Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Organizations that develop mobile apps need to be aware of the potential cyber security threats. These threats can lead to the loss of users' private data, which can have serious repercussions for industries like fintech, healthcare, ecommerce, etc. In order to prevent these malicious practices, Dynamic Application Security Testing (DAST), a security testing tool, has been introduced. It helps to weed out specific vulnerabilities in web applications whenever they run in the production phase.

What are cloud-native applications? According to the Cloud Native Computing Foundation (CNCF), the term “cloud native” describes systems that are specifically designed to help build and run scalable applications in all cloud environments, including public, private, and hybrid clouds. Cloud-native applications use the attributes of cloud architecture in ways that legacy systems can’t. They don’t need any onsite computing infrastructure and can scale quickly to meet demand.

Elaine Feeney is a member of the Netskope Network Visionaries advisory group. For any business, speed matters a lot. Speed of service is key to happy customers because any delays that users experience directly impact the success of the organization. Security processing that causes delays for the customers or employees has negative business impacts. Now more than ever, security controls have become a board-level priority due to elevating risks.

More organizations than ever run on Infrastructure-as-Code cloud environments. While migration brings unparalleled scale and flexibility advantages, there are also unique security and ops issues many don’t foresee. So what are the major IaC ops and security vulnerabilities? Configuration drift. Cloud config drift isn’t a niche concern. Both global blue-chips and local SMEs have harnessed Coded Infrastructure.

At CrowdStrike, we combine cloud scale with machine learning expertise to improve the efficacy of our machine learning models. One method for achieving that involves scanning massive numbers of files that we may not even have in our sample collections before we release our machine learning models. This prerelease scan allows us to maximize the efficacy of our machine learning models while minimizing negative impact of new or updated model releases.