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In recent years, organizations have moved quickly to integrate new AI technology into their business processes, from basic machine learning models to generative AI tools like ChatGPT. Despite offering numerous business advantages, however, the integration of AI expands organizations' attack surfaces.

Threat actors are constantly looking for ways to infiltrate target IT environments, and AI-powered tools can become another entry point to exploit. AI security strategies are essential to safeguard company data from unauthorized access.

MLSecOps is a framework that brings together operational machine learning (ML) with security concerns, aiming to mitigate the risks that AI/ML models can bring to an organization. MLSecOps focuses on securing the data used to develop and train ML models, mitigating adversarial attacks against those models, and ensuring that developed models comply with regulatory compliance frameworks.

ML models can help organizations increase efficiency by automating repetitive tasks, improving customer service, reducing operational costs and maintaining competitive advantages.

But ML adoption also introduces risks at different points, including during the development and deployment phases, especially when using open source large language models (LLMs). The following are among the most significant risks:

The term MLOps refers to the process of operationalizing ML models in production. It involves several phases:

MLSecOps, therefore, is a natural extension of MLOps. Similar to how DevOps evolved into DevSecOps by integrating security practices into the software development lifecycle, MLSecOps ensures that ML models are developed, tested, deployed and monitored using security best practices.

MLSecOps integrates security practices throughout the ML model development process. This integration ensures the security of ML models in two areas:

MLSecOps specifically focuses on the security issues related to ML systems. The following are the five main security pillars that MLSecOps addresses.

Like other software tools, ML systems frequently use components and services from various third-party providers, creating a complex supply chain. A security vulnerability in any component across the ML system supply chain could allow threat actors to infiltrate it and conduct various malicious actions.

Typical supply chain elements for an ML system include the following:

The U.S. was the pioneer in addressing the security aspects related to the software supply chain. In 2021, the Biden administration issued Executive Order 14028, which requires all organizations in both public and private sectors to address security vulnerabilities in their supply chain.

Model provenance is concerned with tracking an ML system's history through development, deployment, training, testing, and monitoring and usage. Model provenance helps security auditors identify who made specific changes to the model, what those changes were and when they occurred.

Some elements included in the model provenance of an ML system include the following:

Model provenance is essential to comply with the various data protection compliance regulations, such as the GDPR in the European Union, HIPAA in the United States and industry-specific regulations such as the Payment Card Industry Data Security Standard.

Governance, risk and compliance (GRC) frameworks are used within organizations to meet government and industry-enforced regulations. For ML systems, GRC spans several elements of MLSecOps, with the primary aim of ensuring that organizations are using AI tools responsibly and ethically. As more organizations build AI-powered tools that rely on ML models to perform business functions, there is a growing need for robust GRC frameworks in the use and development of ML systems.

For instance, when developing an ML system, organizations should maintain a list of all components used in development, including data sets, algorithms and frameworks. This list is now known as the machine learning bill of materials (MLBoM). Similar to a software bill of materials for software development projects, MLBoMs document all components and services used to create AI tools and their underlying ML models.

Trusted AI deals with the ethical aspects of using AI tools for different use cases as more organizations rely on AI tools to perform job functions, including critical ones. There is an emerging need to ensure that AI tools and their underlying ML models are giving ethical responses and are not biased in any way towards characteristics like race, gender, age, religion, ethics or nationality.

One method to check the fairness of AI tools is to request that they explain their answers. For instance, if a user asks a generative AI tool to recommend the best country to visit in summer, the model should provide a justification for its answer. This explanation helps humans understand what factors influenced the AI tool's decision.

Adversarial machine learning is concerned with studying how threat actors can exploit ML systems in various ways to conduct malicious actions. There are four primary types of adversarial ML:

ML development teams can use the MLSecOps methodology efficiently to mitigate cyberattacks when developing ML models. The following are some MLSecOps best practices:

Nihad A. Hassan is an independent cybersecurity consultant, an expert in digital forensics and cyber open source intelligence, and a blogger and book author. Hassan has been actively researching various areas of information security for more than 15 years and has developed numerous cybersecurity education courses and technical guides.

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Secure your machine learning models with these MLSecOps tips - TechTarget