AI models are an exciting and innovative tool for streamlining workflows, reducing human error, and maximizing resources. However, AI models are also the next frontier in cybersecurity, requiring new approaches tailored to the unique risks posed by AI. 

Unlike traditional software, AI models pose risks related to data exposure, model manipulation, and unpredictable behavior, which require a tailored approach to assessment and risk management. In fact, 80% of data experts say that AI increases data security challenges.

An AI security assessment provides the structured framework organizations need to identify vulnerabilities, evaluate potential threats, and establish the right controls before issues escalate. Follow the steps in this guide to conduct a thorough and effective AI security assessment that keeps your systems safe, reliable, and compliant. 

1. Inventory All Models and Tools

Before you can secure your AI systems, you need to know exactly what you’re working with. That starts with a comprehensive inventory of all AI models and tools across your organization, regardless of who built them or where they live.

This step involves cataloging:

• Internally developed AI models, including version numbers and training datasets.
• Third-party tools and APIs, such as generative AI services, ML libraries, and data labeling platforms.
• AI-powered features embedded in other products, like analytics dashboards or customer service chatbots.

The goal is to build a clear map of your AI ecosystem, including where your team hosts the models, the data these models can access, and how your team uses these models. You’ll also want to track ownership: who’s responsible for maintaining and securing each model?

2. Collaborate With Vendors

Many organizations rely on third-party vendors for tools, models, or infrastructure, making vendor collaboration a critical part of your AI security assessment.

Start by identifying all external partners involved in your AI stack. This includes:

• Model and API providers
• Cloud service providers (CSPs)
• Data vendors 
• Third-party AI platforms integrated into your systems

From there, engage vendors directly to understand their security practices. Ask pointed questions:

• How do they secure their models and data pipelines?
• What safeguards are in place against misuse or data leakage?
• Do they offer audit reports? 
• Do they have third-party certifications?

You should also ensure that data handling and usage terms are clearly defined in your contracts, especially when it comes to sensitive or proprietary information.

3. Conduct a Risk Assessment

Once your AI assets and vendors are mapped out, it’s time to evaluate where the real risks lie. Conducting a risk assessment helps you prioritize which systems, models, or integrations pose the greatest security threats. 

Use standard frameworks like NIST AI RMF or ISO/IEC 23894 to guide your risk assessment. These frameworks ensure you think holistically about technical vulnerabilities, compliance, and ethical risk. 

At the end of this step, you should have a clear risk matrix: what’s at stake, where the risks are highest, and the actions required to mitigate those risks. 

4. Set Up Controls

Once you understand your risks, the next step is to implement controls. These are your front-line defenses that keep your AI systems secure and compliant.

Set up controls for: 

• Access: Limit who can view, modify, or deploy models. Use role-based access control (RBAC), multifactor authentication (MFA), and strong identity management practices to prevent unauthorized access.
• Data: Encrypt sensitive data in transit and at rest. Anonymize training data when possible, and establish policies to restrict the use of third-party data sources that may introduce legal or ethical risks.
• Models: Introduce safeguards like input validation, output monitoring, and adversarial testing. These help defend against prompt injection, data leakage, and model misuse.
• Auditability: Ensure your AI systems have clear logging and versioning. You should be able to track how your team trained and updated the model, as well as how the model makes decisions. 
• Human-in-the-loop (HITL): For critical use cases, such as financial recommendations or medical assessments, you should maintain human oversight to validate outcomes and catch anomalies.

5. Create Mitigation Plans

Even with strong controls in place, no system is immune to failure. That’s why creating mitigation plans is a core part of any effective AI security assessment. 

These plans prepare your team to respond quickly and effectively when issues arise, reducing damage, restoring trust, and ensuring business continuity. 

Don’t Let Risk Outpace Innovation

AI security isn’t a set-it-and-forget-it process. It requires ongoing monitoring and periodic reviews to stay ahead of emerging threats, ensure controls are working as intended, and adapt to changing threats. 

However, few organizations have the internal resources required for constant vigilance. Mindgard’s Offensive Security solution bridges this gap through red teaming and automated AI security testing built for modern AI. 

Whether you’re deploying LLMs, integrating third-party tools, or developing in-house models, Mindgard helps you identify vulnerabilities before attackers do.

Request a demo today and turn your AI security assessment into a strategic advantage.

Frequently Asked Questions

Are there specific regulations that require AI security assessments?

Not yet, but they’re coming. The EU AI Act, NIST AI RMF, and proposed U.S. legislation are pushing for risk-based classifications, transparency, and accountability. Even in jurisdictions without specific laws, conducting AI security assessments can help demonstrate due diligence and readiness.

What teams or roles should be involved in an AI security assessment?

It's not just an IT problem. Multiple roles have helpful context for identifying and mitigating risk. That’s why a robust assessment should involve cross-functional collaboration between data scientists, security teams, legal and compliance officers, DevOps, and product managers. 

How often should we conduct a full AI security assessment?

While monitoring should happen continuously, a full end-to-end security assessment is typically recommended at least annually. You should also conduct one when you deploy a new model, significantly alter your data sources, or expand into new regulatory environments.