2 posts tagged with "security"

DevOps security combines three words: development, operations, and security and its very goal is to remove any barriers that may exist between software development and IT operations._

A survey found that over 58% of businesses had a data breach the previous year, with 41% resulting from software flaws. Infractions may cost businesses millions of dollars and potentially damage their reputation in the industry._

Yet, there has been tremendous progress in the application development processes. Businesses in the modern day often use DevOps practices and technologies while developing new applications and systems. The DevOps method emphasizes incremental deployment rather than a single massive deployment. Daily releases are possible in certain instances. It is not simple, however, to identify security flaws in the daily updates. Thus, security is an extremely important part of the DevOps workflow. Each application development team—development, testing, operations, and production—must take security precautions to prevent breaches. This article discusses DevOps Security's recommended practices for developing and deploying apps safely._

DevOps Security Challenges and Considerations#

The DevOps philosophy has revolutionized how businesses create, run, and maintain their applications and IT infrastructure, whether on or in the cloud. DevOps merges IT development with IT operations, combining demands and specifications, coding, testing, high availability, implementation, and more.

DevOps often collaborates with agile software development procedures, which encourages cross-team alignment, cooperation, and individualized development. DevOps software development is characterized by a constant pursuit of velocity, automation, and monitoring across the whole process, from code integration and testing through release and deployment, as well as infrastructure management. These methods shorten the time it takes to create a product and get it to market while ensuring its features and capabilities evolve in response to market demand and company goals.

Best practices of security in DevOps#

When it comes to safety, what impact does DevOps have? Let's explore how DevOps methods and popular tools create unique security concerns.

1. Implementation of the DevSecOps Model#

Another famous name in the field of DevOps is "DevSecOps." Divorce is the core security technique that all IT companies have been using. The term really refers to the combination of three distinct but interrelated disciplines: development, security, and operations.

DevSecOps is an approach to leveraging security technologies in the DevOps life cycle. Hence, from the outset of application development, security has to be a part of it. By incorporating security into the DevOps process, businesses can create apps that are both reliable and safe from exploits. This strategy is also useful for breaking down barriers between different departments, such as IT and security.

A few fundamental practices are required for a DevSecOps methodology:

• Embed security technologies into your development workflow.
• Experts in cyber security must review all automated testing.
• Developing threat models requires cooperation between development and security teams.
• The product backlog should provide top priority to security needs.
• Before deploying any new infrastructure, all existing security policies should be examined.

2. Review the code in a smaller size#

You need to read the code in a smaller size to understand it. Reviewing too much code at once is a bad idea, as is reviewing the whole program in one sitting. Examine the piece of the code by piece to ensure thorough examination.

3. Establish a system for dealing with future changes#

Set up a method for handling upcoming changes. After an application has reached the deployment phase, it is no longer desirable to have new features added or old ones taken away by developers. The only thing that can assist you is to start using the change management strategy.

Thus, the change management strategy should be used for application modifications. The developer should be able to make adjustments after the project has been authorized.

4. Maintain active application monitoring#

Security is often overlooked when an application is deployed to a production environment.

The application process should be in a constant state of evaluation. To ensure no new vulnerabilities have been added, you should routinely analyze its code and conduct security tests.

5. Train the development team on security#

Security best practices should also be taught to the development team.

For example, if a new developer doesn't know about SQL injection, you must educate them on what it is, how it works, and how it might damage the program. Don't get technical. Therefore, you must inform the development team of new security regulations and best practices at a wide level.

6. Secure Coding Standards#

Developers focus on the features of an app rather than its security since it is not a top concern for them. Yet, with the growth of cyber risks in the modern day, you must ensure that your development team understands the best security measures before building the application.

For this reason, developers need to be familiar with security technologies that may detect flaws in their code during development and suggest solutions.

7. Use DevOps Security Automation Tools#

If you want to save time and effort in the DevOps processes, you should use security automation tools.

Use automation tools to test an application and create repeatable tests. It will be simple to create safe products with the help of automated tools for code analysis, remote management, configuration management, vulnerability management, etc.

8. Segregate the DevOps Network#

Segmenting the network is a good idea for the company.

A company's resources, including software, hardware, data storage, and more, should not depend on a single network. Hackers who breach your network will have complete access to your company's resources. Hence, having a distinct network for each logical element would be best.

For instance, keeping your development and production networks completely separate is recommended.

Conclusion#

DevOps security may assist in detecting and fixing code vulnerabilities and operational shortcomings before they cause problems. DevOps security guarantees that application and system development is secure from the start. This increases availability lowers data breaches and assures the development and distribution of sophisticated technology to fulfill corporate objectives.

A company that cares about its customers' data security should adhere to these DevOps security best practices. Combining security best practices with the DevOps approach may save a company millions. Start using the security best practices described here for safer and quicker app releases.

