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3 ways AI will integrate with DevSecOps

by:QY Precision      2019-09-08
In recent times, with the advancement of the SDLC development process, organizations are moving towards artificial intelligence and ML.
In addition to the advantages of artificial intelligence, a major threat is security.
I believe to train the process based on historical behavior and then take better action when the same situation arises.
There should be some practices that involve more security practices and artificial intelligence technologies. This is .
This is a study that seeks to integrate the security principles in DevOps Technology.
In this DevOps team and the cybersecurity team, the agenda is to provide faster code, but at the same time provide maximum security.
It makes up for the gap between the safety teams, which will make the product more reliable and the delivery pipeline stronger.
It follows micro service
A single functional module based on infrastructure, not a complex module.
The process will be continuously monitored and infrastructure upgraded so that the organization can continue its new development.
A hybrid cloud environment should be integrated into the infrastructure.
In addition, an ongoing feedback loop will help developers get to know their bugs earlier and have control over all security bugs.
With the help of Real Madrid
Time feedback systems, organizations can never put their systems at risk.
Benefits of using DevSecOps on DevOps: Let\'s take a look at what DevSecOps will provide benefits that don\'t exist in DevOps.
AI and ML can help DevSecOps by increasing the speed of false positive recognition.
This will eventually reduce the time spent identifying threat vectors.
Let\'s take a look at how DevSecOps, with the help of three ways, have appeared this year so that IT organizations can object to starting using DevSecOps with the same enthusiasm as before implementing AI.
Due to the very strict time guidelines, early developers did not have enough time for security checks.
They have very little time to complete the development so that the code can be put into production.
They don\'t bother with the safety test of the product because it\'s an add-onon to the time.
This means that developers can develop at a faster pace so that they can meet the guidelines.
With the rapid development of artificial intelligence technology, safety testing can be done very easily.
With AI and security testing, you can reduce vector recognition time and the delivery pipeline in DevOps will become faster.
This will help developers learn more about the risk of being vulnerable and they will have a huge impact on the delivery pipeline.
You have real-time access to security risks, in which AI will advance DevOps security testing. Cyber-
Security incidents are rapidly increasing, and it becomes very important that we limit these violations, otherwise it can cause huge losses.
If you think that artificial intelligence can completely replace human beings, then this is a completely wrong statement.
Ai also depends on humans, because you need to input some data in the first process of the training process.
This data has to be integrated through manual monitoring, and if you use the wrong data, it is clear that the machine will be trained in the wrong way.
Therefore, it is very important to ensure that the correct data is entered into the machine in order to train it in a perfect way.
There should be more cyber security experts who know what data is correct and what data is wrong.
They should know how to train artificial intelligence models.
These should be the experience of being proficient in safety testing.
They should know if the data is damaged.
They should know whether the conclusion is correct.
Machine learning and artificial intelligence are efficient technologies, but the first step is manual input. if humans train models in the right way, then even if these technologies do not get the right output.
AI can be integrated into DevSecOps as it will help developers continuously improve performance without compromising product delivery.
With the help of artificial intelligence, developers can develop faster, and at the same time, they can learn from mistakes.
They can learn from their experience and avoid these mistakes in the long run.
Before pushing the code to production, AI will give developers enough time to complete coding and check the code.
You can find a security vulnerability with security testing.
So it certainly helps to eliminate all errors and make the safe delivery pipeline very powerful.
You can successfully implement AI in DevSecOps by integrating security from the beginning to the end of the pipeline.
By integrating AI and ML in DevSecOps, you can even automate security.
You should always try to implement vulnerability checks like OWASP dependency checks and discovery tests.
You can improve the consistency of your deployment.
You can follow some direct compliance to increase the traceability of the pipeline.
So try adjusting AI in DevSecOps to make your process more secure.
Your product will be delivered at a faster pace and the customer will be very pleased with this new progress from DevSecOps. All the best! !
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