Top Threats for AI/ML Development and How to Eliminate Them

According to a recent McKinsey & Company report, global adoption of artificial intelligence surged to 72% as of early 2024, with generative AI usage skyrocketing from 33% to 65% in just one year. This trend highlights the widespread development of both consumer and enterprise AI. While this expansion presents exciting opportunities for AI developers, it also introduces critical challenges, like security risks and compliance issues.

Failing to understand these threats can undermine AI projects' effectiveness, reliability, and scalability. In this article, we will identify the most pressing machine learning threats and explain how you can overcome them. You can ensure your projects achieve their full potential by addressing these challenges.

TL;DR

• Most machine learning models face threats such as model drift, security issues, lack of secure storage, compliance issues, and inefficient pipeline workflow.
• KitOps addresses some of these concerns by securing the models and their artifacts, enforcing OCI compliance for container images and auditing, and guaranteeing efficient workflows across AI pipelines.

Machine learning development threats

There are several threats to developing AI/ML solutions nowadays. This section identifies some of these issues and how they negatively affect the execution of machine learning projects.

Model drift
One of the biggest challenges machine learning systems face is model drift. Over time, ML models lose their effectiveness as real-world data evolves, making their predictions less accurate or even misleading.

This is especially a major issue for sectors where data is continuously evolving, like finance and banking. Imagine building a fraud detection machine learning model for a bank only to discover that it’s flagging legitimate transactions because the fraud patterns have changed. When data drift occurs, training data becomes obsolete, leading to incorrect and sometimes dangerous outputs for the users.

Machine learning security risks
Machine learning security is a major concern for those in the development space. ML models can be susceptible to security issues owing to the large amounts of data they are constantly fed. Machine learning algorithms need these large amounts of input data to be effective. However, this data can be used to manipulate the ML model if adequate security measures are not taken. Adversarial or poisoning attacks, where malicious inputs are designed to fool a model, can disrupt operations or lead to significant data breaches. Consider a self-driving car tricked into misreading traffic signs—the consequences could be disastrous.

A notable case that exemplifies this phenomenon is the 2016 case of Microsoft chatbot Tay AI. Tay, a Twitter-based AI chatbot, was designed to learn from user interactions. However, malicious users corrupted the bot with offensive language and extremist views, causing it to quickly adopt inappropriate and harmful behavior, leading to its shutdown within 24 hours. This example emphasizes how vulnerable AI systems can be when exposed to untrusted data. Other AI security risks include model theft and Distributed Denial of Service (DDOS) attacks, in which models are spammed with numerous prompts to make their services unavailable to legitimate users.

Lack of secure storage options
The growth of data used for AI projects makes it difficult to find platforms that can scale fast enough to meet its needs. Additionally, compliance requirements impose more limits on the storage solutions that can be used for ML projects since they may not meet the requirements.

Finally, many secure storage options may not easily integrate with existing workflows and tools used in AI/ML development, hindering the progress of ML projects. If companies opt for less secure storage options due to these barriers, it can compromise the integrity of models and lead to data breaches and other adversarial machine learning attacks. It is difficult for ML startups to grow rapidly without having a storage solution that scales to meet their demands. Having a storage solution that is secure, scalable, and version-controlled makes it easy to collaborate with other team members, deploy, and roll back to previous versions easily in case of any errors.

Compliance issues
Compliance with regulations and industry standards is essential to the progress and success of a machine learning project. This includes compliance with privacy laws, regulations, and industry standards. Open Container Initiative (OCI) compliance is one such industry standard.

OCI compliance makes sure container images align with best practices for security, such as proper layering and secure configuration. Furthermore, OCI compliance helps to ensure portability, security, and a smooth workflow across different platforms. With several teams collaborating on machine learning projects, maintaining OCI compliance across the board might become tedious, but it is absolutely necessary. Lack of compliance can lead to portability issues since the images may not be able to run across different environments.

