Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As organizations continue to evolve their DevSecOps programs by adopting comprehensive testing and monitoring, the next step is to take action on the insights uncovered. This means remediating security issues as early as possible and responding to security alerts and incidents in a timely manner. However, many security and development teams find that triaging the findings of every tool and managing remediation efforts is time-consuming and costly.

Managing the risks of AI development tools is crucial for organizations looking to responsibly and effectively leverage this technology’s potential. AI offers transformative capabilities, particularly in coding assistance, where tools can speed up development and reduce manual workloads. However, these benefits can come with risks, such as security vulnerabilities and compliance challenges, that cannot be overlooked.

Snyk is exploring using the open-source Golang project Bento to read data from Kafka streams and materialize intelligence to various outputs. We are pleased to share that we are proactively helping secure the Bento project by contributing dependency fix updates.

Snyk is committed to our partnership with ServiceNow, and together, we're revolutionizing how organizations manage Application vulnerabilities and risk. Snyk's market-leading developer security platform and ServiceNow's robust Security Operations (SecOps) capabilities offer a powerful solution for Application Security teams and Enterprise CISOs.

Security is often seen as a roadblock in development, slowing releases and adding friction between teams. However, as software development cycles become faster and more complex, security must evolve from a blocker to an innovation driver. DevSecOps ensures security is a core part of the development workflow, and automation plays a crucial role in making this integration smooth and effective.

AI code generation is exactly what it sounds like — using artificial intelligence to write and improve code. Tools powered by large language models (LLMs) and specialized AI systems can help developers generate boilerplate code, fix bugs, and even refactor entire sections of an application. And developers are leaning in. According to a GitHub survey, 92% of developers have already used AI coding tools at work or on personal projects.