How to Safeguard LLMs with Guardrails
Guardrails help secure large language models by preventing harmful outputs and misuse through clear policies, limited access, and expert-led testing and monitoring.
Fergal Glynn
AI systems learn and adapt, and sometimes they can behave in ways even their creators can’t fully predict. AI has access to a lot of sensitive data and systems, which is why AI risk assessments are so crucial. Properly understanding and acting on potential threats helps businesses mitigate risks before they cause real harm.
An AI risk assessment is more than a traditional IT audit. In addition to AI security concerns, it also looks at the potential for bias and compliance issues. AI risk assessments are a must before deployment, after major updates, and on a regular basis to ensure continuous monitoring.
An AI risk assessment is a formalized process for identifying, analyzing, and ranking risks associated with the development or use of artificial intelligence technologies. It’s a method for organizations to identify potential areas where AI use can cause harm or loss (such as bias, data leakage, security breaches, or unintended behavior) and determine how to mitigate or manage those risks before they impact operations, users, or regulatory compliance.
Unlike traditional IT risk assessments, AI risk assessments consider how models make decisions, the data they learn from, and the contexts in which they operate. They examine technical, ethical, and regulatory dimensions simultaneously. This means assessing not only whether a model performs accurately, but also whether it behaves responsibly when faced with ambiguous or adversarial input.
Ideally, AI risk assessments should cover the complete model lifecycle, from design and training to deployment and monitoring. They should identify potential failure modes of the model, how they could translate into undesirable real-world consequences, and what governance mechanisms are needed to detect and prevent them.
The outcome of an AI risk assessment is a comprehensive view of the organization’s AI risk posture and a plan for ongoing monitoring as the systems change.
Skipping AI risk assessments leaves an organization vulnerable. AI systems are increasingly embedded in critical decision-making, so the risk from unsafe AI multiplies quickly. An innocuous-looking algorithm might deny a loan application, screen a résumé, or filter personal data.
A slight oversight, replicated across millions of operations, could become a systemic failure. Real-world failures make the consequences of poor AI governance impossible to ignore:
The technical damage from AI system failures is only part of the risk. The EU AI Act will require high-risk systems to meet stringent transparency and governance criteria. Companies that fail to document risk assessments could face fines of up to 7% of global turnover. In the U.S., the FTC has already warned companies that deceptive claims of AI safety or fairness will lead to enforcement action. For investors, partners, and customers, those deficiencies erode trust and credibility.
Align your assessments with global standards like ISO/IEC 42001. This standard outlines how to establish and operate an AI management system. It can help you ensure your risk framework is well-organized, auditable, and prepared for new or changing regulations.
AI risk assessments enable transparency. A well-documented assessment shows how risks were scoped, addressed, and are being monitored:
Follow these five steps to conduct a thorough AI risk analysis that not only improves compliance but also gives your organization a competitive advantage.
The initial stage of every AI risk assessment is defining its scope: what you’re evaluating, why, and who’s responsible. A well-defined scope helps you identify the most at-risk systems and enables your team to stay laser-focused on the most relevant systems without wasting time chasing low-impact risks.
At this stage, decide whether you’ll use a qualitative, quantitative, or hybrid AI risk assessment method. This choice shapes how you’ll measure likelihood and impact later.
Begin by listing the AI systems or components included in this assessment. Are you evaluating:
Document what’s in and what’s out of scope for this assessment, and why. For instance, your in-scope customer service chatbot will be assessed for risk because it interacts with personal data, but internal A/B testing models are out-of-scope because of very low external exposure. The in/out list should be clear enough that anyone can tell if a new risk is relevant to your assessment, and detailed enough to be used as an audit record.
Lastly, map out the stakeholders who are involved with the AI systems and will take part in the assessment. This includes:
Defining roles and responsibilities early prevents gaps in accountability and ensures that both technical and ethical risks are addressed from the start.
To effectively manage AI risks, you need visibility into all models used and relied upon by the organization, including those requiring generative AI risk assessments for models that produce text, images, or code. Begin by creating a centralized AI inventory that includes each system, its use case, and the associated risks.
Information for each AI system should include:
Each system’s inclusion in your AI inventory lays the groundwork for a comprehensive AI security risk assessment, helping you uncover data points like unprotected APIs, insecure model endpoints, and external data dependencies.
The catalog serves as the foundation for your AI governance program. It enables you to:
Every AI has a unique risk profile based on the sensitivity of its data, the criticality of its decisions, and its exposure to external factors. Therefore, each application should be evaluated individually instead of applying a one-size-fits-all risk level to everything.
The table below highlights the key types of AI risks and mitigation strategies.
