Insights from experts shaping secure development

Si bien los problemas de codificación pueden ser parte del problema, los errores de lógica empresarial suelen ser el resultado de defectos de diseño o suposiciones lógicas incorrectas cuando se crea una aplicación por primera vez.

En ciberseguridad, a menudo somos como cazadores. Nuestros ojos están fijos en el horizonte, buscando la próxima vulnerabilidad emergente. Sin embargo, este enfoque orientado hacia el futuro puede tener el sorprendente efecto de reducir nuestra conciencia general sobre la seguridad.

Es común que los sitios web y las aplicaciones permitan a los usuarios enviar comentarios y otros datos a través de una aplicación mediante el correo electrónico. Y la mayoría de las personas ni siquiera piensan en ello en términos de un posible riesgo de seguridad.

Kamer van Koophandel shares how it embedded secure coding into everyday development through role-based certifications, Trust Score benchmarking, and a culture of shared security ownership.

See how Paysafe's partnership with Secure Code Warrior led to a 45% boost in developer productivity and a major reduction in code vulnerabilities.

DigitalOcean's use of Secure Code Warrior training has significantly reduced security debt, allowing teams to focus more on innovation and productivity. The improved security has strengthened their product quality and competitive edge. Looking ahead, the SCW Trust Score will help them further enhance security practices and continue driving innovation.

Organizations are at an inflection point driven by the explosive adoption of AI, which promises significant changes in how businesses operate. That leaves CISOs on unsteady ground. As the gatekeepers of their organization’s data and access, they must ensure the security of the enterprise. However, the prospects of a headlong charge into wide-ranging, and possibly unchecked, use of AI could create a flood of security issues that many CISOs, under their current organizational structures, aren’t equipped to handle.

Security leaders and developers alike are already acutely aware that AI coding assistants and agentic agents can introduce vulnerabilities into the code they generate. A recent study unveiled another critical concern to keep them up at night — LLMs used for making iterative code improvements may introduce new vulnerabilities over time, even when explicitly asked to make code more secure.

Government bodies are trying to develop rules and regulations for safe AI use, but enterprises can't afford to wait. They need to address the risks now.

See how 16 leading AI models actually code, scored across 11 real-world frameworks and 1,760 codebases — the framework matters as much as the model.

AI risk doesn't stop at engineering. Get the one-pager on Citizen AI — build AI literacy and safe habits across your whole workforce.

From AI autocomplete to autonomous agents—explore how software development is evolving and what it means for security, governance, and your team.