Category: Risk Assessment & Analysis

Risk Identification

Also known as: Risk Recognition

Simply put

Risk identification is the process of finding and documenting potential threats that could affect an organization's objectives. It is typically the first step in broader risk management activities, done before risks are measured or addressed. The goal at this stage is to recognize and record what could go wrong, not yet to decide how serious each risk is or how to respond.

Formal definition

Risk identification is the systematic, typically initial phase of a risk assessment or risk management process in which an organization recognizes and documents potential sources of risk, risk events, and their possible impacts on objectives. As commonly defined, it precedes and is distinct from later stages such as risk analysis, measurement, evaluation, and treatment, though some frameworks also treat opportunities alongside threats within its scope. In many frameworks it employs structured methods and produces documented outputs (for example, a risk register or inventory) that feed downstream assessment activities. Note that the exact placement, terminology, and scope of risk identification vary by framework and sector, and the evidence available here describes the general concept rather than any single authoritative or AI-specific definition; its application within AI governance or model risk management would draw on domain-specific guidance not contained in these sources.

Why it matters

Risk identification matters because it establishes the foundation on which all subsequent risk management activity depends. If a potential threat is never recognized or documented, it cannot be measured, evaluated, or treated later in the process. As commonly framed, it is the first step in a broader risk management workflow, and gaps at this stage tend to propagate downstream: an unrecorded risk becomes an unmanaged risk. This is why many frameworks treat systematic identification, rather than ad hoc awareness, as the goal.

Because its function is recognition and documentation rather than judgment, risk identification deliberately holds off on deciding how serious a given risk is or how to respond to it. Professionals frequently err by collapsing identification into analysis, prioritizing or dismissing risks before they have been fully catalogued. Doing so can cause an organization to under-record threats that appear minor at first glance but prove material once measured. Keeping identification distinct from later analysis and evaluation preserves a complete inventory for downstream assessment.

The scope, terminology, and placement of risk identification vary by framework and sector, and some frameworks also bring opportunities, not only threats, within its scope. The sources available here describe the general concept rather than any single authoritative or AI-specific definition. Applying risk identification within AI governance or model risk management would draw on domain-specific guidance not contained in these sources, so practitioners should map the general process to the requirements of whatever framework governs their context.

Who it's relevant to

Risk and Compliance Officers

Those responsible for risk management processes rely on risk identification as the systematic first step that populates the risk register or inventory. A complete, well-documented identification stage is what allows later measurement and treatment activities to operate on a full picture rather than a partial one.

Model Risk Managers

The general concept of risk identification underlies risk work in model risk management, though its application in that domain would draw on domain-specific guidance not contained in the sources here. Practitioners should map the general recognize-and-document process to the specific model risk framework and terminology that governs their institution.

AI Governance Specialists

Those building oversight structures for AI systems can treat risk identification as the foundational recognition step that precedes measurement and response. Because the sources describe the general concept rather than an AI-specific definition, governance teams should align identification practices with whichever AI-specific guidance or framework applies to their context.

Auditors and Assurance Professionals

Reviewers assessing a risk management process can examine whether identification is systematic and documented, and whether it has been kept distinct from later analysis and evaluation. Confirming completeness of the identified-risk inventory is central to evaluating whether downstream assessment rests on sound inputs.

Project and Delivery Managers

In project settings, risk identification is how teams find and document possible issues before they affect delivery. Recording potential problems early, without prematurely prioritizing them, keeps the full set of threats available for subsequent handling.

Inside Risk Identification

Risk taxonomy

A structured categorization of the types of risk an AI system or model may present, such as data risks, model design risks, operational and deployment risks, and downstream impact risks. Taxonomies help ensure identification is systematic rather than ad hoc, though the specific categories used typically vary by organization, sector, and applicable framework.

Inherent risk assessment

The identification of risk before controls or mitigations are applied. Distinguishing inherent risk from residual risk (the risk remaining after controls) at the identification stage helps clarify where controls are most needed. Risk identification typically surfaces inherent risk; the effect of controls is generally assessed later in the risk management process.

Scope and use-case definition

A clear statement of the model's or system's intended purpose, boundaries, users, and context of deployment. Because risks are often context-dependent, identification typically depends on a well-defined scope; a model considered low risk in one use may present materially different risks in another.

Sources of model risk

In many model risk management frameworks, model risk is commonly framed as arising from fundamental errors in a model or from incorrect or inappropriate use. Identification aims to capture both dimensions rather than focusing only on technical defects.