If you aren’t sure how to assess these risks, use a trusted framework like the NIST AI Risk Management Framework (AI RMF) to speed up the process.
Once you’ve identified potential risks across your systems, the next step is to prioritize them through AI model risk assessment and AI risk scoring to determine which issues pose the greatest impact.
Not every risk warrants the same level of urgency or investment. Some may be catastrophic if left unaddressed, while others are low-impact and can be managed with less effort or in lower priority.
Scoring and ranking risks helps you make the most of your resources, especially if you have a small development team. You can begin this process by assigning a score to each individual risk, based on two primary criteria:
AI risk scoring combines these two dimensions to give each risk a quantifiable value that supports consistent prioritization across systems. Choose a consistent AI risk assessment method for scoring (qualitative, quantitative, or hybrid) to ensure risks are evaluated objectively and compared on equal footing.
Next, plot each risk on a risk matrix. The matrix visualizes where risks fall based on their combined likelihood and impact:
The above matrix allows decision-makers to quickly visualize which risks are above your organization’s risk appetite (i.e., the maximum level of risk you’re willing to accept).
Within the context of AI, keep in mind that the impact of risks can also encompass ethical failures (bias, transparency, etc.) and reputational damage in addition to any financial or technical damage or costs. Use your scoring outcomes to drive focused AI risk mitigation strategies and justify investments in AI governance, monitoring, and control improvements.
The next step is to remediate risks in order of severity and business impact. Your AI security risk assessment results should guide which threats to address first, ensuring critical vulnerabilities are mitigated before they affect compliance or operations.
Your team should address critical issues immediately, followed by high, medium, and low-priority issues. Establish timelines and accountability to ensure issues are progressing to resolution.
Your chosen AI risk assessment method should also inform how you prioritize and allocate controls across AI systems. In some cases, a targeted set of controls can address multiple risks simultaneously. Common examples include:
Assign an owner to each risk, along with a mitigation plan and a timeline for completion. Owners should document what action will be taken, why it’s effective, and how success will be measured.
Finally, establish Key Risk Indicators (KRIs) or performance metrics to track progress. Example KRIs include:
These metrics help teams ensure that risks are actually closed, not just patched. Over time, this increases accountability and builds a repeatable framework for AI risk management.
Conducting manual generative AI risk assessments can boost your AI security efforts, but a one-time analysis is just a snapshot in time. As data and user interactions change, so will AI model behavior, meaning yesterday’s low-risk system can become high-risk overnight. To stay ahead of this, your organization needs 24/7 monitoring to supplement periodic AI risk analysis.
Integrate 24/7 monitoring tools that align with your AI model risk assessment framework. These tools should automatically detect and flag:
Tools should alert, log, and feed data back into AI governance dashboards. Continuous monitoring can become the foundation for an AI assurance feedback cycle over time: detect, respond, retrain, and report.
Tools like Mindgard’s AI Artifact Scanning solution provide 24/7 surveillance of your AI systems, allowing you to detect anomalies, adversarial manipulation, and model drift in real time. With these insights, your team can catch issues early and respond before they escalate, reinforcing compliance and trust.
AI algorithms shape outcomes at every level. Learning how to conduct an AI risk assessment is crucial for protecting your organization’s data and maintaining users’ trust in your company. Regular AI risk assessments are a critical first step, but on their own, they’re not enough to protect your data, reputation, and long-term business value.
By implementing the five steps outlined in this guide, your organization can build a proactive and repeatable framework for identifying, ranking, and mitigating AI risks. Incorporating structured AI risk scoring ensures that the most critical threats are prioritized and tracked effectively. As your systems mature, this evolves into a continuous AI model risk assessment cycle, detecting, responding, and retraining models as new threats emerge.
Risk changes fast, and your team needs to keep up, so you can’t rely on manual reviews alone. Continuous monitoring is critical. Mindgard’s AI Artifact Scanning and Offensive Security solutions offer 24/7 AI threat monitoring and anomaly detection to help your team stay ahead of emerging risks and act before they cause disruption or erode customer trust.
Stay ahead of rapidly evolving AI threats: Book a Mindgard demo today and see how automated AI risk monitoring can turn your assessments into real-world protection.
Cybersecurity protects your infrastructure, while AI risk management protects your decision-making engine. A cybersecurity audit focuses on system-wide defenses like firewalls and access controls. An AI risk assessment goes further, evaluating how AI-specific factors like bias and model drift affect your organization.
At a minimum, revisit them quarterly for high-impact systems or after major changes. Many organizations now treat these assessments as living documents and update them in tandem with ongoing model monitoring tools.
Track leading indicators like model accuracy drift, false-positive rates, access log anomalies, and user feedback trends. Combine these with trailing indicators such as audit results or incident frequency.
