An AI security assessment identifies and mitigates the risks unique to AI systems through six steps: inventorying AI assets, evaluating vendors, analyzing risk, setting controls, planning mitigation and validating it all with AI red teaming.
97% of organizations that suffered an AI-related breach lacked proper AI access controls, according to IBM’s 2025 Cost of a Data Breach Report. An AI security assessment finds those gaps before attackers do: it is a structured evaluation of every AI model, agent and data pipeline you run, moving through six steps from inventory and vendor review to risk analysis, controls, mitigation planning and red team validation.
The framework below aligns with the NIST AI RMF and ISO/IEC 23894, so the assessment you conduct stands up to auditors and adversaries alike.
The scale of the problem shows up clearly in IBM's 2025 Cost of a Data Breach Report: AI breaches are no longer rare, and the organizations getting hit are overwhelmingly the ones that skipped basic assessment work. Almost every breached organization lacked AI access controls, and a fifth of breaches traced back to shadow AI no one had inventoried.
Each gap in the chart below maps to a step in the assessment process that follows.
An AI security assessment is a structured, systematic evaluation of an organization’s AI systems, covering models, agents, data pipelines, APIs and supporting infrastructure, that identifies vulnerabilities, quantifies security risk and assigns controls.
An AI security assessment extends a traditional security audit with tests for failure modes unique to AI: prompt injection, jailbreaks, data poisoning, model extraction and unsafe agent behavior. NIST catalogs these attack classes in its Adversarial Machine Learning taxonomy, NIST AI 100-2, updated in March 2025.
A complete assessment produces three artifacts:
Most security teams repeat the assessment at least annually and after every major model, agent or data source change.
These attack classes are not theoretical, and the bar for attackers is low:
“Most of these attacks are fairly easy to mount and require minimum knowledge of the AI system and limited adversarial capabilities.”
- Source: Alina Oprea, Professor, Northeastern University and co-author of NIST AI 100-2. NIST News, January 2024
That asymmetry is why the assessment below treats adversarial testing as a required step, not an optional extra.
Before you can secure your AI systems, you need to know exactly what you’re working with. That starts with a complete inventory of all AI models and tools across your organization, regardless of who built them or where they live.
This step involves cataloging:
The goal is to build a clear map of your AI estate, 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?
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:
From there, engage vendors directly to understand their security practices. Ask pointed questions:
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.
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.
Start with the NIST AI RMF and its four functions, Govern, Map, Measure and Manage, then map identified threats to MITRE ATLAS techniques so findings use a vocabulary your security team already shares. ISO/IEC 23894 extends the same discipline to AI-specific harms. These frameworks make sure you cover technical vulnerabilities, compliance and ethical risk.
NIST’s own researchers are blunt about what happens when this step gets skipped:"
“Despite the significant progress AI and machine learning have made, these technologies are vulnerable to attacks that can cause spectacular failures with dire consequences.”
- Source: Apostol Vassilev, computer scientist, NIST and co-lead author of NIST AI 100-2. NIST News, January 2024
A framework-aligned risk matrix is how you find out which of those failures your organization is actually exposed to. For a step-by-step walkthrough of applying the framework, see our NIST AI RMF implementation guide.
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.
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:
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.
Your mitigation plan should name four things:
Test the plan with a tabletop exercise before an incident forces the issue. For prioritized fixes by risk type, see our guide to AI risk mitigation strategies.
AI red teaming is the adversarial security testing that validates an AI security assessment: simulated attacks against your deployed AI systems that prove whether controls hold. Red team exercises target the attack classes that matter for artificial intelligence security: prompt injection that hijacks agent behavior, jailbreaks that bypass guardrails, extraction of training data from fine-tuned models and tool-chaining exploits inside agentic workflows.
Every confirmed exploit feeds back into the risk matrix from step 3 and the control plan from step 4, turning the assessment from a snapshot into a loop. Automated AI red teaming platforms run these attack libraries continuously, so a new vulnerability surfaces in days rather than at next year’s audit.
Microsoft’s AI Red Team reached the same conclusion after testing more than 100 generative AI products:"
“The work of securing AI systems will never be complete. But by raising the cost of attacks, we believe that the prompt injections of today will eventually become the buffer overflows of the early 2000s.”
- Source: Blake Bullwinkel, Amanda Minnich, Shiven Chawla et al., Microsoft AI Red Team, "Lessons From Red Teaming 100 Generative AI Products", January 2025
Raising the cost of attacks is exactly what a completed assessment does; red teaming is how you verify the price went up.
An AI security assessment framework gives organizations a shared structure for the whole exercise, and five frameworks do most of the work. The NIST AI RMF organizes the assessment around four functions: Govern, Map, Measure and Manage.
NIST AI 100-2, the adversarial machine learning taxonomy updated in March 2025, supplies the attack vocabulary for AI threat modeling. ISO/IEC 42001 defines an auditable AI management system that enterprise buyers increasingly require from vendors.
