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Among the many professionals experimenting with the possibilities of generative artificial intelligence are developers. GenAI can speed up the code creation process and help devs tap into unique and innovative solutions. However, if overused or misused, GenAI can also lead to issues ranging from inadequate security to bias.
Not all software is created equal; therefore, not all uses of AI-generated code are equal. To capture the maximum value of AI-generated code while protecting against security threats, organizations must have well-integrated application security programs as part of the software development life cycle so that they can manage risk tolerance and security protocols for each application as the code is being implemented.
GenAI is an accelerator for application security testing. It has the capability, for example, to scan code bases and suggest remediations, shorten the time a vulnerability exists, and suggest best practices. It is low-risk if utilized as a “co-pilot”—that is, as an assistant to a human security tester or developer who is reviewing large amounts of data . -
AI code-generation tools stand to significantly increase the volume of code developers are able to write and produce. However, while these tools can provide relief from growing software demands, they require at least the same level of scrutiny that would be given to code written by a human. Failing to review AI-generated code properly will lead to the introduction of technical debt and, eventually, rework.
Empower developers to validate generative logic before a launch. Frame AI as an accelerator, not a replacement; strong human guardrails equal a controlled boost. Machines are here to augment human work, not replace it! - As with all human-written code, it is paramount to perform regular security audits of AI-generated code and to understand what was generated, as it is critical to ensure it meets relevant compliance standards to mitigate risks across production environments. -
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