Can an AI-based edge computing platform drive businesses or is that a myth? We explore this topic here._

Introduction#

For a long time, artificial intelligence has been a hot issue. We've all heard successful tales of forward-thinking corporations creating one brilliant technique or another to use Artificial Intelligence technology or organizations that promise to put AI-first or be truly "AI-driven." For a few years now, Artificial Intelligence (AI) has been impacting sectors all around the world. Businesses that surpass their rivals are certainly employing AI to assist in guiding their marketing decisions, even if it isn't always visible to the human eye (Davenport et al., 2019). Machine learning methods enable AI to be characterized as machines or processes with human-like intelligence. One of the most appealing features of AI is that it may be used in any sector. By evaluating and exploiting excellent data, AI can solve problems and boost business efficiency regardless of the size of a company (Eitel-Porter, 2020). Companies are no longer demanding to be at the forefront or even second in their sectors; instead, businesses are approaching this transition as if it were a natural progression.

Artificial Intelligence's (AI-driven) Business Benefits#

Businesses had to depend on analytics researchers in the past to evaluate their data and spot patterns. It was practically difficult for them to notice each pattern or useful bit of data due to the huge volume of data accessible and the brief period in their shift. Data may now be evaluated and processed in real-time thanks to artificial intelligence. As a result, businesses can speed up the optimization process when it comes to business decisions, resulting in better results in less time. These effects can range from little improvements in internal corporate procedures to major improvements in traffic efficiency in large cities (Abduljabbar et al., 2019). The list of AI's additional advantages is nearly endless. Let's have a look at how businesses can benefit:

• A More Positive Customer Experience: Among the most significant advantages of AI is the improved customer experience it provides. Artificial intelligence helps businesses to improve their current products by analyzing customer behavior systematically and continuously. AI can also help engage customers by providing more appropriate advertisements and product suggestions (Palaiogeorgou et al., 2021).

• Boost Your Company's Efficiency: The capacity to automate corporate procedures is another advantage of artificial intelligence. Instead of wasting labor hours by having a person execute repeated activities, you may utilize an AI-based solution to complete those duties instantly. Furthermore, by utilizing machine learning technologies, the program can instantly suggest enhancements for both on-premise and cloud-based business processes (Daugherty, 2018). This leads to time and financial savings due to increased productivity and, in many cases, more accurate work.

• Boost Data Security: The fraud and threat security capabilities that AI can provide to businesses are a major bonus. AI displays usage patterns that can help to recognize cyber security risks, both externally and internally. An AI-based security solution could analyze when specific employees log into a cloud solution, which device they used, and from where they accessed cloud data regularly.

Speaking with AI Pioneers and Newcomers#

Surprisingly, by reaching out on a larger scale, researchers were able to identify a variety of firms at various stages of AI maturity. Researchers split everyone into three groups: AI leaders, AI followers, and AI beginners (Brock and von Wangenheim, 2019). The AI leaders have completely adopted AI and data analysis tools in their company, whilst the AI beginners are just getting started. The road to becoming AI-powered is paved with obstacles that might impede any development. In sum, 99% of the survey respondents have encountered difficulties with AI implementation. And it appears that the more we work at it, the more difficult it becomes. 75% or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more effort than the other two groups and began 4-5 years earlier, had over 60% of their projects encounter difficulties. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. The staff was resistant to embracing new methods of working or were afraid of losing their employment. Considering this, it should be unsurprising that the most important tactics for overcoming obstacles include culture and traditions (Campbell et al., 2019). Overall, it's evident that the transition to AI-driven operations is a cultural one!

The Long-Term Strategic Incentive to Invest#

Most firms that begin on an organizational improvement foresee moving from one stable condition to a new stable one after a period of controlled turbulence (ideally). When developers look at how these AI-adopting companies envision the future, however, this does not appear to be the case. Developers should concentrate their efforts on the AI leaders to better grasp what it will be like to be entirely AI-driven since these are the individuals who've already progressed the most and may have a better understanding of where they're headed. It's reasonable to anticipate AI leaders to continue to outpace rival firms in the future (Daugherty, 2018). Maybe it's because they have a different perspective on the current, solid reality that is forming. The vision that AI leaders envisage is not one of consistency and "doneness" in terms of process. Consider a forthcoming business wherein new programs are always being developed, with the ability to increase efficiency, modify job processing tasks, impact judgment, and offer novel issue resolution. It appears that the steady state developers are looking for will be one of constant evolution. An organization in which AI implementation will never be finished. And it is for this reason that we must start preparing for AI Edge Computing Platform to pave the way for the future.

References#

• Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S.A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), p.189. Available at: link.
• Brock, J.K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), pp.110–134.
• Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y. (Jody), & Mavrommatis, A. (2019). From Data to Action: How Marketers Can Leverage AI. Business Horizons.
• Daugherty, P.R. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), pp.24–42. Available at: link.
• Eitel-Porter, R. (2020). Beyond the Promise: Implementing Ethical AI. AI and Ethics.
• Palaiogeorgou, P., Gizelis, C.A., Misargopoulos, A., Nikolopoulos-Gkamatsis, F., Kefalogiannis, M., & Christonasis, A.M. (2021). AI: Opportunities and Challenges - The Optimal Exploitation of (Telecom) Corporate Data. Responsible AI and Analytics for an Ethical and Inclusive Digitized Society, pp.47–59.