ZDNET Urges Security Checks Before Code, Citing 6 Early Design Risks
Updated
Updated · ZDNet · May 11
ZDNET Urges Security Checks Before Code, Citing 6 Early Design Risks
2 articles · Updated · ZDNet · May 11
Six design-stage risks — including trust boundaries, identity, authorization, data exposure, logging and failure modes — should be addressed before coding to stop vulnerabilities from reaching production.
Threat modeling and secure-by-design practices catch risky assumptions while architectures are still flexible, shifting teams from late bug-fixing to preventing flaws at the source.
Developer workflows should then enforce that approach with IDE security alerts, pull-request checks, secrets detection, CI/CD tests and deployment guardrails that block risky changes before release.
Dependency hygiene is a parallel priority because third-party libraries, containers, APIs and AI-generated code can introduce supply-chain weaknesses; teams should lock versions, review transitive packages and monitor ownership or vulnerability signals.
Federal guidance from CISA and NIST SP 800-218 reinforces the shift, arguing that early security controls are cheaper than production incidents, hotfixes, liability and customer fallout.
With AI tools introducing 10x more security flaws, are we losing the battle for secure code?
Can new EU laws fix a supply chain where 95% of flaws hide in unvetted third-party code?
As AI finds thousands of exploits for just $20,000, who is responsible for fixing them all?
AI-Driven Coding’s Security Crisis: 45% of Generated Code Is Vulnerable—Risks, Real-World Impact, and Solutions
Overview
The report highlights ZDNET's urgent warning in May 2026 about the growing security crisis in AI-driven coding. As artificial intelligence is rapidly adopted, security struggles to keep up, leading to major flaws that remain unsolved. The fast pace of software production means security is often treated as an afterthought, creating a 'treadmill' effect where new code brings more problems than can be fixed. AI systems face attacks on many fronts, including issues with autonomous agents, poisoned training data, and prompt injection attacks. This creates a risky environment for users and businesses, demanding a shift to proactive security measures.