完全可见和控制 AI 生成的代码。快速、安全地创新。
The widespread adoption of AI coding tools presents a new challenge: a lack of visibility and governance over AI-generated code.
of developers use or plan to use AI tools in their development process.
Stack Overflow
of AI-generated code contains security vulnerabilities.
Veracode
of security teams lack visibility into AI usage in their codebase.
Cycode
The new AI capabilities of SCW Trust Agent provides the deep observability and control you need to confidently manage AI adoption in your secure software development lifecycle (SDLC) without sacrificing security.
Gain deep visibility into AI-assisted development, including which developers are using which AI/LLM models and on what code bases.
Automate policy enforcement to ensure AI-enabled developers meet secure coding standards before their contributions are accepted in critical repos.
Connect AI-generated code to developer skill levels, vulnerabilities produced, and actual commits to understand true security risk being introduced.
Without a way to manage AI usage, CISO’s, AppSec and engineering leaders are exposed to new risks and questions they can not answer. A few concerns include:
SCW empowers organizations to embrace the speed of AI-driven development without sacrificing security. AI Signals is the first solution to provide visibility and governance by correlating a unique combination of three key signals to understand AI-assisted developer risk at the commit level.
Get a full picture of AI coding assistants and agents, as well as the LLMs powering them. Discover unapproved tools and models. No more “shadow AI.”
Gain deep visibility into AI-assisted software development, including which developers are using which LLM models and on which code bases.
Connect AI-generated code to actual commits to understand the true security risk being introduced. Automate policy enforcement to ensure AI-enabled developers meet secure coding standards before their contributions are accepted.
Trust Agent: AI gives companies visibility over the risks introduced by developers using LLM-backed, code-generating tools. The solution does this in three steps:
By correlating these key signals, Trust Agent: AI provides actionable information to security and engineering teams including unsanctioned LLM model use and identification of developers with limited secure coding knowledge who are committing AI-generated code.
