
AI-Generated Code Security Risks: What Your Tools Aren’t Telling You About Vulnerable Code
By Izcóatl Estañol13 min readCybersecurity
Technical guidance on secure coding practices and application security for engineering leaders shipping software under real deadlines and real threat models.
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By Izcóatl Estañol13 min read

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AI Deepfakes: A Threat to Facial Biometric AuthenticationApplication security stops being an afterthought once you treat it as an engineering discipline rather than a checklist run before release. This hub collects practical writing on secure coding practices, covering the decisions that shape whether a codebase resists attack or simply passes a scan. We look at how vulnerabilities get introduced during normal development work, not just during obvious mistakes, and what teams can change in their daily process to catch problems earlier. Topics range from language-specific hardening, such as hardening Node.js applications, to broader architectural questions like building security into design. We also cover the human and organizational side of the problem, including how offensive testing informs defense and where access controls quietly erode over time.
Application security only works when it is built into how software gets shipped, not layered on afterward. Teams we work with through our application security services typically start by reviewing existing code and pipelines against current threat models, then rebuilding the parts that no longer hold up. If governance and policy questions matter more than implementation right now, our Security Strategy and Compliance hub covers that ground. For teams weighing a broader security review across infrastructure and process, our cybersecurity services page outlines how that engagement typically starts.