Once they are accustomed to using these tools, you will start to see the real benefits of AI adoption. Wondering what DevSecOps skills do you need to survive and thrive in the generative AI era? This article will answer this question for you.
In this article, Anti-Dos will highlight five DevSecOps skills you need to thrive in the generative AI era.
Table of Contents
5 DevSecOps Skills You Need To Thrive In Generative AI Era
1. Prompt Engineering and Response Validation
2. Data Engineering
3. Shift Left Security
4. Master The Entire AI Stack
5. Test Automation
5 DevSecOps Skills You Need To Thrive In Generative AI Era
Here are five DevSecOps skills you need to succeed in the generative AI era.
1. Prompt Engineering and Response Validation
The input you give to generative AI tools influences the output. The more generic a prompt, the more generic the output. On the flipside, if you write a prompt which provides all the details and is highly specific, you are more likely to get a better output from these generative AI models. Even then, it is important to validate the output given by these models as they are subject to errors, biases and hallucinations.
Just like other technologies, generative AI can do a great job at some tasks but can fail miserably at others. It is important to understand which tasks generative AI can ace and which ones it struggles with. Spend time learning how to prompt these generative models to get the desired outputs and learn how to critically analyze the output to identify eros and inconsistencies in the output.
Avoid blindly trusting these AI models and using the output as it is otherwise, you might end up in hot waters. Trust the models but double check their output for accuracy/ Thanks to generative AI, developers can test new ideas and validate them or reject them quickly and try new things that they have never done before.
2. Data Engineering
Generative AI models require large volumes of structured and clean data for training. To ensure continuous supply of this training data, businesses must develop data pipelines to feed the training data into these large language models. That is where data engineering comes into play.
Data engineers and data scientists will help businesses put their unstructured data to good use. They will be responsible for embedding this unstructured data into the large language model through the pipeline. This enables businesses to automate their technology infrastructure and integrate different applications. Data pipeline orchestration skills are also crucial for cleaning data and preparing it for training the AI models.
3. Shift Left Security
Security should be an integral part of your software development lifecycle. You should perform security checks during every stage instead of considering security as an afterthought. This is what the shift left security approach is all about. Invest in advanced malware protection to secure your business from malware attacks.
By adopting a shift-left approach to cybersecurity, businesses can identify software earlier in the software development lifecycle, fix these issues and deliver a more refined final product. With manual testing and siloed security becoming irrelevant, skills surrounding these domains are also losing value. On the other hand, the ability to integrate AI into software is becoming more and more popular with each passing day.
According to Stephen Magill, Vice President of Product Innovation at Sonatype, “Devops teams should prioritize skills that bridge the gap between generative AI and devops, such as mastering AI-driven threat detection, ensuring the security of automated continuous integration and continuous delivery pipelines, and understanding AI-based bug remediation..”
He further adds, “Investing in areas that are the biggest pain points for teams, such as the lack of insight into how code was built or code sprawl from producing too much code, is also crucial, while less emphasis can be placed on manual and reactive tasks”
4. Master The Entire AI Stack
Technology platform providers are incorporating generative AI capabilities into various development tools, such as integrated development environments and IT service management platforms. Copilots, which generate code based on developers' prompts, present opportunities for efficiency but necessitate thorough evaluation for integration, performance, security, and legal considerations. Pryon Founder and CEO Igor Jablokov emphasizes the need for the devops stack and the cybersecurity industry to catch up in identifying generative code to prevent copyright issues and defects in enterprise applications.
Organizations with significant intellectual property are exploring the development of privatized Large Language Models tailored for specific applications, such as searching financial information, analyzing healthcare patient data, or creating educational tools. Developers and data scientists interested in contributing to large language model development must familiarize themselves with emerging technologies like vector databases and open-source stacks such as Hugging Face, Llama, and LangChain.
While large language models with 100 billion parameters are popular, there is a growing acknowledgment that fine-tuning and composing smaller models may alter the landscape, requiring effective management of the models' life cycle.
In the realm of generative AI, the modern devops engineer faces the challenge of learning vector databases, open-source stacks, and navigating the complexities of managing the life cycle of models.
Nikolaos Vasiloglou, Vice President of research machine learning at RelationalAI, suggests that the game may change with the use of smaller models and emphasizes the importance of mastering these technologies. Finally, the transition from developing proofs of concept to delivering production-ready generative AI capabilities is highlighted as crucial.
Kjell Carlsson, head of data science strategy and evangelism at Domino, underscores the significance of operationalizing generative artificial intelligence models and their pipelines, identifying this as a key skill for driving impactful outcomes in the field of artificial intelligence.
5. Test Automation
Many IT organizations are facing challenges in meeting the growing demands for security and quality assurance test automation due to insufficient staffing, skills, and tools. To address this, developers, operations engineers, and data scientists are encouraged to invest in acquiring security and test automation skills. Marko Anastasov, cofounder of Semaphore continuous integration and continuous delivery, emphasizes the potential of artificial intelligence in shifting security, quality assurance, and observability left in the development life cycle, allowing for early issue detection, higher-quality code delivery, and rapid developer feedback.
As artificial intelligence and automation take on a more significant role, traditional skills like manual testing and siloed security may diminish in importance. It is crucial for IT to implement continuous testing and security practices when incorporating generative artificial intelligence capabilities into workflows, utilizing artificial intelligence-generated code, or experimenting with developing large language models.
Which generative AI skill is the most important in your opinion? Share it with us in the comments section below.
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