AI/ML security assessments can unveil a wide array of potential vulnerabilities, which can be broadly grouped into several categories.
Data protection and access control: Issues in this category often revolve around inadequate data protection measures, insecure data storage and transmission methods, and insufficient access controls for AI/ML systems. Additionally, over-privileged user accounts in AI/ML environments may pose a considerable risk.
System infrastructure and component management: This category includes insecure integration of third-party AI/ML components, use of outdated or vulnerable AI/ML libraries and tools, and misconfigurations in AI/ML infrastructure, including cloud platforms. Weak or nonexistent AI/ML security testing processes and over-reliance on default security configurations for AI/ML tools can further expose systems to potential threats.
AI/ML model management: Insecure model training and validation processes, and inadequate AI/ML model version control and management can lead to significant risks in this category. Moreover, the lack of transparency and explainability in AI/ML models, as well as insufficient model life cycle management and retirement processes, can compound these .
AI/ML model robustness and security: AI robustness refers to a model’s ability to resist being fooled, and data poisoning refers to the potential for training data to be corrupted. Taken together, these underscore the importance of performing assessments against the model itself to understand its limitations, ways to exploit it, and how to protect it.
Design flaws and single-point failures: Discovering defects in AI/ML models is a common problem. Poor performance in ML and AI models is usually caused by inadequate or insufficient input data, an incorrectly trained neural network that cannot produce accurate results for given inputs, or bugs in the code used to train it. Additionally, the architecture surrounding the model needs to be adequately evaluated to ensure that it won’t be the source of potential failures.
Policy, governance, and compliance: This encompasses the lack of AI/ML-specific security policies and procedures, the absence of an AI/ML governance structure, and noncompliance with data privacy regulations. Moreover, no AI/ML-specific risk assessment and management processes and inadequate AI/ML supply chain security measures can put the organization at risk of regulatory penalties.
Monitoring, incident response, and recovery: Inadequate monitoring and auditing of AI/ML systems, poor incident response and recovery plans for AI/ML-related incidents, and ineffective AI/ML model monitoring and performance-tracking can severely hinder an organization's ability to respond to and recover from security incidents.
Training, documentation, and transparency: This group includes insufficient AI/ML security training and awareness programs, inadequate documentation of AI/ML systems and processes, and poor AI/ML patch management and vulnerability remediation practices. These issues can lead to gaps in understanding and mitigating potential threats to the organization's AI/ML systems.
The goal an assessment is not just to expose these weaknesses but also to provide actionable solutions to fortify your AI/ML infrastructure holistically.