Stakeholder and lifecycle coverage

Input from across the model or AI lifecycle (design, data, development, validation, deployment, monitoring, and retirement) and from relevant roles such as developers, business owners, and independent reviewers. This helps reduce blind spots, though the degree of independence and the specific roles involved differ across organizations and frameworks.

Documentation of identified risks

A recorded inventory of identified risks, typically capturing the nature of each risk, its potential sources, and the affected use or system. Documentation supports later measurement, monitoring, and oversight, and provides an auditable record; formats and required contents vary by organization and applicable guidance.

Common questions

Answers to the questions practitioners most commonly ask about Risk Identification.

Is risk identification the same as risk assessment or risk measurement?

No. Risk identification is typically the step focused on recognizing and cataloging potential sources of risk, whereas assessment and measurement involve analyzing, quantifying, or rating those risks. In many frameworks these are treated as distinct phases: you cannot meaningfully measure a risk you have not first identified, but identifying a risk does not itself tell you its likelihood or severity. Conflating the two can cause organizations to skip the discovery step and measure only the risks they already anticipated.

Does completing risk identification mean the identified risks have been addressed or reduced?

No. Identification only surfaces and documents potential risks; it does not eliminate, control, or mitigate them. Treatment, control design, and monitoring are separate activities. A common error is treating a completed risk inventory as evidence that risks are managed, when identification is only the starting point of the broader risk management lifecycle. Governance controls generally reduce or manage risk rather than remove it.

How does risk identification typically fit into a model risk management workflow?

In many frameworks, risk identification occurs early in the model lifecycle and is revisited at key stages such as development, validation, deployment, and change events. It commonly feeds downstream activities like risk assessment, control design, and ongoing monitoring. Because model risk can emerge from data, assumptions, implementation, or use, identification is generally treated as iterative rather than a one-time exercise.

Who is generally responsible for performing risk identification?

Responsibility often spans multiple lines of defense. In many organizations the first line (those who develop and use models) is closest to operational sources of risk and contributes directly to identification, while the second line (independent risk or model risk functions) may challenge and supplement those findings. The third line (internal audit) typically evaluates whether the identification process is adequate rather than performing it. The specific allocation varies by organization and framework.

What sources or inputs are commonly used to identify model-related risks?

Common inputs can include model documentation, data lineage and quality reviews, intended-use and limitation statements, validation findings, prior incidents, stakeholder interviews, and monitoring outputs. Because risks may be technical, operational, or contextual, many practitioners draw on both structured methods and expert judgment. The appropriate mix typically depends on the model's complexity, use case, and applicable regulatory or organizational expectations.

How often should risk identification be repeated?

There is no single universally mandated cadence. In many frameworks, identification is refreshed periodically and also triggered by specific events such as material model changes, new data sources, changes in use, or emerging performance issues. The suitable frequency generally scales with the risk profile of the model and any applicable governance or regulatory expectations, which vary by sector and jurisdiction.

Common misconceptions

Risk identification is the same as risk measurement or scoring.

Identification is generally the step of recognizing and naming the risks that exist, distinct from measurement, which estimates likelihood, magnitude, or severity. Treating them as one step can lead practitioners to prematurely discard risks that are hard to quantify but nonetheless real. The two are typically sequential and complementary within a broader risk management process.

Identifying risks means they have been addressed or reduced.

Identification surfaces risk but does not by itself control, mitigate, or eliminate it. Governance and control activities are measures that reduce or manage risk after it has been identified; identification alone leaves risk at its inherent level. No governance control should be described as eliminating risk.

Risk identification is a one-time activity completed at model approval.

In many frameworks, risk identification is treated as ongoing, because model behavior, data, and deployment context can change over time. A risk profile established at approval may become outdated, so identification is commonly revisited across the lifecycle rather than fixed at a single point.

Best practices

Define the model's or AI system's intended use, scope, and deployment context before identifying risks, since many risks are context-dependent and shift with the use case.

Use a structured risk taxonomy or checklist to make identification systematic, while remaining open to novel or emerging risks that predefined categories may not capture.

Identify risks arising both from model errors and from inappropriate or unintended use, rather than limiting attention to technical defects.

Distinguish inherent risk identified at this stage from residual risk assessed after controls, and record which one you are capturing to avoid conflating the two.

Draw on multiple perspectives across the lifecycle and, where the framework or organization calls for it, from parties independent of the development team to reduce blind spots.

Document identified risks in an auditable inventory and revisit identification periodically, treating it as an ongoing activity rather than a one-time approval gate.