The risks of AI-generated code are real — here’s how enterprises can manage the risk.

Source: DNyuz | Author: VentureBeat | Read the full article in English

The rise of artificial intelligence (AI) in coding has transformed how software is developed, with predictions suggesting that AI could soon generate the majority of code used in applications. This shift raises important questions for businesses about the quality and security of AI-generated code. Unlike traditional coding practices that involve careful oversight, AI-generated code can introduce bugs and vulnerabilities, leading to significant operational challenges.

Experts emphasize the need for companies to understand the origins of their AI-generated code and to implement robust review processes. Tools that analyze source code are evolving to help organizations manage the risks associated with AI, ensuring that the code meets necessary standards for quality and security. As companies increasingly rely on AI for coding, they must remain vigilant about potential issues that could arise from using automated tools.

To navigate this new landscape, businesses are encouraged to adopt best practices for reviewing and validating AI-generated code. By doing so, they can harness the benefits of AI while minimizing the risks, ultimately ensuring that their software remains reliable and secure in an increasingly complex digital environment.

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