Insights from experts shaping secure development

Post-quantum cryptography (PQC) is critical for protecting data from quantum computing threats. Learn how “harvest now, decrypt later” exposes risk and how developers can prepare for quantum-safe security.

Explore Enabler 2: Senior Leadership Sponsorship. Learn why active buy-in from the CIO, CTO, and CISO is vital to drive developer adoption and program credibility.

While development teams look to make the most of GenAI’s undeniable benefits, we’d like to propose a four-point foundational framework that will allow security leaders to deploy AI coding tools and agents with a higher, more relevant standard of security best practices. It details exactly what enterprises can do to ensure safe, secure code development right now, and as agentic AI becomes an even bigger factor in the future.

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.

By assessing and understanding a development team’s security maturity, organizations can formulate a plan with the right stakeholders, process, and technology to build and support the necessary skills and capabilities.

Security maturity in development teams should be a continuous cycle of improvement with realistic goals along the way. As development teams increase their security maturity, they reduce the amount of rework and minimize risk, while also allowing automation to help create efficiency in the SDLC.