The Qwiet Blog

Key Findings The SAIL (Secure AI Lifecycle) Framework outlines AI-specific risks across the lifecycle, but lacks implementation details. Qwiet AI fills this gap with direct, technical controls focused on code and configuration-level vulnerabilities. Qwiet AI aligns with SAIL Phases 2 and 3 by identifying hidden AI assets, detecting hardcoded secrets, auditing third-party AI components, and […]

Key Takeaways AI Native means built, not bolted. It’s the difference between a platform that embeds AI into its architecture and one that adds it later as a feature. Only the former can deliver meaningful context, automation, and integration across the SDLC. Developers need signal, not noise. AI-native tools like Qwiet prioritize relevance, context, and […]

Key Takeaways All-in-one platforms trade depth for surface-level coverage: Bundling SAST, DAST, IAST, RAST, and ASPM into a single tool often leads to overlap in low-risk areas (e.g., basic code vulnerabilities) and blind spots in high-risk ones (e.g., complex business logic vulnerabilities). Context-aware tools, which understand an application’s specific context, outperform general-purpose scanners: These tools […]

Key Takeaways Static tools miss logic-driven vulnerabilities. Traditional SAST tools flag obvious syntax-level risks but fail to understand business rules, multi-tenant boundaries, or the actual intent behind code behavior. Qwiet’s comprehensive analysis traces full execution paths across helpers, middleware, and services. Modeling code as a connected graph uncovers hidden risks buried in trusted-looking utilities, such […]