DevSecOps – The Key Tech Trend For 2021

The last year has been a testing period for businesses. The pandemic accelerated the pace of digital transformation across industries. Organizations moved to the cloud and used digital tools to do work. However, that led to a growing concern about securing the data. After all, everybody was working remotely, and there were higher chances of data breaches occurring. Research showed a 273% increase in data breaches in the first half of 2020 compared to 2019.This compelled organizations to adopt DevSecOps.

DevSecOps is a framework that integrates security into every stage of development. It has made security a natural part of the development process. The need for DevSecOps has increased to such an extent that 74% of IT leaders confirmed an increase in accelerating security initiatives to secure software development.

Although the vaccination drive has begun, organizations are planning to work remotely in 2021. They are planning to prioritize DevSecOps to establish security best practices.

No wonder major tech leaders and publications call it the key tech trend of 2021. The DevSecOps market is growing so rapidly, that it is expected to grow at a CAGR of 32.05% by 2028.

How Can DevSecOps Enhance Security in 2021?

DevSecOps addresses security concerns early on

Unlike the earlier software development methods where security testing occurred at a later stage, DevSecOps ensures that the vulnerabilities and security gaps are identified at an early stage itself. This way, the security gaps are identified and fixed early on before they go unnoticed. The good part about DevSecOps is that the onus of securing the application is not restricted to cybersecurity experts alone. It breaks down the silos. Even developers are trained to detect vulnerabilities and fix them at the development stage itself. Sometimes security threats could seep in while leveraging open-source libraries for development. Hence, developers need to conduct due diligence and do thorough security checks before using it to code. Integrating the DevSecOps solution with the development process allows developers to determine if the open-source library they plan to use is vulnerable to threats.

DevSecOps improves compliance

Given the history of data breaches and frauds, cybersecurity has become a priority for organizations worldwide. Non-adherence to regulations impacts the organization’sreputation and they end up attracting heavy fines and lawsuits. Software development methodologies like DevOps help organizations achieve faster time to market. What took years to develop now barely takes months.

Continuous development and innovation aregiven more attention. Due to this, developers and testers often overlook security lapses and vulnerabilities, and that small gap leads to potential security threats in the future. DevSecOps aims to solve this problem. Organizations can redesign their operational and compliance framework and make continuous security a best practice. This enables developers to develop applications that are secure by design. The mandate of adhering to compliance also coerces organizations to monitor for security lapses frequently rather than making it a one-time exercise.

DevSecOps makes security an enabler

Developers often see security tests as a roadblock to innovation. With DevSecOps, developers can strikea balance between security and innovation. They can build applications that are both secure and innovative. They can include security checks and tests into the process to ensure that there is no delay in development due to security assessments.

In fact, by integrating security into the development process and by monitoring it continuously, organizations can prevent breaches, save millions, and increase their ROI. It also improves the development and security teams’ efficiency as both can work collaboratively to build safe and innovative applications. This approach will also make developers see DevSecOps as an enabler and not as an obstacle.

DevSecOps accelerates response to security incidents

Typically, organizations took a reactive approach while solving security incidents. Sometimes the response was so slow that an IBM report states that it takes a combined 280 days to identify and contain the breach. Here’s what Wendi Whitmore, director of X-Force Threat Intelligence at IBM, had to say about time and cost relation in detecting incidents.

To save costs and time, organizations must implement DevSecOps. The main objective of DevSecOps is to create a ‘Security as a code’ culture within the organization. This means that the developers will have to do regular security checks throughout the software development process. They have to create playbooks and workflows in advance to ensure a consistent and measurable response to incidents. These workflows and playbooks can also be integrated with the DevOps process to take pre-emptive security measures and mitigate risks before it’s too late.

DevSecOps automates security

Testing for vulnerabilities is a time-consuming and resource-intensive task. Even if an organization hires a large team to do testing, human errors can seep into the process. DevSecOps helps organizations to automate security testing. Security functions like identity management and access control can be automated so developers don’t need to do manual testing. They can focus on more important strategic functions. The processes run smoothly.
Considering that automation reduces the chances of security loopholes, it becomes easier for the team to identify a problem and fix it, in case an incident occurs. DevSecOps can also automate the reporting process, so organizations can have a detailed overview of the incidents and look for ways to prevent future issues.


2021 will change the way organizations look at security processes. DevSecOps compels organizations to change how security tests were done and implement it as part of their development process – right from planning to the testing stage. It requires a complete shift in culture as the teams will not be able to function in silos and have to re-engineer their existing processes to make security an integral part of the development process. However, by establishing best practices and by selecting the right security automation tools, organizations can improve security.


What is automated machine learning (AutoML)?

3d rendering robot learning or machine learning with education hud interface

Automated machine learning (AutoML) signifies a fundamental shift in how organizations of all sizes strategy machine learning and information science. Implementing conventional machine learning approaches to real-world business issues is time consuming, resource-intensive, and hard. It requires specialists from the many areas, including information scientists — a number of those most sought after professionals at the job market today .

Automated machine learning varies which, which makes it simpler to construct and utilize machine learning versions from the actual world by conducting systematic procedures on raw information and picking models that extract the most applicable information from the information — what’s often known as the sign in the sound.” Automated machine learning integrates machine learning best practices from top-ranked data scientists to produce information science more accessible across the business.

Here’s the conventional machine learning procedure at a high level:

When creating a version with the standard procedure, as you can see from Figure 1, the sole automated task is version coaching . Automated machine learning applications automatically implements all of the actions outlined in red — guide, tedious modeling jobs that used to demand expert data scientists. That conventional procedure often takes months or weeks. With automatic machine learning nevertheless, it requires days for business specialists and information scientists to develop and compare dozens of versions, locate insights and forecasts , and resolve more business issues quicker.

Automating these measures allows for increased agility in the democratization of information science to include individuals without extensive programming knowledge.

Manually building a machine learning model is a multistep process that needs domain knowledge, mathematical experience, and computer engineering abilities — that is a whole lot to ask of one firm, let alone a single information scientist (supplied you can employ and keep 1 ). Not just that, there are an infinite number of chances for human error and prejudice, which degrades model precision and devalues the insights you could receive from the model. Automated machine learning empowers organizations to utilize the baked-in understanding of information scientists without wasting money and time to develop the capacities themselves, concurrently enhancing return on investment in data science initiatives and lessening the quantity of time that it takes to catch value.

Automated equipment learning makes it possible for companies in each business — healthcare,
By automating the majority of the modeling jobs required so as to develop and deploy machine learning units, automatic machine learning empowers business users to execute machine learning options easily, thereby allowing a company’s data scientists to concentrate on more complicated issues.