A step-by-step guide to AI security assessments [With a template]

As artificial intelligence becomes deeply integrated into business operations, organizations have started feeling the pressure to keep up. According to Vanta’s 2025 survey, more than 50% of the organizations report being overwhelmed by the speed of AI adoption and growing compliance obligations.

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This issue is aggravated by the fact that AI tools evolve faster than governance policies can adapt, potentially leaving complex gaps for security teams to fill. One of the standardized ways to address this issue is to conduct regular AI security assessments, as they help evaluate and proactively plan for the risks associated with AI systems.

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In this article, we’ll explore an AI-informed approach to security assessments and discuss:

• The concept of AI security assessments and their benefits
• A five-step process for conducting effective AI security assessments
• A template to support the process

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What is an AI security assessment?

An AI security assessment is a formalized process of identifying, evaluating, and mitigating security threats associated with the design, deployment, and monitoring of AI systems. Its primary goals are to proactively address AI system vulnerabilities, ensure operational resilience, and support compliance with emerging AI regulations and standards.

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Given the variety of threats AI systems face, an effective AI security assessment should address risks such as:

• Unauthorized system access
• Malicious external attacks
• Algorithm or training data manipulation
• AI-related supply chain attacks

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What should an AI security assessment include?

To ensure a comprehensive review, your AI security risk assessment must cover the following components:

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Benefits of AI security assessments

Regular AI security assessments strengthen your organization’s AI governance program. Here are some granular benefits:

• Improved cyber resilience: Ongoing assessments enable you to proactively identify vulnerabilities in your AI security posture, update your incident response plans, and be better prepared for emerging AI threats
• Ethical and responsible AI use: Reviewing your outputs and training data allows you to identify potential inaccuracies and biases, which helps you address behavioral anomalies and fine-tune AI systems with retraining
• Increased stakeholder confidence: A completed AI security assessment demonstrates your organization’s commitment to responsible AI use, which helps build trust with your stakeholders and adds credibility to your AI systems
• Reduced downtime and better operational continuity: Remediating vulnerabilities after each assessment cycle helps you avoid or minimize potential operational disruptions
• Alignment with evolving regulatory landscape: Reviewing your AI security posture frequently enables you to update your safeguards and policies to reflect the latest regulatory requirements, if applicable

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Complete comprehensive AI security assessments in 5 steps

The specifics of your AI security assessment will depend on your industry, tech stack, and applicable AI use cases, but the following steps provide a strong foundation:

1. Scope your assessment and inventory your AI assets
2. Perform an internal gap assessment
3. Review AI vendor relationships
4. Develop incident response plans
5. Continuously monitor your AI systems

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Step 1: Scope your assessment and inventory your AI assets

To properly scope your AI security assessment, you first need to define its objective, build an inventory of all AI assets, assign a risk profile to each, and include support systems that aren’t necessarily AI systems but can impact AI security. IT systems without AI integrations, those that don’t handle sensitive data, and decommissioned systems are usually out of scope.

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When building your inventory, include all internal AI models, third-party services, and tools with embedded AI features in your list. Keep the inventory data in a centralized hub to enable your security and IT teams to maintain a holistic view of your AI systems and their dependencies.

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You should also consider the possibility of undetected AI programs, (shadow AI). You may have to ask your team to disclose the use of any such programs and add them to your inventory. In the long run, you can establish technical oversight and clear policies that govern the use of shadow AI.

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It’s worth noting that asset inventory is a core compliance requirement for several AI-related standards and regulations, such as the NIST AI RMF and the EU AI Act, which emphasize the need to identify and document AI systems as part of broader security efforts.

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Step 2: Perform an internal gap assessment

Regardless of whether you build and use internal AI models or rely on external partners, verifying your security safeguards is essential for risk assessment and gap remediation.

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For this step, you can start by identifying AI regulations that apply to your industry and region, and use them as a reference point to conduct a gap assessment. After identifying regulatory and security gaps, conduct a thorough threat modeling and risk assessment exercise to prioritize what gaps to remediate first.

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“

Typically, threat modeling and risk analysis of AI systems is the most time-consuming part of an AI security assessment. Lack of mature frameworks, complex attack surfaces, and resource dependencies from multiple teams leads to increased effort.”

Tim Blair

Sr. Manager, GTM GRC SMEs, Vanta

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LinkedIn

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AI gap assessments require you to perform reviews of:

• Access control policies
• Adversarial testing
• Integration points
• Logging policies

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Remediation measures will depend on the risks uncovered and their impact. For instance, Samsung banned the use of generative AI tools like ChatGPT after discovering the upload of sensitive code to the platform, which is a high-impact risk.

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Step 3: Review AI vendor relationships

Instead of building their own models, many organizations use managed AI services, which only expands their attack surface. Before you integrate vendor tools into your tech stack, it’s essential to vet each vendor through detailed questionnaires and ensure that they meet your internal security standards and regulatory requirements.

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Some of the most important aspects of vendor services you should address include:

• Privacy policies: If you share sensitive data, verify how vendors handle it—mainly, how the data is stored, if it’s shared with their subcontractors, and whether the information is used for training AI models
• Technical and administrative safeguards: Establish whether the vendor’s safeguards meet your security standards and if they are effective against data leaks, unauthorized access, and model manipulation
• Performance testing: Determine if the vendor conducts regular reviews to track AI model performance, identify model drift, and test output fairness
• Reporting and communication: Ensure that your vendors have clear escalation procedures and reporting mechanisms for AI-related incidents

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Step 4: Develop incident response plans

With AI’s rapid evolution, the technology is introducing new and unpredictable vulnerabilities, making AI-informed incident response plans essential. Your incident response plan should include procedures for detecting and responding to mapped AI threats, as well as rollback procedures for deployment issues.

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You should also establish continuous monitoring for AI security controls so that you can detect and respond to security incidents faster, minimizing their impact and reducing potential downtime.

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It’s equally important to develop communication protocols for materialized incidents. Define task owners and communication channels for reporting to customers, partners, and, if applicable, regulatory bodies, to demonstrate transparency and build trust.

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Step 5: Continuously monitor your AI systems

Although frequent AI security audits are useful, they don’t capture risks that emerge between cycles. To bridge this gap, integrate AI oversight into your existing monitoring processes. With the right setup and system integrations, you can monitor suspicious data flows, model update logs, unusual outputs, and access logs in real time.

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However, manually monitoring all AI security aspects can be time-consuming, even for a medium-sized team. That’s because AI systems tend to generate huge amounts of data, which can result in audit fatigue. The better solution is to configure existing tools to flag anomalies, record key events, and generate audit-friendly reports.

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Stay on top of your AI security posture with Vanta

Vanta is a leading trust management platform that helps organizations demonstrate responsible AI practices and strengthen customer trust. We achieve this by providing tailored resources and guidance for robust AI governance, aligned with AI-relevant regulations and frameworks like the EU AI Act and ISO 42001.

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Vanta also offers a standardized, expert-vetted AI Security Assessment Template that you can follow to guide your internal and external (vendor) AI security assessments. It covers all key risk areas and is a demonstrable tool for trust management across industries.

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Vanta can support other AI security and governance aspects with capabilities like:

• Question Automation, which speeds up responses to security questionnaires
• A unified monitoring dashboard
• Automated evidence collection powered by 400+ integrations
• Pre-built policy templates and control sets
• Access to public-facing Trust Centers

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Schedule a custom demo to explore how Vanta can support your AI security program.

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