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How to strengthen AI security using MLSecOps

AI-driven system have become prime targets of sophisticated cyberattacks exposing critical weaknesses across industries. The stakes have never been greater for organizations that are increasingly integrating AI and machine-learning (ML) in their operations. Data poisoning, adversarial attack that can lead to AI misinformation and other challenges are all part of the AI/ML Lifecycle.

As a response to these threats a new discipline has emerged, machine learning operations security (MLSecOps), to provide a strong foundation for AI security. Explore five categories that make up MLSecOps.

1. AI Software Vulnerabilities in the Supply Chain

AI systems are based on a large ecosystem of tools, data and ML components that can be sourced from a variety of vendors and developers. If not secured properly, every element in the AI supply chain – whether it is datasets or pre-trained models or development tool – can be exploited.

SolarWinds, a hacker who compromised government and corporate networks is one of the most well-known examples. Infiltrating the software supply chain and embedding malicious software into widely used IT Management software, attackers were able to compromise the entire system. In the AI/ML context an attacker can inject corrupted components or data into the supply chain. This could compromise the entire system or model.

To reduce these risks, MLSecOps stresses thorough vetting and constant monitoring of the AI Supply Chain. This approach involves verifying the source and integrity of ML components, particularly third-party ones, and implementing controls at each phase of the AI Lifecycle to ensure that no vulnerabilities are introduced.

2. Model Provenance

In the world of AI/ML, models are often shared and reused across different teams and organizations, making model provenance — how an ML model was developed, the data it used, and how it evolved — a key concern. Understanding the provenance of a model helps to track changes, identify security risks, monitor the access and ensure the model performs according to expectations.

The accessibility and benefits of open-source platforms such as Model Garden or Hugging Face are common. But open-source modeling poses risks as well, because they can contain vulnerabilities that malicious actors can exploit if they are introduced into the ML environment of a user.

MLSecOps recommends that you maintain a detailed history of every model, including a Bill of Materials or AI-BOM. This will help to mitigate these risks.

Implementing tools and practices to track model provenance can help organizations better understand the integrity and performance of their models and protect them from malicious manipulations or unauthorized changes. This includes but is not limited insider threats.

3. Governance, Risk Management and Compliance (GRC),

GRC is essential to ensuring ethical and responsible AI development and usage. GRC frameworks are used to provide oversight and accountability and guide the development of fair and transparent AI powered technologies.

The AI-BOM can be a valuable tool for GRC. This is a complete inventory of all the components in an AI system. It includes ML pipeline details and model and data dependencies. This level of understanding is important because you cannot secure something that you don’t know about.

An AI-BOM gives you the visibility to safeguard AI systems, including from supply chain vulnerabilities and model exploitation. This MLSecOps approach provides several advantages including increased visibility, proactive risk management, regulatory compliance, improved security operations, and more.

Best practices in MLSecOps should include audits of models to assess their fairness and bias, as well as maintaining transparency via AI-BOMs. This proactive approach allows organizations to comply with ever-changing regulatory requirements while building public trust in AI technology.

4. Trusted AI

Trustworthiness is a critical consideration for the development of machine-learning systems, given AI’s increasing influence on decision making processes. In the context MLSecOps, trusted AI represents a crucial category focused on ensuring AI/ML integrity, security, ethical considerations throughout its lifecycle.

The Trusted AI approach emphasizes transparency and explainability of AI/ML systems, with the goal of creating systems that users and stakeholders can understand. Trusted AI is a complement to MLSecOps because it prioritizes fairness and strives to minimize bias.

The concept of trusted AI supports the MLSecOps Framework by advocating continuous monitoring of AI Systems. To maintain fairness, accuracy and vigilance in the face of security threats, ongoing assessments are required. Together, these three priorities create a safe, secure and fair AI environment.

5. Adversarial Machine Learning

MLSecOps includes a category called adversarial Machine Learning (AdvML). This is a critical component for anyone building ML-models. It focuses on identifying, and mitigating, risks associated with adversarial threats.

These attacks manipulate input data in order to deceive the models. They can result in incorrect predictions or unexpected behaviors that could compromise the effectiveness AI applications. If a model is fed a subtle change to a face recognition image, it could be misidentified.

By incorporating AdvML during the development phase, builders can improve their security measures and protect against these vulnerabilities. This will ensure that their models are resilient and accurate in various conditions.

AdvML stresses the importance of continuous monitoring and evaluation throughout AI systems’ lifecycle. Developers need to conduct regular assessments such as adversarial training or stress testing in order to detect potential weaknesses before they can be exploited.

By prioritizing AdvML, ML practitioners are able to proactively protect their technologies and reduce operational failure risks.

You can also read our conclusion.

AdvML is a category that, along with the others, demonstrates MLSecOps’ critical role in addressing AI Security challenges. Together, these categories demonstrate the importance of leveraging MLSecOps to protect AI/ML against existing and emerging threats. Security can be embedded into all phases of the AI/ML Lifecycle to ensure models are secure and resilient.