Azure App Service is a platform-as-a-service (PaaS) commonly used to deploy applications and APIs, as well as functions, mobile apps, and more. It provides flexibility and reliability when deploying new applications and infrastructure, but it also introduces new security risks to your system. In particular, reduced visibility into the infrastructure and deployment of your application leads to a greater chance of application vulnerabilities being exploited by an attacker.
Artificial Intelligence is revolutionizing everything and cybersecurity is no exception. A growing number of industry experts and professionals are beginning to reflect on the benefits and risks of this innovative technology within the cybersecurity framework. AI can be used to enhance protection for businesses against threats but, on the flip side, it can also be applied to refine cybercriminal attacks.
In the fast-paced environment of cloud-native apps, security and seamless connections are a priority. Many DevOps and SecOps professionals use Kubernetes native features to handle their container security, keeping a tight grip on access and secrets to improve security posture. The integration between AWS AssumeRole and JFrog Access in Amazon Elastic Kubernetes Services (EKS), enhances enterprise security by automating secrets management.
Proprietary severity scoring often burdens AppSec teams. With every new vendor, you must evaluate their custom severity framework and work to translate assessed risk between tools. To eliminate this burden and provide our customers with a clear security assessment for configurations across the SDLC, Snyk will be moving towards standardizing our code to cloud security rules set on the Common Configuration Scoring System (CCSS)!
The rapid rise of artificial intelligence, more specifically, generative AI systems such as OpenAI’s ChatGPT, has simultaneously spurred intense development and concern over the past year. On the 30th of October, President Joe Biden signed an Executive Order that urges new federal standards for AI development, safety, security, and trustworthiness that also address many other facets of AI risk.