AI does much more than write emails today. This technology is the backbone of essential technologies, from life-saving healthcare diagnostics to financial planning. Every AI model introduces some risk, though some can have a far-reaching impact, making them higher risk.
That’s why every organization deploying AI should follow a structured AI risk management checklist. Whether you work with sensitive data or complex automations, a proactive AI risk assessment can help you mitigate vulnerabilities before they become liabilities.
Learn what qualifies an AI model as high-risk, plus a step-by-step AI risk management checklist for reducing your exposure.
AI risk management is the process of identifying, assessing, and mitigating risks associated with artificial intelligence systems to minimize potential harm. The goal of risk management is to ensure that AI models are safe, ethical, and aligned with internal policies and external regulatory requirements.
This matters now more than ever, as AI has progressed beyond experimentation to systems that drive decisions in healthcare, financial lending, hiring, and public infrastructure.
As society increasingly relies on AI, organizations face greater legal liability when harm occurs, along with reputational damage and public scrutiny. Governments and standards bodies are also beginning to require formal risk management practices, rather than treating them as best practices.
AI risk management involves both technical controls (things automated systems can do) and governance controls (things humans do). Technical controls include model monitoring, adversarial testing, data quality validation, and performance audits.
Governance controls include clear accountability, documentation, role-based access controls, and incident response plans.
Risk management also requires AI risk assessments, which are structured evaluations of the likelihood of certain harms and their potential impact. Risk assessments provide organizations with the evidence they need to determine which AI systems require additional controls and oversight.
For example, a bank discovers that its credit-risk scoring model is utilizing “alternative” data sources (such as rental payment history, utility bills, and mobile-phone usage) that have not been fully validated.
Since these data sources had not undergone the same quality checks and regulatory review as traditional credit bureau data, the bank is exposed to increased model risk, compliance risk, and potential consumer fairness concerns.
This is a typical example of how having a large portfolio of AI/ML models with partial or no oversight can create hidden risk.
Global frameworks, such as ISO/IEC 23894 and the NIST AI Risk Management Framework, as well as regulations like the EU AI Act, are requiring organizations to implement AI risk management. These frameworks offer guidance on conducting risk assessments at each stage of the AI lifecycle: design, training, deployment, and ongoing monitoring.
By taking a proactive approach, organizations can build more trustworthy AI, reduce regulatory risk, and scale AI innovations responsibly.
Although the two terms are closely related, AI risk management and AI risk assessment are two distinct pieces of the same governance puzzle. AI risk assessment is a component of the broader AI risk management process.
You can think of risk management as the strategy and risk assessment as the diagnostic tool. Insights from assessments inform the overall management process, enabling teams to improve policies, retrain models, and enhance protections over time.
All AI systems have the potential for misuse, but the consequences differ. High-risk AI models have more serious consequences that can result in real-world harm.
In an AI risk assessment, “high-risk” refers to systems that, should they fail, could cause significant damage. These models need more rigorous monitoring because, if things go wrong, the consequences are severe.
This isn’t an exhaustive list, but you’ll need extra precautions in place if your AI falls into these categories:
You’re responsible for evaluating the model’s potential for harm. In most cases, it’s best to be overly cautious, since the stakes are so high.
Before you can assess or manage risk, you need to understand which types of risks AI systems can introduce. High-risk models often fall into one or more of the following categories:
AI systems can discriminate against protected groups if they are trained on skewed or non-representative data. This can lead to unjust outcomes in hiring, lending, law enforcement, healthcare, and other domains.
For example, an AI system used across U.S. health systems prioritized healthier white patients over sicker Black patients when determining eligibility for extra care, because it used healthcare-cost data as a proxy for illness rather than direct medical need.
AI models can leak sensitive information even if the training data is anonymized. Risks include inadvertent data exposure, model inversion attacks, and unauthorized access to sensitive data.
For example, research has demonstrated that large language models (LLMs) are vulnerable to regurgitating private training data back to users, violating GDPR, HIPAA, and other privacy regulations.
AI systems can be subverted by adversaries via adversarial inputs, data poisoning, jailbreaking, or API abuse. Attackers can use these methods to change model behavior or extract its private logic.
For example, Mindgard researchers showed that Microsoft’s Azure AI Content Safety filters could be bypassed using techniques such as character injection (e.g., invisible characters) and adversarial ML evasion, reducing the effectiveness of the guardrails and enabling harmful content to pass safeguards.
These findings expose critical risks associated with AI systems that rely on automated moderation, highlighting the need for layered controls and ongoing adversarial testing.
