Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How good is Symbolic AI as an automated expert system for analyzing code paths and detecting security vulnerabilities? Snyk Code is tested and demonstrates the benefits of rule-based, algorithmic systems.

AI has made good on its promise to deliver value across industries: 77% of senior business leaders surveyed in late 2024 reported gaining a competitive advantage from AI technologies. While AI tools allow developers to build and ship software more efficiently than ever, they also entail risk, as AI-generated code can contain vulnerabilities just like developer-written code. To enable speed and security, DevSecOps teams can adopt tools to integrate security tasks into developer workflows.

Cloud security teams are often faced with an onslaught of noise from their detection tooling, making it nearly impossible to distinguish truly malicious threats from benign behaviors. Many threats will go uninvestigated simply because there aren’t enough analysts for the sheer amount of alerts, leaving organizations exposed to potential breaches.

The transition of AI from experimental to production is not without its challenges. Developers face the challenge of balancing rapid innovation with the need to protect users and meet strict regulatory requirements. To address this, we are introducing Guardrails in AI Gateway, designed to help you deploy AI safely and confidently.

Please see part Part I for our introduction to leveraging AI in the enterprise and Part II for why the legacy landscape falls short in the age of GenAI.

With the announcement of Anthropic’s Claude 3.7 Sonnet model, we, as developers and cybersecurity practitioners, find ourselves wondering – is the new model any better at generating secure code? We commission the model to generate a classic CRUD application with the following prompt: The model generates several files of code in one artifact, which the user can manually copy and organize according to the file tree suggested by Claude alongside the main artifact.

API adoption has become a critical driver of digital transformation, fueling cloud migration, seamless integrations, and the monetization of data and functionality. This rapid expansion, however, has inadvertently created increasingly complex ecosystems that often outpace the security measures designed to protect them.