How to strengthen AI security with MLSecOps

AI-powered systems have become prime targets for sophisticated cyberattacks, exposing critical vulnerabilities across industries. As organizations increasingly embed AI and machine learning (ML) into their operations, the stakes for securing these systems have never been higher. From data poisoning to adversarial attacks that can confuse AI decision-making, the challenge spans the entire AI/ML lifecycle.

In response to these threats, a new discipline, Machine Learning Security Operations (MLSecOps), has emerged to provide the foundation for robust AI protection. Let’s explore the five basic categories within MLSecOps.

1. AI software supply chain vulnerabilities

AI systems rely on a vast ecosystem of commercial and open-source tools, data, and ML components, often available from multiple vendors and developers. If not properly secured, every element within the AI ​​software supply chain, be it datasets, pre-trained models or development tools, can be exploited by malicious actors.

The SolarWinds hack, which compromised multiple government and corporate networks, is a well-known example. Attackers infiltrated the software supply chain, embedding malicious code into widely used IT management software. Similarly, in the AI/ML context, an attacker can inject malicious data or tampered components into the supply chain, potentially compromising the entire model or system.

To mitigate these risks, MLSecOps AI emphasizes thorough testing and continuous monitoring of the supply chain. This approach includes verifying the origin and integrity of ML assets, especially third-party components, and implementing security controls at each stage of the AI ​​lifecycle to ensure no vulnerabilities are introduced into the environment.

2. Model provenance

In the world of AI/ML, models are often shared and reused across teams and organizations, making model provenance — how an ML model was created, the data it used, and how it evolved — a main concern. Understanding model provenance helps track model changes, identify potential security risks, monitor access, and ensure the model is working as expected.

Open source models on platforms like Embrace Face or Model Garden are widely used due to their accessibility and collaborative benefits. However, open-source models also introduce risks, as they may contain vulnerabilities that can be exploited by bad actors once they are introduced to the user’s ML environment.

MLSecOps best practices call for maintaining a detailed history of each model’s origin and lineage, including an AI-Bill of Materials, or AI-BOM, to guard against these risks.

By implementing tools and practices to track model provenance, organizations can better understand the integrity and effectiveness of their models and protect against malicious manipulation or unauthorized changes, including but not limited to insider threats.

3. Governance, Risk, and Compliance (GRC)

Strong GRC systems are essential to ensure responsible and ethical AI development and use. The GRC framework provides oversight and accountability, guiding the development of fair, transparent, and accountable AI-powered technologies.

AI-BOM is a key pattern for GRC. It is essentially a comprehensive inventory of the components of an AI system, including details of the ML pipeline, model and data dependencies, licensing risks, training data and its sources, and known or unknown vulnerabilities. This level of insight is crucial because one cannot confirm what one does not know.

An AI-BOM provides the visibility needed to protect AI systems from supply chain vulnerabilities, model exploits, and more. This MLSecOps-supported approach provides several key benefits, such as increased visibility, proactive risk mitigation, regulatory compliance and improved security operations.

In addition to maintaining transparency through AI-BOM, MLSecOps best practices should include regular audits to assess the validity and bias of models used in high-risk decision-making processes. This proactive approach helps organizations comply with evolving regulatory requirements and build public confidence in their AI technology.

4. Trusted AI

The increasing impact of AI on the decision-making process makes reliability an important consideration in the development of machine learning systems. In terms of MLSecOps, trusted AI represents an important category focused on ensuring the integrity, security, and ethical considerations of AI/ML throughout its lifecycle.

Trusted AI emphasizes the importance of transparency and interpretability in AI/ML, aiming to create systems that are understandable to users and stakeholders. By prioritizing fairness and seeking to minimize bias, Trusted AI complements broader practices within the MLSecOps framework.

The Trusted AI concept underpins the MLSecOps framework by supporting continuous monitoring of AI systems. Validity, accuracy, and maintaining vigilance against security threats require ongoing evaluation, ensuring models remain resilient. Together, these priorities foster a trusted, equitable, and secure AI environment.

5. Adversarial Machine Learning

Within the MLSecOps framework, Adversarial Machine Learning (AdvML) is an important category for those developing ML models. It focuses on identifying and mitigating the risks associated with adversary attacks.

These attacks manipulate input data to trick models, potentially leading to incorrect predictions or unexpected behavior that can compromise the performance of AI applications. For example, subtle changes in an image fed to a facial recognition system can cause the model to misidentify the person.

By incorporating AdvML techniques during the development process, manufacturers can enhance their security measures to protect against these vulnerabilities, ensuring their models remain resilient and accurate in a variety of scenarios.

AdvML emphasizes the need for continuous monitoring and evaluation of AI systems throughout their lifecycle. Developers should implement regular evaluations, including adversarial training and stress testing, to identify potential vulnerabilities in their models before they are deployed.

By prioritizing AdvML practices, ML practitioners can proactively protect their technology and reduce the risk of operational failure.

Conclusion

AdvML, among other categories, demonstrates the critical role of MLSecOps in addressing AI security challenges. Together, these five categories highlight the importance of leveraging MLSecOps as a comprehensive framework to protect AI/ML systems against emerging and existing threats. By embedding security at every stage of the AI/ML lifecycle, organizations can ensure that their models are high-performing, secure and resilient.