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Implementing Data-Driven DevSecOps

DevSecOps is currently implemented in a method that has absolutely nothing to do with the quick and flexible DevOps CI/CD pipeline. It’s akin to fighting a modern forest fire with tactics from the 19th century.

To put out a fire, firemen back then used a method known as a “bucket brigade,” in which they lined up and passed buckets from one hand to another. It takes much labor, without a doubt, but many are ineffective. Moreover, it takes far too long and is a time wastage to eliminate the kinds of wildfires we are currently dealing with.

Similarly, current DevSecOps initiatives’ mostly manual approaches are ineffective in putting out the flames of cyberattacks and digital threats that modern mobile apps confront. These threats and attacks, which are looking for new vectors to launch fresh attacks elsewhere, are growing and changing every time like a contemporary mega-fire.

The security teams launch the manual “bucket brigade” to add as much protection as they can against drawbacks found by traditional DevSecOps techniques. However, neither the CI/CD process nor the threats have stopped.

The threat landscape has changed, and new DevOps automation tools have been implemented that have led to new vulnerabilities. Because developers need real-time information about attacks and threats to create defenses against the most recent dangers, pen testing is ineffective.

The result? Vulnerable mobile apps. But fret not. Because that’s where Data-Driven DevSecOps enters.

The need for a data-driven solution:

In business, the C-suite is working hard to make their firms data-driven organizations where decisions are made based on analysis of hard data rather than on intuition or expert opinion.

The implementation of security for mobile applications must use the same strategy. Additionally, a mobile app’s security implementation needs to be automated. Security cannot fall behind the CI/CD process given how much of it has already been automated.

Data-driven DevSecOps is created by combining these two components. In this approach, the development and security teams have a system of record that offers real-time information on cyberattacks and cyber threats affecting apps in the field.

This information reassures the team’s decisions about the safeguards that are urgent & that must be included in the upcoming build. The mobile app developers got the ability to acquire data in close to real-time on the specific dangers and vulnerabilities that their apps are up against in the real world is now very much a reality.

Developers can acquire detailed insights into the most frequent challenges their apps might encounter, where threats are more common in particular geographical areas when combined with location, network, and other data types.

Additionally, they can foresee threats before they materialize into vital issues. With this information in hand, development teams with automation tools for DevOps can decide which safeguards to prioritize in the upcoming release to make the most use of their time and resources and offer the highest level of safety to their end users.

Why automation is the solution?

However, data alone is insufficient. The threat landscape for mobile apps is changing frequently, and development teams need to be able to act swiftly on the insights presented. Manual implementation methods are too sluggish and inefficient to keep up with this.

To ensure that security implementation goes as easily as feature creation, a solution must exist to automatically incorporate mobile app security, anti-fraud, and anti-cheat safeguards from within the CI/CD pipeline.

Data-driven DevSecOps has many benefits. Publishers, developers, and security teams can first clearly comprehend what cybercriminals are doing to their apps & actual users. With this data and the wealth of additional information that businesses can gather from their apps, they can identify which threats are most urgent, which are rapidly emerging, and even how they are distributed across various geographical areas.

This information is incredibly helpful when deciding which safeguards to include in the upcoming release. Finally, after the app is deployed, teams can utilize the information they gather about threats and attacks with automation tools for DevOps to demonstrate the efficiency and worth of each incorporated defense. Information like this is crucial for ongoing improvement and convincing management and the C-suite of the importance of data-driven DevSecOps.

Data-driven DevSecOps - The Benefits:

Companies aiming to include scalable, rapid development, security, and operational principles into their DevSecOps approach shouldn’t overlook automation, or they risk missing out on one of the practice’s most significant advantages.

Low-level remedial chores can be automated and eliminated throughout the software development lifecycle, as with most technological automation approaches. It includes integrating security measures with DevOps companies UK into apps and keeping an eye on them & keeping an eye on apps from a cybersecurity standpoint.

When efficiency is essential to software development, the accuracy of the code frequently suffers. DevSecOps frameworks should incorporate automated code verification checks. These tests can reveal flaws and suggest corrective actions that won’t interfere with plans for software updates and deployment.

Processes that automatically and uniformly integrate security functionalities into all software builds should be part of a comprehensive DevSecOps architecture. The security is built consistently using this highly organized methodology each time an application goes through the continuous integration/continuous delivery lifecycle process.


Using mature DevSecOps automation through DevOps companies UK, vulnerabilities are fixed without the direct involvement of IT security personnel by giving developers access to self-service security tools. DevSecOps self-service tools let developers manage security without the need for human bottlenecks and promote skill development across teams.

Modern DevSecOps frameworks use AI and machine learning approaches to accelerate, streamline, and simplify challenging DevSecOps activities. Here are two illustrations:

  • By gathering and examining OS and software log data, it is possible to determine which software features malicious actors are seeking to exploit. AI can advise code additions, revisions, or architectural changes based on this data to proactively discover code vulnerabilities.
  • Testing-wise, code additions or changes can be gone through well-calibrated machine-learning technologies to determine how a specific change might influence other program elements.

It makes little sense to manually replicate DevSecOps tools and processes once they have been developed and refined, especially when more computational resources are needed or when complete frameworks must be replicated and installed in different physical locations.

Thanks to DevOps automation tools and data-driven DevSecOps, scaling these systems and processes up or down at a moment’s notice can be automated and initiated with just a few clicks. DevSecOps has been shown to result in 85% fewer security incidents, according to a recent Comcast case study.

Leverage Data-driven DevSecOps to battle security risks:

With data-driven DevSecOps, organizations can make security integration far more effective and efficient, with real-time statistics to show it. They can also provide data, transparency, and visibility surrounding security to all stakeholders in the CI/CD process.

The DevOps specialists at TechMobius can collaborate with your company to evaluate the procedures you use, find knowledge gaps, and create specialized solutions. Let your company leverage the benefits of data-driven DevSecOps with TechMobius!

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