Insights from experts shaping secure development

Was sind die wichtigsten Beweggründe für Entwickler, wenn es darum geht, etwas über sichere Codierung zu lernen, und wie können sie genutzt werden, um ein erfolgreiches Anwendungssicherheitsprogramm zu entwerfen und zu implementieren?

Da die Zahl der Cyberbedrohungen weiter zunimmt, gehen Unternehmen täglich Kompromisse zwischen Sicherheit, Praktikabilität und Geschwindigkeit ein und setzen sich dabei Risiken aus.

Steigern Ihre Programmierer in einer Welt, in der sich Cyberbedrohungen weiter vermehren? Ist das menschliche Element der sicheren Codierung — der alles entscheidende Entwickler — bereit, seinen Beitrag zum Schutz unserer vernetzten Welt zu leisten? Um diese Frage zu beantworten, schauen wir uns einige Erkenntnisse aus einer kürzlich von Secure Code Warrior in Zusammenarbeit mit Evans Data Corp. durchgeführten Studie über die Einstellung von Entwicklern zu sicherer Codierung, sicheren Codepraktiken und Sicherheitsoperationen an.

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.

Developers have long struggled to truly claim a seat at the table in traditional threat modeling programs, but with the right skills, they have the opportunity to wield AI responsibly to seriously cut risk and rework in their codebase.

AI-generated code may be “made by machine”, but taking a cookie-cutter approach to securing that code would fall well short of mitigating the vulnerabilities LLMs can introduce. Organizations need to establish precise security reviews, with human developers anchoring the process to implement effective security controls while also managing the specific coding temperament of each LLM used. AI-generated code must undergo the same personalized risk assessments as code written by human developers.

s artificial intelligence (AI) tools become more widely used in the software development process, their impact on security is becoming clearer. According to recent research, nearly 70% of organisations have discovered vulnerabilities caused by AI tools while one in five have experienced a serious incident as a result of those vulnerabilities.

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.

Gartner named SCW twice in the 2026 Hype Cycle for Secure Software Engineering. Here's why it matters for AI-driven development.