases and best practices.
- Continuous threat modeling is essential, which involves creating ongoing threat models of your environment by connecting to code and knowledge bases.
- AI can provide design reviews for upcoming changes and flag major changes in progress.
AI SAST (Static Application Security Testing) and Pentesting:
- These tools use AI methodologies to discover and validate existing issues while continuously assessing new deployments.
- They aim to improve and standardize code by automatically gathering security context when applicable.
Agent-Based Improvements:
- Agents can gather necessary security context, improving and standardizing code as they perform tasks.
- This ongoing guidance helps developers make better coding decisions and implement secure features.
Developer MDM (Management and Monitoring):
- Developer MDMs allow the enforcement of AI coding standards while providing broader governance of developer endpoints.
- Teams need to enforce

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