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Articles by Tag: Code Generation

AI Coding Gains Are Real. The Hidden Cost Is Moving Downstream
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AI Coding Gains Are Real. The Hidden Cost Is Moving Downstream

AI coding tools can accelerate development, but the hidden cost often moves downstream into review, validation, release, and remediation. CIOs should scale selectively, fund the control layer, and measure whether the whole delivery system improves. Not just whether developers generate code faster.
When Speed Meets Risk: Protecting API Keys in AI-Driven Development
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When Speed Meets Risk: Protecting API Keys in AI-Driven Development

AI has sped up software delivery, but it is also exposing API keys and other sensitive information. If this trend continues, businesses are basically doing half the job for bad actors and making it easier for exploitation to occur. CISOs and IT leaders must pair AI coding velocity with disciplined governance to keep their sensitive information secure.
More Precise Vibe Coding with Spec-driven Development
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More Precise Vibe Coding with Spec-driven Development

Vibe coding has accelerated software development through rapid prototyping. However, the generated code may not match what is required sometimes. Spec-driven development can solve this problem by constraining AI’s creative wiggle room. CIOs and IT leaders can harness spec-driven development to ensure that AI-generated code is more consistent, accurate, and auditable.
Less Vibes, Better Development: Making Vibe Coding Enterprise-Ready
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Less Vibes, Better Development: Making Vibe Coding Enterprise-Ready

Vibe coding accelerates development by enabling rapid prototyping and leveraging AI tools. However, this approach often leads to technical debt, including hardcoded secrets, inadequate input validation, and limited testing. It’s crucial for CIOs and IT leaders to balance speed with security to mitigate risks and ensure sustainable software practices.
Unlocking AI’s Coding Edge Without the Compliance Headaches
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Unlocking AI’s Coding Edge Without the Compliance Headaches

AI coding assistants boost developer productivity and code quality, but they can also introduce legal landmines, such as inadvertently incorporating open-source code with incompatible licenses. CIOs and IT leaders must proactively govern AI-generated code to mitigate IP risks and ensure responsible adoption throughout the software development lifecycle.