"This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations exposed, and developers will struggle to backtrack in tracing and identifying where - and how - a security gap occurred."
"This isn't the stuff of hypothetical musings either. The problem is already here: One in five organizations have suffered from a serious security incident directly tied to AI-generated code. Nearly two-thirds of coding solutions produced by large language models (LLMs) turn out to be either incorrect or vulnerable - and roughly one-half of the correct solutions are insecure - meaning LLMs cannot yet create deployment-ready code."
Rapid AI expansion is increasing technical debt across enterprises, with forecasts predicting 75 percent of companies' tech debt will reach moderate or high severity in 2026. Software development teams widely use AI coding assistants to boost output, but frequently omit adequate safety controls and secure practices in deployments. This creates hard-to-trace security gaps, lengthening detection and remediation times. One in five organizations already experienced a serious incident tied to AI-generated code. Nearly two-thirds of LLM-produced coding solutions are incorrect or vulnerable, and about half of correct solutions are insecure. AI continues to struggle with authentication, access control, and configuration risk factors, intensifying rework needs and exposure.
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