Quality assurance, or QA, is one of the go-to solutions for organizations looking to enhance their application security (AppSec). But alone, they don’t provide enough coverage and can give your team a false sense of security that comes back to haunt you during audits, or worse: after a breach. QA tools are only the tip of the iceberg when it comes to flagging and remediating flaws that leave your applications vulnerable to attacks.

It can sometimes feel like development and security teams are working toward two separate goals. Both developers and security professionals are supposed to be working toward timely, secure releases, but in reality, developers tend to prioritize speed and function, and security professionals prioritize security measures. How can you unify the teams and focus them on shared goals? A little history can help.

We know firsthand how critical it is for developers and security professionals to have a great working relationship. That extends beyond simply communicating well; for your DevSecOps program to come together so that you can secure your applications, you need to break down silos and improve security knowledge across the board.

Containers are on the rise. As reported by GlobalNewswire, Allied Market Research estimated that the application market would grow from its 2016 value of $698 million to $8.20 by 2025. With a compound annual growth rate of 31.8% between 2018 and 2025, this increase would largely reflect both the surge in popularity in application container technology along with a growing number of organizations’ migration to the cloud.

At Outpost24, we’re committed to providing information security testing solutions to not only fit seamlessly into the SDLC, but also to enhance our customers’ experiences in implementing and maintaining their best practices to ensure their SDLC leads to a strong security posture and comprehensive resiliency around their full stack.

As more and more businesses were forced to move to the cloud with the COVID-19 crisis, content and data have proliferated across devices, users, apps, and locations as a result of the new, mass work-from-anywhere reality. This brought a growing set of challenges to prevent data silos and content sprawl while remaining compliant with data regulations and governance.

Data stream clustering refers to the clustering of data that arrives continually such as financial transactions, multimedia data, or telephonic records. It is usually studied as a “Streaming Algorithm.” The purpose of Data Stream Clustering is to contract a good clustering stream using a small amount of time and memory.

Defensics is a generational fuzzer, which means it creates test cases based on a detailed model of the input data. The result: test cases that are very realistic but messed up in some way. This technique is highly effective in burrowing into different control paths in the target and revealing vulnerabilities. Subjectively speaking, the test cases have high quality. The disadvantage of generational fuzzing is that somebody has to create the data model for the inputs you are fuzzing.

The number of cyberattacks launched on organizations continues to rise every year. More attacks means more security alerts that security analysts have to triage each day. Many security teams have turned to a security orchestration, automation and response (SOAR) tool to help them automate the ever-increasing volume of security alerts, and respond to threats faster and more comprehensively.