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KI-gestütztes Coding: Warum Kontrolle besser ist als Vertrauen

In verschiedenen Branchen ist der Einsatz von KI-gestützten Coding-Assistenten unter Entwicklern weit verbreitet. Allerdings geht dies oft mit einem nahezu blinden Vertrauen in die Sicherheit der KI-generierten Code-Vorschläge einher, obwohl eindeutige Belege für deren regelmäßige Sicherheitslücken vorliegen. Folglich hinken die Sicherheitsmethoden den Anforderungen zur Absicherung dieser Technologie hinterher. Tech-Teams müssen daher die Risiken von KI-gestützten Coding-Tools durch verstärkte Sicherheitsautomatisierung minimieren. Über die unsicheren KI-Outputs hinaus müssen präventive Maßnahmen auch die unbelegte Annahme in Frage stellen, dass KI-generierter Code grundsätzlich dem von Entwicklern geschriebenen überlegen ist. Laden Sie unseren Leitfaden herunter, um mehr über fortschrittliche Sicherheitspraktiken für KI-generierten Code zu erfahren und Ihren Entwicklungsprozess zu schützen.

A CISO’s Guide To Safely Unleashing the Power of GenAI

As developers embrace generative AI for enhanced productivity, the evolution of security tools is crucial to match this technological advancement. Gartner forecasts that by 2026, 80% of code will integrate genAI APIs or apps, underscoring the need for adaptive security measures. Despite the benefits of AI-driven development, significant challenges arise from the reliance on vast, risky datasets and the often misplaced trust in AI-generated code's security. Current security tools struggle to keep up with the rapid code generation capabilities of genAI. This playbook explores the benefits and drawbacks of genAI, provides guidelines for establishing a secure genAI program, and identifies essential features of effective genAI security tools to ensure safe AI adoption and innovation. Download now to secure your AI-driven development.

Buyer’s Guide for Developer Security Tools

The movement to shift security earlier into the software development lifecycle (shift left) and make it a continuous process (DevSecOps) has created challenges and opportunities for companies. The purpose of shifting left is to build security into the fast, iterative development processes of modern apps, and reduce the security backlog early, in the same way other code issues are reduced. Success in this endeavor means fewer security issues get shipped with the production code and discovered later, when it’s more expensive and burdensome to fix them. Shifting left requires tools that support these capabilities, enabling developers to find and fix issues as they work and empowering them to be self-sufficient. But there are challenges that must be overcome for shift left to work. Traditionally, security tasks are handled by a team of experts who work separately from developers. However, the rise of DevSecOps and the decentralization of code security makes silos unsustainable — security needs to scale alongside development. To shift security left and make it continuous, developers must become quasi-security practitioners themselves. Download to find out more.

AI Code, Security, and Trust: Organizations Must Change Their Approach

AI coding assistants have achieved widespread adoption among developers across all sectors. However, many developers place far too much trust in the security of code suggestions from generative AI, despite clear evidence that these systems consistently make insecure suggestions. Unfortunately, security behaviors are not keeping up with AI code adoption. Technology organizations need to protect themselves against AI code completion risks by automating more security processes and inserting the right guardrails to protect not only against bad AI code but also against the unproven perception that AI-generated code is always superior to novel human code. Download to find out more.