An AI risk assessment produces a scoped inventory plus a living risk register that ranks risks, assigns owners, and defines review triggers. Start by inventorying your AI systems today, beginning with the one that touches the most customers or the most sensitive data. The backbone references for this work are the NIST AI Risk Management Framework, the EU AI Act, and the MIT AI Risk Repository.
TL;DR:
- EU AI Act rules require continuous risk management for systems deemed high risk, and certain deployers must also complete a Fundamental Rights Impact Assessment.
- Start with the system that reaches the most customers or handles the most sensitive data, and timebox its first assessment to two weeks.
- Use ordinal likelihood and impact scores rather than unsupported percentages; reserve probabilistic pathway modeling for high stakes systems where failures could cascade across the organization.
- Each register entry should name an individual owner, distinguish controls already in place from planned work, and include a review date and escalation trigger.
- For conversational agents, sample real transcripts and probe edge cases; independent audits add depth, while continuous monitoring tracks behavior across every conversation.
Table of Contents
- Canonical frameworks and regulatory touchpoints to anchor your assessment
- A step-by-step AI risk assessment process for teams
- How to build and operate a living AI risk register
- Measuring and prioritizing risks: scales, methods, and cadence
- High-value templates and repositories to reuse
- Practitioner example: where independent audits and continuous monitoring plug into assessments
- Types of AI risks specific to conversational AI
- Techniques for auditing and verifying AI behavior in real customer conversations
- Integration of AI risk assessment outcomes with customer experience metrics
- Continuous monitoring and updating risk assessments in dynamic AI environments
- Methods for interpreting customer feedback and actionable insights from AI interactions
- Make assessments habitual, not a one-off compliance checkbox
- How Lexic helps you operationalize AI risk assessments
- FAQ
- Sources
Canonical frameworks and regulatory touchpoints to anchor your assessment
Three reference points should shape how any assessment is scoped and documented. The NIST AI Risk Management Framework organizes activity around four functions and its accompanying Playbook maps suggested actions to each one. The EU AI Act sets binding obligations for certain systems. The MIT AI Risk Repository supplies a shared vocabulary so teams are not building a taxonomy from scratch.
- Govern: establish policy, roles, and accountability for AI risk management before assessing individual systems.
- Map: identify context, intended use, and potential impacts for each AI system.
- Measure: analyze, benchmark, and track risks using qualitative and quantitative methods.
- Manage: prioritize and respond to risks based on assessed impact.
Under the EU AI Act’s Article 9, providers and deployers of high-risk AI systems must implement a continuous risk management system, and certain deployers must additionally complete a Fundamental Rights Impact Assessment under Article 27. Both obligations require documented evidence of risks and mitigations, not a one-time sign-off. The MIT AI Risk Repository rounds this out with a living catalogue of risks and incident evidence that teams can draw from instead of starting a taxonomy from a blank page.
A step-by-step AI risk assessment process for teams
A repeatable sequence turns framework guidance into work product. Each step below produces an artifact that feeds directly into the risk register.
- Scope and inventory: list every AI system, its components, data flows, and the human actors who operate or are affected by it.
- Map impacts and stakeholders: run an initial hazard scan using an aspect-oriented taxonomy to catch risks across technical, operational, and societal dimensions.
- Assess likelihood and impact: score each identified risk on ordinal scales and document the evidence or reasoning behind each score.
- Record mitigation status: note whether a control is in place or only planned, assign a named owner, and set a review cadence with a defined escalation trigger.
- Validate with evidence: use monitoring data, user feedback, or an independent audit to confirm that the Measure and Manage functions are grounded in real system behavior rather than assumption.
The sequence is cyclical. A system re-enters step 1 whenever its scope changes, a new data source is connected, or an incident reveals a gap the original scan missed.
Pro Tip: Timebox the first pass to two weeks per system; a slow, perfect inventory delays protection longer than a fast, imperfect one that gets revised.
