Protecting data and other corporate assets is an increasingly difficult challenge that touches nearly every corner of an organization. As the complexity of digital systems increases, so do the challenges.
One way to help govern through chaos is by integrating development, security, and operations through a DevSecOps methodology that integrates security across the IT lifecycle.
However, as artificial intelligence (AI) advances and machine learning (ML) moves to the center of organizations, new challenges arise: how to incorporate ML security into broader development cycles. AI models are different from other software. They make predictions, recommendations, and handle automation, often without direct interfaces or specific code.
That's why it's important to develop protections and guardrails to keep AI and ML models safe. This is where Machine Learning Security Operations (MLSecOps) comes in. Extend DevSecOps principles across the entire AI and machine learning lifecycle. The MLSecOps framework focuses on protecting code, algorithms, and data sets that involve intellectual property (IP) and other sensitive data.
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First, it's important to understand how DevSecOps and MLSecOps are similar and different. Both have a common path. That is, it begins with the creation or initiation of an idea throughout the development process. Both provide security training to developers and data scientists and help them understand threat modeling. Therefore, many of the same techniques that work within DevSecOps also work in the MLSecOps world.
This includes the concept of shift left, which aims to introduce critical checks, balances, and protections early in the development process to reduce security risks later on. This approach saves costs, reduces technical debt, and most importantly, avoids real-world breaches and failures. However, many similarities end here. Traditional software applications are very different from machine learning model-driven apps.
Traditional software development tasks have been to develop interfaces with buttons, drop-down boxes, and other features that guide customers through a linear process, such as purchasing. As a result, appearance and functionality are paramount. Within an ML-driven app, the focus is on the model, the data on which the model is trained, and the insights the model provides. Typical use cases include probability and It focuses on identifying patterns.
Unlike traditional software development environments using integrated development environments (IDEs), data scientists typically use Jupyter notebooks to write code. This happens outside of the IDE and often outside of the traditional DevSecOps lifecycle. As a result, data scientists who are not trained in secure development techniques can put sensitive data at risk by storing unprotected secrets and other sensitive information in notebooks. Simply put, the same tools and protections used in the DevSecOps world are not effective for ML workloads.
Environmental complexity is also important. Traditional development cycles typically lead directly to software application interfaces or APIs. The field of machine learning focuses on iterative efforts to build trainable models that lead to better results. ML environments generate a large number of serialized files that are necessary for dynamic environments. Result is? Organizations can become overwhelmed by the complexity inherent in version control and integration.
What do all this mean in a practical sense? DevSecOps is complex, but MLSecOps is even more complex. MLSecOps involves a rapidly evolving, highly dynamic environment that incorporates numerous metrics and constantly changing conditions and requirements. To maximize the value of MLSecOps, business and IT leaders must rethink and rewire processes to shift left.
MLSecOps is the path to a more secure enterprise
Navigating the MLOps space is not easy, but it is extremely important. A high degree of collaboration between engineers, data scientists, and security experts is critical to mitigating cybersecurity risks. Educating data scientists on best practices is central to building stronger, more dynamic security models.
However, there are several other steps that can make or break MLSecOps. These include: Building a solid best practice foundation using a shift-left approach. Gain complete visibility into your ML pipeline, including datasets, models, and platforms. Regularly audit your environment and perform vulnerability assessments of all tools and platforms in your environment. Assess the security risks of third-party models before they are integrated into your AI environment.
The ultimate goal is transparency and traceability across the machine learning supply chain. It includes a Machine Learning Bill of Materials (MLBOM) that provides a comprehensive view into the environment and all its components. With MLSecOps and MLBOM in place, enterprises can determine whether their models contain vulnerabilities or pose other types of security or compliance risks.
Deploying the MLSecOps framework can help strengthen security and maximize the value of AI and machine learning.
