This article was written by Michael Erquitt, Senior Security Engineer at Security Journey.
The internet has become more complex with the rapid rise of AI and machine learning. This has significantly impacted how people interact with it in various fields.
In response, professionals across industries and academia have all adapted their work and perceptions to fit this new paradigm. Let’s take some time to dive into the teams creating the technology shaping the world.
It Takes a Team to Advance Technology
These technological advances are driven by great teams comprised of domain experts, like:
- Data Engineers - designs and builds systems for data storage, processing, and retrieval
- Machine Learning Engineers - develop artificial intelligence (AI) systems
- Product Architects – responsible for designing products and how they work
- Application Engineers - create, design, test, and improve software
These experts work cross-functionally alongside Software Developers, Cloud Architects, and Security Architects, all with the goal of creating technological advancements and robust AI systems. Everyone involved in the development of these tools plays a role in not only impacting the current users but also shaping the future.
Step 1 - Data Collection and Analysis for AI
The first step in creating an AI system involves data science and engineering. Data Engineers and Scientists are tasked to gather, transform, and manage raw data into a format that is accessible and usable for the future AI system. The skills to get data to the point of being involves heavily using statistics from regression, classification, and Bayesian Networks.
Collecting and organizing data is a crucial first step, during which the requirements are defined, and sources are identified or created. In order for generative AI, like ChatGPT, to develop comprehensive and accurate responses for users, it is essential to gather and sort all of the data that will be used.
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After obtaining the data, it is necessary to have expertise in relational and non-relational databases to manipulate the data into a suitable form for the next phase of modeling. This is typically done using languages such as Python and Jupyter notebooks, but depending on the specific problem being addressed, the team may need to develop new validation methods and tools. The skills required in this field are broad and encompass knowledge of both cloud computing and extract, transform, and load (ETL) systems.
Moving beyond the technical hurdles that data engineers face, they must also comply with data governance and security policies of not only their own enterprise but also under the industry or federal framework.
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Upon completing the technical and compliance work, the Data Scientists will embark on the crucial task of analyzing the data sets. This analysis aims to identify patterns and correlations that can be translated into actionable insights. This process of analysis and presentation is critical, as it can significantly impact the project's success. Therefore, the team will take great care to ensure that all insights are accurate, relevant, and actionable.
If the data engineering responsibilities seem overwhelming, it's because they require compliance, technical aptitude, and careful mitigation of mistakes that can grow exponentially more serious as the model is trained.
Step 2 - Model Engineering for AI
Once the data has been obtained and verified, the Machine Learning Engineer plays a crucial role in model engineering. They are responsible for implementing the chosen machine learning algorithms and conducting AI experiments and tests. They design analytical methodologies to conduct statistical analysis, which is then used to refine the test results obtained previously.
To top off all that work, it is crucial to establish a machine-learning infrastructure to ensure the developed learning models are secured and maintained. This includes implementing effective security measures to prevent unauthorized access or data breaches and regularly monitoring and updating the models to ensure they remain accurate and up-to-date. Proper maintenance of the machine-learning infrastructure is crucial for the project's success and the learning models' long-term usefulness.
Step 3 - Product Design and Engineering for AI
After the work of Data Scientists and Machine Learning Engineers, the final responsibility falls upon the expertise of the Product Architect and Application Engineer. The Application Engineer is responsible for designing, developing, and maintaining AI-based products and applications for specific industries and users. Their technical domain is vast, as their responsibility includes embedding and fundamental Web Application Security, which is already broad, even in simple applications.
Engineers must have a threat model that outlines how plugins can impact the infrastructure and system behaviors. Although these tools have innovative features, they possess immense power that can lead to real-world negative consequences if used incorrectly, irresponsibly, or beyond the limits of the underlying technology. Therefore, it is of utmost importance to use these tools correctly, responsibly, and within the limits of their capability to avoid any undesirable outcomes.
AI Dreams Are About the Teams
The development of AI technology is a complex process requiring team effort. The contributions of each team member, including data engineers, machine learning engineers, software developers, cloud architects, security architects, data scientists, and product architects, are crucial to creating innovative and robust AI systems.
It is the collaboration of these experts that drives technological advancements and shapes the future of the world. As AI continues to evolve and become more integrated into various industries, it is important to recognize and appreciate the hard work and expertise of the teams behind it. And give these teams the means to secure these technologies to keep the public safe.
Security Journey customers have access to training content that supports their entire SDLC. With expertly crafted role-based learning paths, learners can receive targeted training that is relevant to their roles. And with topic-based learning paths, such as AI and LLM, your team can pinpoint important topics in your organization, quickly and easily.