How to build and operate a living AI risk register
A risk register only earns the word “living” when its fields force accountability rather than just description. The CASRAI practical template recommends a specific set of fields that keep entries auditable over time.
- Risk ID and description: a short identifier plus a description that names the specific mechanism, not a vague category label.
- Category, likelihood, impact: ordinal scores recorded separately so reviewers can see which dimension is driving priority.
- Owner: a named individual, never a team or department.
- Mitigation status: “in place” or “planned,” each with a target date for planned controls.
- Review cadence and escalation trigger: a scheduled recheck date plus the condition that forces an earlier review.
- Status: open, mitigated, accepted, or closed, updated at every review.
Naming an individual owner matters because accountability friction is one of the main reasons registers go stale, according to CASRAI’s guidance. Separating “in place” from “planned” mitigations prevents a register from implying protection that has not actually been built yet.
Smaller organizations often run one shared register across all AI systems; larger ones split by system or business unit once row counts make a shared sheet unwieldy. Either way, the register works best when it plugs into existing enterprise risk management processes rather than living as a separate compliance artifact nobody reopens.
Measuring and prioritizing risks: scales, methods, and cadence
Ordinal scales, such as low, medium, high, or a 1 to 5 range, produce more defensible prioritization than percentage-based scores when the underlying data does not support that precision. CASRAI’s practitioner guidance warns specifically against false percentage precision for AI risk scoring.
- Combine likelihood and impact using a simple matrix or qualitative multiplication, and write down the assumptions behind each score.
- Reserve probabilistic or scenario-based methods for systems where failure could cascade across the organization.
- Set a fixed review date for every risk, then add event-driven triggers for conditions like model updates or new regulatory guidance.
For systemic or high-stakes risks, adapt Probabilistic Risk Assessment (PRA) methods from high-reliability industries. PRA-style pathway modeling surfaces cascade effects that simple likelihood-times-impact scoring tends to miss, since it traces how one failure propagates into downstream harms rather than scoring each hazard in isolation.
High-value templates and repositories to reuse
Several public resources let teams skip the work of building a taxonomy from scratch.
- Start from the MIT AI Risk Repository for a living catalogue of risks and incident evidence mapped to multiple frameworks.
- Pull canonical risk categories and standards crosswalks from the Deployer AI Risk Register instead of inventing your own labels.
- Use the NIST AI RMF Playbook for suggested actions already mapped to the Govern, Map, Measure, and Manage functions.
- Adopt the PRA workbook once a system is high-stakes enough to justify probabilistic modeling.
- When selecting supporting tools, prioritize ones that capture audit evidence, support continuous monitoring, and integrate with existing ticketing or CRM systems so findings route to the people who act on them.
Practitioner example: where independent audits and continuous monitoring plug into assessments
Assessments stay credible only when the Measure and Manage functions are backed by evidence from real system behavior, not self-reported confidence.
- Audit findings map directly to register entries, turning a generic “escalation risk” row into a specific, evidenced one with a remediation recommendation attached.
- Continuous monitoring keeps the register current between formal review cycles and flags conditions that should trigger an early reassessment.
- Combining periodic audits with ongoing monitoring gives a documented trail that compliance teams can point to when a regulator asks how a risk was measured and managed.
Types of AI risks specific to conversational AI
Conversational agents carry a distinct risk profile compared to batch or embedded AI systems, because every interaction is a live, two-way exchange with a person who may be frustrated, vulnerable, or asking something the system was never designed to handle.
Bias in dialogue shows up when an agent responds differently based on a customer’s phrasing, name, or apparent demographic, often in ways that are invisible until someone reviews a large sample of transcripts side by side. Failure to escalate is one of the most consequential categories because it compounds quietly: a customer in distress or with a complex issue gets looped through scripted responses instead of being routed to a person, and the failure often goes unnoticed until it shows up in a churn or complaint spike. Privacy breaches occur when an agent surfaces or retains personal data inappropriately, whether by repeating sensitive details back in a transcript, logging information beyond what a privacy policy permits, or failing to confirm identity before disclosing account details.