If the model’s behavior is not well-understood or explainable, it can be hard to audit, defend, or regulate. This includes black-box models, where the internal workings are opaque or the decision-making process is not well understood.
Lack of explainability can lead to difficulties in assigning responsibility or liability, especially when the model makes mistakes or causes harm. It can also make it difficult to comply with legal and regulatory requirements, such as the GDPR’s “right to explanation” or the CCPA’s “right to know” provisions.
Safety risks are prevalent when a system fails or behaves unpredictably in ways that can cause harm to humans or the environment, especially in high-stakes, time-critical, or safety-critical applications.
Even a small probability of failure can pose significant harm in cases where the AI is in control of a critical system or directly impacts human life (energy, healthcare, transportation, etc).
For example, malfunctioning or unreliable AI systems used in medical diagnostics have led to the misclassification of scans or medical images, potentially resulting in incorrect or delayed treatments.
Before you can assess or mitigate risk, you need a clear process for managing AI risks across its lifecycle. The AI Risk Management Process provides that structure. It outlines the core activities every organization should follow when working with high-risk AI systems, from identification to testing and ongoing oversight.
Begin by taking an inventory of every AI model your company uses, ranging from small automation tools to complex predictive systems. Once listed, classify each one based on its potential for harm or disruption.
To maintain consistency, develop an internal rubric that measures the impact on users, data, and operations. This approach will help you evaluate each model fairly, especially if you’re managing a large portfolio.
After that, conduct a formal AI risk assessment to understand the specific risks each model introduces, such as bias or safety concerns. This deeper analysis helps you determine which systems need more oversight.
Establish policies and controls for every high-risk AI in your organization. All companies are different, but these policies should be documented:
Controls can and should change over time, though, so consider this a living document that will evolve as you learn more about your model and potential risks.
Automating some of these controls, especially for monitoring or after-hours incident response, can help you react faster when something goes wrong. Mindgard’s always-on AI Artifact Scanning ensures your system functions as intended, 24/7.
After defining your policies, it’s time to implement them with safeguards. Train your AI models to resist adversarial attacks and data manipulation, and stay ahead of emerging threats through AI threat libraries and vulnerability databases.
Most importantly, test your defenses regularly with red teaming. These simulated exercises verify that your safeguards work as intended. Mindgard automates this process by continuously scanning your models for vulnerabilities and tracking the progress of mitigations.
If a system is classified as high-risk, it requires structured oversight and measurable safeguards to ensure its safety. Use this checklist to turn high-level risk principles into operational steps.
Start by mapping out your AI landscape and quantifying the level of exposure each model brings.
Once you’ve identified high-risk systems, establish clear governance policies to manage them.
Translate policies into proactive, measurable defenses.
Discovering a system is high-risk doesn’t mean you should shut it down. The objective is to reduce exposure by implementing appropriate safeguards, both technical and procedural. Top strategies include:
Running through an AI risk management checklist once isn’t enough. High-risk AI systems need ongoing oversight. Unfortunately, managing this manually just isn’t possible, even with an experienced team.
That’s where Mindgard comes in. With automated scanning and red teaming designed specifically for AI models, Mindgard’s Offensive Security and AI Artifact Scanning solutions make it easier to identify risks before they cause real-world harm. Take the guesswork out of AI risk assessments: Request a Mindgard demo today.
A comprehensive AI risk management checklist should cover every stage of the AI lifecycle, from design to monitoring. Every company’s checklist will differ, but it should cover data governance, bias detection, access controls, and incident response.
It depends. You should always perform an AI risk assessment before deployment and at regular intervals afterward, particularly when retraining models. Some businesses conduct AI risk assessments on a monthly basis, while others do so a few times a year. Ultimately, it comes down to the nature of your AI models and your appetite for risk.
No. The EU AI Act is just one of many frameworks for assessing high-risk AI systems. Other standards, such as ISO/IEC 23894 (AI risk management) and ISO/IEC 42001 (AI management systems), also outline requirements for identifying, monitoring, and mitigating risks.
Outside Europe, frameworks such as the U.S. NIST AI Risk Management Framework (AI RMF) provide guidance or establish legal expectations for assessing AI systems that may impact human rights, safety, or critical services.
As an AI developer, you’re ultimately responsible for identifying and protecting vulnerable AI systems. Any model capable of affecting people’s safety, finances, or fundamental rights should be treated as high-risk, regardless of whether you’re legally required to do so.
