Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The impact of AI tools on software development is starting to make itself felt. As the productivity of developers increases, so does the necessity for software testing. Luckily, AI is also seeing increasing adoption in testing, enabling dev teams to ensure robust and secure software despite increasing output. In this in-depth guide, we will explore the top 18 AI testing tools in 2023 that have the potential to take your testing processes to the next level. Overview.

In our first year participating in the Forrester Wave™: Static Application Security Testing (SAST) Q3 2023, we’re thrilled that Snyk has been recognized as a Strong Performer in a mature, yet evolving, enterprise software security category. Snyk is disrupting the SAST market with a developer-first approach to application security, illustrated by our position in strategy and market presence in the evaluation.

Developing and maintaining secure code at scale is hard. Having the right Static Application Security Testing (SAST) solution makes it easier, but how are practitioners to choose? In the following interview, you’ll learn about three emerging trends from detailed analysis of the SAST landscape in The Forrester Wave™: Static Application Security Testing, Q3 2023.

Today’s software applications power almost every aspect of our lives, and ensuring the security of these applications is paramount. Threat actors can cause devastating consequences for companies, leading to financial losses, reputational damage, and legal repercussions. Companies building commercial or in-house applications must adopt robust security measures throughout their software development lifecycle to avoid releasing vulnerable code.

Finding deeply hidden and unexpected vulnerabilities early in the development process is key. However, time to invest in proactive tests is limited. Prioritizing speed over security is common. Our new AI-assistant CI Spark closes this gap and enables both speed and security. CI Spark makes use of LLMs to automatically identify attack surfaces and to suggest test code. Tests generated by CI Spark work like a unit test that automatically generates thousands of test cases.