Other conversational-specific risks include inconsistent answers across channels, agents that fabricate information with confident phrasing, and systems that cannot recognize when a conversation has moved outside their intended scope. Each of these risk types belongs in the inventory and mapping steps described earlier, scored on the same likelihood and impact scales used for every other system.
Techniques for auditing and verifying AI behavior in real customer conversations
Verifying what an AI agent actually does, rather than what its documentation claims it does, requires looking at real conversations rather than test scripts alone.
Transcript sampling across a representative mix of customer intents, sentiment levels, and channels catches behaviors that scripted test cases miss, since real customers phrase requests in ways no test suite anticipates. Scenario-based probing, where auditors deliberately introduce edge cases such as a distressed customer or a request outside the agent’s intended scope, tests whether escalation and safety behaviors trigger as designed. Reviewing full conversation outcomes, not just the agent’s final response, reveals whether an issue was actually resolved or merely closed.
Independent, third-party review adds a layer that internal testing structurally cannot: an auditor with no stake in the agent’s deployment decision has less incentive to interpret ambiguous behavior favorably. This is part of why combining independent audits with continuous monitoring produces more reliable evidence for the Measure function than either approach alone, since audits provide depth on a sample while monitoring provides breadth across every conversation.
Verification should also check disclosure: whether the agent identifies itself as AI when required, whether it discloses the limits of what it can help with, and whether it hands off cleanly when a human should take over. These checks map directly back to the risk register, turning an abstract “compliance risk” entry into a specific, testable item with a pass or fail outcome attached.
Integration of AI risk assessment outcomes with customer experience metrics
A risk register that lives apart from customer experience data misses half the picture. Escalation failures, bias incidents, and privacy near-misses rarely show up as a dedicated flag in a CX dashboard. They show up as a drop in satisfaction scores, a spike in repeat contacts, or a quiet rise in churn among customers who had a bad AI interaction and never said why.
Connecting risk assessment outcomes to CX metrics means treating a sudden shift in sentiment, Net Promoter Score, or repeat-contact rate as a potential signal that a risk has materialized, not just a CX problem to route to a different team. A register entry for “escalation failure” becomes far more useful when it is tied to the specific sentiment or churn pattern that would indicate it is happening at scale, rather than sitting as a standalone compliance item.
This integration also works in reverse: CX teams reviewing conversation data often surface new risks before a formal audit would. A pattern of confused or frustrated responses to a particular type of request is both a customer experience problem and a candidate new row for the risk register. Building a feedback loop between CX reporting and risk register updates keeps both functions working from the same evidence instead of maintaining separate, conflicting pictures of how the AI system is actually performing.
Continuous monitoring and updating risk assessments in dynamic AI environments
AI systems change faster than most review cadences assume. A model update, a new data source, a prompt change, or a shift in customer behavior can all alter a system’s risk profile between scheduled review dates.
Continuous monitoring closes that gap by tracking system behavior on an ongoing basis rather than only at review checkpoints, and it is what makes event-driven triggers meaningful instead of theoretical. Without monitoring, an “event-driven trigger” field in the register is just an assumption; with it, the register actually fires a reassessment when the relevant condition occurs.
Practical monitoring signals worth tracking include shifts in escalation rates, changes in the types of requests an agent handles, drift in response patterns after a model or prompt update, and any spike in complaints tied to a specific interaction type. Each of these should map to a specific risk register row so that a monitoring alert has a clear destination rather than generating noise that no one owns.
Updating the register itself should follow the same discipline as creating it: a changed likelihood or impact score needs a documented reason, a newly discovered risk needs the full field set filled in rather than a quick note, and a closed risk needs evidence of why it is considered resolved. Treating updates with the same rigor as the initial assessment is what keeps a register from degrading into a document nobody trusts.
Methods for interpreting customer feedback and actionable insights from AI interactions
Customer feedback on AI interactions arrives in far more forms than a post-chat survey score, and most of the useful signal sits in the conversations themselves rather than in the rating a customer bothers to leave afterward.
