Nothing is impervious to cyber attacks, including AI systems. AI has access to sensitive information that could cause tremendous harm in the wrong hands. Organizations should regularly conduct AI vulnerability assessments to spot potential weaknesses in their armor and address the most pressing issues.
However, many internal teams are unsure where to begin with conducting an AI vulnerability assessment. In this guide, you’ll learn the five essential steps to conducting an AI vulnerability assessment, as well as how solutions like Mindgard can fill in the gaps.
An AI vulnerability assessment is a structured review of an organization’s AI systems and infrastructure to identify potential weaknesses that attackers could exploit. While a traditional security assessment looks for issues in networks or applications, an AI vulnerability assessment focuses on risks unique to machine learning and AI pipelines.
This methodology should encompass the entire AI application lifecycle, including data collection, preparation, and storage, model training, and model deployment to production. Vulnerabilities can show up at any point in this chain. This can include risks such as:
Assessing your AI infrastructure helps security teams achieve full visibility into their exposure levels. Teams can better prioritize remediation efforts before deploying models, which lowers the risk of model compromise and enhances AI system reliability under real-world conditions.
An AI vulnerability assessment helps protect your models from security threats and performance issues. Follow these steps to strengthen AI resilience.
Start by defining the scope of your assessment. This ensures you conduct a focused assessment that covers the right assets.
During this stage, work with your team to define objectives and goals. For example, are you assessing primarily for security threats, performance, or compliance? It’s also crucial to note any boundaries or exclusions from testing, as well as a tentative timeline and a list of participating personnel.
Once you’ve defined your scope, the next step is to create a complete inventory of everything that could be affected by an AI vulnerability.
Every organization will scan different assets, but some of the most common include:
Next, rank assets by importance. This step ensures you’re not just aware of each asset, but also focusing your efforts on where a breach or failure could cause the most damage. Use a simple scoring system (such as high, medium, and low) to rank assets and allocate resources where they’ll have the most impact.
Start with threat modeling to visualize and anticipate potential attack paths for each asset. By mapping out how an attacker could compromise your assets, you can proactively target high-risk areas during scanning.
Next, the AI vulnerability scan should look at each layer of the system, including hardware, software, networks, and processes. Use a mix of automated tools, such as Mindgard’s Artifact Scanning solution, and manual reviews.
Conducting an AI vulnerability assessment will give your team a list of weaknesses that need to be addressed. However, you likely don’t have the resources to treat all of these gaps equally.
During this step, your team needs to remediate the issues that are most likely to cause serious harm first, rather than using resources on low-impact issues.
Deploy security patches, update configurations, or replace outdated systems starting with the highest-severity items. Test all fixes in a controlled environment before pushing them live to avoid introducing new vulnerabilities.
AI vulnerability assessments are an essential part of securing AI models, especially those that use proprietary or sensitive data. New threats emerge, models evolve, and integrations change. Over time, yesterday’s secure system could become tomorrow’s entry point for attackers.
Continual vulnerability management ensures you’re always ready to respond to new risks. Stay ahead of emerging threats by:
The five steps in this guide will help you create a proactive strategy that evolves alongside your AI systems and stays ahead of the threats of tomorrow. Still, manually managing these assessments requires significant time and resources.
A modern AI security platform like Mindgard uses automation to reduce manual effort and speed up threat detection. Mindgard’s Artifact Scanning solution provides automated AI vulnerability assessment to help organizations:
Build a more resilient AI ecosystem: Book your Mindgard demo today.
A cross-functional team works best. This typically includes AI engineers, data scientists, security professionals, compliance officers, and, when relevant, third-party security partners.
Prioritize fixes based on the likelihood of exploitation and potential impact. Use a scoring framework, such as the Common Vulnerability Scoring System (CVSS), to rank vulnerabilities as critical, high, medium, or low, and remediate them in that order.
Yes. Scans identify existing weaknesses, but threat modeling helps predict future vulnerabilities and potential attack paths, allowing you to stay ahead of risks rather than just react to them.