Inefficient pipeline workflows
In a typical AI/ML pipeline, different team members—such as Data Scientists, ML Engineers, and DevOps Engineers—produce various outputs, including model artifacts from data scientists and container images from DevOps engineers. However, these artifacts are often passed between teams that may not be familiar with each other’s tools and processes. This lack of integration can lead to inefficiencies, errors, and misunderstandings, particularly if teams use different formats or standards for their artifacts.

Companies can improve workflow efficiency and speed up development by using a secure and shareable artifact management system that facilitates collaboration and improves workflow efficiency.

Now that you know the threats most AI projects face, let’s see how we can eliminate these using KitOps.

Eliminating these threats with KitOps

As developers work on AI projects, they will invariably encounter several of the above-mentioned challenges, which can slow or halt the development of their projects. One effective solution is KitOps, a tool designed to help you package and manage your AI/ML projects effectively. At the core of KitOps is the ModelKit, which allows you to package models and dependencies, such as configurations, code, datasets, and documentation, in one place. This makes tracking changes, controlling access, and monitoring project assets easier.

While KitOps features offer many benefits, you may be wondering how it can specifically help you eliminate the threats to your ML project’s development. Find out below.

Using ModelKits to tackle model drift

How do you ensure that your ML models stay effective over time? The obvious answer would be constant retraining of the models using more up-to-date and relevant data. However, doing this might be cumbersome without a reliable and easy way to package and version your project so that implementing and tracking changes, updates, and deployments becomes a breeze.

KitOps enables you to package your models and all associated dependencies into ModelKits. With ModelKits, models are bundled with everything needed for reproducibility, making it easier to deploy across different environments. It becomes easier to track changes in models, datasets, or notebooks, compare model versions, roll back to previous versions, and deploy new versions, all of which help to tackle the issue of model drift efficiently.

Exploring comprehensive security solutions with KitOps
KitOps allows organizations to track, control, and audit every change made to AI project artifacts, including models, configurations, and codebases. This level of control is essential for security-conscious organizations. The ability to audit your models through each phase of development and deployment helps identify vulnerabilities before they become real-world issues. KitOps integrates with your current DevOps tools, so you don't have to worry about overhauling your workflow. Rather, it will fit right into the pre-existing security architecture of your machine-learning system.

JozuHub's secure storage system
You can use ModelKits with JozuHub for the secure storage of all versions of your ModelKits and the files stored within them, which may include sensitive data. This eliminates the risks of hacking that could otherwise exist if you stored your model artifacts in an insecure remote repository.

Furthermore, with JozuHub, you can access features like versioning, collaboration, rollbacks, deployments, and other operational tasks. JozuHub also helps you get the most out of KitOps' ModelKits, which are designed to work well together.

Ensuring seamless compliance with KitOps
ModelKits are an OCI-compliant packaging format for all AI/ML artifacts. As such, teams can easily package their models, datasets, code, and configurations within ModelKits and share them in a standardized format that works with any OCI-compliant registry. This allows organizations to control access and meet data protection and privacy regulations.

Modelkits also makes sure all AI project components can be easily tracked, controlled, and audited across different environments while maintaining OCI compliance. KitOps allows organizations to improve collaboration, streamline deployment, and meet industry regulations without compromising on the security or integrity of their AI artifacts. Its audibility also simplifies compliance, as regulators can easily verify the history and integrity of AI systems.

Smoother workflow across the AI/ML pipeline
KitOps is designed to break down the barriers that typically exist between teams. It simplifies the handoffs between data scientists, application developers, and SREs working with LLMs or other models by certifying that the same ModelKit can be used by everyone involved. ModelKits can be passed along the pipeline, with each team only unpacking the tools they need to work.

Furthermore, KitOps integrates with the tools you’re already familiar with since it uses open standards, so there’s no steep learning curve for your teams. Whether leveraging Kubernetes for deployment or using TensorFlow for model training, KitOps works seamlessly across platforms.

Conclusion

AI/ML systems development presents unique challenges, but these challenges can be met head-on with the right tools. Whether it's ensuring compliance, managing security risks, or enhancing team collaboration, KitOps offers the solutions you need to eliminate the threats standing in your way. If you have questions about integrating KitOps with your team, join the conversation on Discord and start using KitOps today.