Sentiment analysis across full transcripts, not just the final exchange, reveals where frustration builds even when a customer ultimately rates the interaction positively out of politeness or fatigue. Urgency scoring helps separate routine questions from conversations that needed faster escalation than they received. Looking at what customers repeat or rephrase, rather than just what they explicitly complain about, often surfaces where an agent’s response genuinely missed the point even though no formal complaint followed.
Turning this feedback into action requires routing it to the team that can actually fix the underlying cause. A pattern of confusion around a specific product feature belongs with Product. A recurring escalation failure belongs with the team that owns the conversational agent’s configuration. A spike in requests an agent cannot handle at all is a signal for Sales or Marketing about an unmet customer need, not just a technical gap.
The common failure mode is treating feedback analysis as a reporting exercise rather than a routing exercise. Insights that stay in a dashboard nobody checks regularly produce no change in the AI system or the risk register. Insights tied to a specific owner, a specific register row, or a specific team’s backlog are the ones that actually reduce the risks they describe.
Make assessments habitual, not a one-off compliance checkbox
The biggest gap we see is treating an AI risk assessment as a document to finish rather than a habit to maintain. A register with named owners, specific escalation triggers, and scheduled review gates only works if upkeep is built into someone’s actual job description and shows up in executive reporting, not left as a side project. Prioritize the risks that matter against the mitigation resources actually available, and revisit that priority list as often as the systems themselves change.
— Sergio Llorens
How Lexic helps you operationalize AI risk assessments
Building a risk register is only as good as the evidence behind it, and most teams lack a reliable way to verify what their conversational AI agents actually do in live customer interactions. We built Lexic Compass to audit conversational agents from any vendor, surfacing escalation failures, security gaps, and disclosure issues that feed directly into your register’s Measure and Manage entries with evidence a regulator can review.
- An Audit Sprint gives you a fast, scoped audit of a specific agent when you need evidence for one high-priority register row.
- Enterprise Continuous Trust extends that assurance into ongoing monitoring, keeping your register current between formal reviews.
- Lexic Pulse adds continuous analysis of customer conversations across channels, turning feedback into the sentiment and escalation signals your register needs.
For teams also evaluating new AI vendors as part of their risk mapping, a structured procurement comparison like Compare 5 Quotes can help surface third-party risk earlier in the selection process.
If your conversational AI has never been independently audited, that is the fastest first step toward a register built on evidence instead of assumption. Start with an Audit Sprint on your highest-traffic agent.
FAQ
What are the four types of AI risk?
Common frameworks group AI risks into categories such as safety and reliability, security, fairness and bias, and compliance or legal risk, though exact groupings vary by source. The MIT AI Risk Repository catalogues risks across these and additional dimensions rather than fixing them at exactly four.
How do I conduct an AI risk assessment?
Start by inventorying every AI system and its data flows, then map impacts and stakeholders using a hazard scan, score each risk on likelihood and impact, and record mitigations with a named owner in a living risk register. Validate the assessment with monitoring data or an independent audit, and revisit it on a fixed cadence plus whenever an event-driven trigger occurs.
Can AI write a risk assessment?
AI tools can help draft risk descriptions, suggest categories from a known taxonomy, or summarize monitoring data, but the scoring, ownership assignment, and validation steps need human judgment and accountability. A register populated without named owners and documented evidence tends to go stale quickly, regardless of how it was drafted.
What is the 30% rule for AI?
Readers encountering this term elsewhere should treat it as informal shorthand rather than a recognized standard, and rely on frameworks like NIST’s AI RMF for documented guidance instead.
What should a conversational AI risk assessment check for specifically?
A conversational AI risk assessment should check escalation behavior, disclosure of AI identity, privacy handling in transcripts, and consistency of responses across channels. Independent audits, such as those run through Lexic Compass, provide evidence on these specific points rather than relying on vendor self-reporting alone.