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These vulnerabilities include privilege escalations, back door credentials, possible injection exposure and unencrypted data.
Such tools can assist the defense capabilities of developers, enabling an easier pathway to a security-first mindset.
But like any new and potentially impactful innovation they also raise potential issues that teams and organizations should explore.
1) Will AI remediation entirely remove security-related tasks from developers?
In addition, teams cannot blindly trust the output of AI coding and remediation assistants.
Hallucinations, or incorrect answers, are quite common, and typically delivered with a high degree of confidence.
Ultimately, we will always need the people perspective to anticipate and protect code from todays sophisticated attack techniques.
2) How should training evolve to maximize the benefits of AI remediation?
3) How can DevSecOps providers add value to teams that use AI remediation?
It all comes down to innovation.
LLMs are trained on publicly available data, and they are only as good as the dataset.
This is, again, a key area in which security-aware developers fill a void.
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