ISO/IEC 23894 extends classic risk management guidance to AI-specific harms. MITRE ATLAS maps real-world adversary tactics against AI systems, and the OWASP Top 10 for LLM Applications ranks the vulnerabilities most likely to appear in generative AI deployments.
Use the NIST AI RMF as the spine of your AI security assessment and the other frameworks as reference layers for threats, controls and certification.
Four tool categories automate most of an AI security assessment.
Manual assessments of a handful of models can run on spreadsheets, but once AI systems reach double digits, automation is the only way assessment frequency keeps pace with deployment frequency. For a vendor-by-vendor comparison, see our roundup of the best AI security tools.
This AI security assessment checklist gives security and IT teams ten items to verify:
The AI security assessment checklist pairs with our broader AI risk management checklist for governance items beyond security. Work through it with the interactive scorecard below, which turns your answers into a measurable AI security posture score, then close gaps in priority order.
AI Security Assessment Readiness Scorecard
Answer eight questions to score your AI security posture across the checklist areas covered in this guide: inventory, ownership, vendor risk, NIST AI RMF alignment, access controls, data protection, monitoring and red teaming. Scoring runs in your browser; nothing is stored or sent anywhere.
Each answer scores 0, 1 or 2 points across eight controls, for a maximum of 16. Tiers: 0 to 6 = Exposed, 7 to 11 = Developing, 12 to 16 = Assessment-Ready. The control areas mirror the NIST AI RMF functions and the checklist in this article. Benchmark context: 97% of organizations that suffered an AI breach lacked proper AI access controls, per IBM's Cost of a Data Breach Report 2025.
Sources: IBM Cost of a Data Breach Report 2025; NIST AI Risk Management Framework. Scorecard logic by Mindgard.
AI security isn’t a set-it-and-forget-it process. It requires ongoing monitoring and periodic reviews to stay ahead of emerging threats and confirm controls keep working as models change. The pressure is not new: back in 2024, 80% of data experts already said AI was making data security harder, and the gap has only widened since.
IBM’s 2025 breach data shows where standing still leads:
“The data shows that a gap between AI adoption and oversight already exists, and threat actors are starting to exploit it.”
- Source: Suja Viswesan, Vice President of Security and Runtime Products, IBM. Cost of a Data Breach Report announcement, July 2025
A repeatable assessment cycle, backed by continuous red teaming, is how that gap gets closed before it becomes an incident.
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.
Yes, specific regulations now require AI security assessments and documented AI risk management, and the list keeps growing.
The EU AI Act entered into force on August 1, 2024: obligations for general-purpose AI models have applied since August 2, 2025, and Article 50 transparency rules take effect August 2, 2026. The Digital Omnibus agreed in June 2026 moved most high-risk deadlines to December 2, 2027 for Annex III systems and August 2, 2028 for Annex I products. In the U.S., Texas’s TRAIGA took effect January 1, 2026 and Colorado’s AI Act follows on June 30, 2026.
The NIST AI RMF remains voluntary, but regulators treat it as the baseline, so conducting an AI security assessment remains the fastest way to demonstrate due diligence in any jurisdiction.
It's not just an IT problem. Multiple roles have helpful context for identifying and mitigating risk. That’s why a thorough assessment should involve cross-functional collaboration between data scientists, security teams, legal and compliance officers, DevOps and product managers.
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.
Assessing AI agents means testing what the agent can do, not just what it can say. Inventory every tool, API and data source the agent can reach, then test for indirect prompt injection, where malicious instructions hidden in emails, documents or web pages hijack the agent’s actions.
Test as well for tool-chaining exploits that turn benign permissions into data exfiltration paths. Apply least-privilege scopes to every agent credential and log every tool call. Agentic systems change faster than chatbots, so assess them at every capability change rather than on an annual cycle.
Our guide to AI agent security challenges covers the attack paths in depth.
An AI security assessment is a technical evaluation of AI systems for exploitable vulnerabilities, misconfigurations and missing controls. An AI risk assessment is broader: it weighs security findings alongside compliance, ethical, operational and reputational impacts to prioritize what the organization addresses first.
In practice the security assessment supplies the vulnerability evidence that feeds the risk assessment’s scoring. Run the security assessment first, then fold its findings into your wider AI risk register.
Mindgard helps organizations discover, assess and defend their AI systems. Spun out of more than a decade of AI security research at Lancaster University in the UK and headquartered in Boston and London, Mindgard operationalizes the expertise of AI researchers and offensive security practitioners through a Security Platform that performs Shadow AI discovery, AI red teaming, and run-time AI protection to assess and mitigate risk across models, agents, and applications.
The expert-level checklist for operationalizing NIST AI RMF, ISO/IEC 42001 and the EU AI Act. 190+ interactive items and a board-ready maturity scorecard. Built for CISOs, AI governance leads and ML engineering teams.
