1

AI Risk Management Framework Concept Paper

13 December 2021 

NOTE TO REVIEWERS

This Artificial Intelligence Risk Management Framework (AI RMF) concept paper incorporates input from 

the Notice of Request for Information (RFI) released by the National Institute of Standards and Technology

(NIST) on July 29, 20211, and discussions during the workshop, “Kicking off NIST AI Risk Management 

Framework,” held October 19-21, 2021.2

Feedback on this paper will inform further development of this approach and the first draft of the AI RMF 

for public comment. NIST intends to publish that initial draft for public comment in early 2022, as well as to 

hold additional workshops, with the goal of releasing the AI RMF 1.0 in early 2023.

NIST would appreciate feedback on whether the concept proposed here is a constructive approach for the 

AI RMF. While additional details will be provided in the more extensive discussion drafts of the framework, 

NIST welcomes suggestions now about this approach as well as details and specific topics reviewers would 

like to see included in the upcoming first draft of the AI RMF. Specifically, NIST requests input on the 

following questions:

− Is the approach described in this concept paper generally on the right track for the eventual AI 

RMF?

− Are the scope and audience (users) of the AI RMF described appropriately?

− Are AI risks framed appropriately?

− Will the structure – consisting of Core (with functions, categories, and subcategories), Profiles, and 

Tiers – enable users to appropriately manage AI risks? 

− Will the proposed functions enable users to appropriately manage AI risks?

− What, if anything, is missing?

Please send feedback on this paper to AIframework@nist.gov by January 25, 2022.

1 https://www.nist.gov/itl/ai-risk-management-framework/ai-rmf-development-request-information

2 https://www.nist.gov/news-events/events/2021/10/kicking-nist-ai-risk-management-framework
2

1 AI Risk Management Framework Concept Paper

2 1 Overview

3 This concept paper describes the fundamental approach proposed for the National Institute of Standards and 

4 Technology (NIST) Artificial Intelligence Risk Management Framework (AI RMF or framework). The AI RMF is 

5 intended for voluntary use and to address risks in the design, development, use, and evaluation of AI products, 

6 services, and systems.

7 AI technologies extend algorithmic methods and automation into new domains and roles, including advising 

8 people and taking actions in high-stakes decisions. The goal of managing the risks associated with the use of AI 

9 technologies for such tasks as recommendation, diagnosis, pattern recognition, and automated planning and 

10 decision-making frames opportunities for developing and using AI in ways that will increase their 

11 trustworthiness and advance their usefulness, while addressing potential harms. 

12 AI risk management follows similar processes as other disciplines. Nevertheless, managing AI risks presents 

13 unique challenges. An example is the evaluation of effects from AI systems that are characterized as being 

14 long-term, low probability, systemic, and high impact. Tackling scenarios that can represent costly outcomes or 

15 catastrophic risks to society should consider: an emphasis on managing the aggregate risks from low 

16 probability, high consequence effects of AI systems, and the need to ensure the alignment of ever more 

17 powerful advanced AI systems. This proposed AI RMF is an initial attempt to describe how AI risks differ from 

18 other domains, and to offer directions – leveraging a multi-stakeholder approach – for creating and 

19 maintaining actionable guidance that is broadly adoptable.

20 AI risk management entails challenges that reflect both the breadth of AI use cases and the quickly evolving

21 nature of the field. The AI ecosystem is home to a multitude of diverse stakeholders, including developers, 

22 users, deployers, and evaluators. For organizations, identifying, assessing, prioritizing, responding to, and 

23 communicating risks across business and social relationships at scale can be a difficult task. These challenges 

24 are amplified by the heterogeneity of AI risks, whether in degree or in kind, which potentially include harmful 

25 bias, threats to safety, privacy, and consumer protection, among others.

26 A voluntary consensus-driven framework can help create and safeguard trust at the heart of AI-driven systems 

27 and business models and permit the flexibility for innovation, allowing the framework to develop along with

28 the technology.

29 NIST’s work on the framework is consistent with its broader AI efforts – as called for by the National AI 

30 Initiative Act of 2020, the National Security Commission on Artificial Intelligence recommendations, and 

31 the Plan for Federal Engagement in AI Standards and Related Tools – for NIST to collaborate with the private 

32 and public sectors to develop the AI RMF.

33 2 Scope and Audience 

34 The NIST AI RMF is intended to serve as a blueprint for mapping, measuring, and managing risks related to AI

35 systems across a wide spectrum of types, applications, and maturity. This resource will be organized and 

36 written in such a way that it can be understood and used by the greatest number of individuals and 

37 organizations, regardless of sector or level of familiarity with a specific type of technology. Ultimately, it will be 

38 offered in multiple formats, including interactive versions, to provide maximum flexibility by users.

39 The intended primary audiences are:

40 (1) people who are responsible for designing or developing AI systems;

41 (2) people who are responsible for using or deploying AI systems; and

42 (3) people who are responsible for evaluating or governing of AI systems.
3

1 A fourth audience who will serve as a key motivating factor in this guidance is

2 (4) people who experience potential harm or inequities affected by areas of risk that are newly 

3 introduced or amplified by AI systems.

4 All stakeholders should be involved in the risk management process. Stakeholders may include the 

5 mission/task champion (leadership), program management, system engineer, AI developer, requirements 

6 representative, test and evaluation personnel, end user, and affected communities, depending on the 

7 application.

8 3 Framing Risk

9 Within the context of the AI RMF, “risk” refers to the composite measure of an event’s probability of occurring 

10 and the consequences of the corresponding events. While some interpretations of consequence focus 

11 exclusively on negative impacts (what is the likelihood that something bad will happen?), NIST intends to use a

12 broader definition that offers a more comprehensive view of potential influences, including those that are 

13 positive, resonating with the goals of developing and applying AI technologies to achieve positive outcomes. If 

14 handled appropriately, AI technologies hold great potential to uplift and empower people and to lead to new 

15 services, support, and efficiencies for people and society. Identifying and minimizing potential costs associated 

16 with AI technologies will advance those possibilities.

17 AI risk management is as much about offering a path to minimize anticipated negative impacts of AI systems, 

18 such as threats to civil liberties and rights, as it is about identifying opportunities to maximize positive impacts.

19 The AI RMF endeavors to lay the groundwork for a common understanding of roles and responsibilities across 

20 the AI lifecycle. It is agnostic as to whether duties are dispersed and governed throughout an organization or 

21 assumed by an individual without any organizational affiliation. By focusing on measurable criteria that 

22 indicate AI system trustworthiness in meaningful, actionable, and testable ways, this framework concept paper 

23 lays out the components of an effective AI risk management program. The framework is designed to be readily 

24 useful to and usable by those with varied roles and responsibilities throughout the AI lifecycle.

25 4 Attributes of AI RMF

26 NIST has developed a set of attributes based on public feedback from a recent request for information and 

27 workshop. NIST encourages comments as to whether the framework envisioned in this concept paper, as well 

28 as all future documents released during the initial development of the AI RMF, successfully meets such 

29 expectations. The following constitutes NIST’s list of framework attributes:

30 1. Be consensus-driven and developed and regularly updated through an open, transparent process. All 

31 stakeholders should have the opportunity to contribute to and comment on the AI RMF development. 

32 2. Be clear. Use plain language that is understandable by a broad audience, including senior executives, 

33 government officials, NGO leadership, and, more broadly, those who are not AI professionals, while 

34 still of sufficient technical depth to be useful to practitioners. The AI RMF should allow for 

35 communication of AI risks across an organization, with customers, and the public at large.

36 3. Provide common language and understanding to manage AI risks. The AI RMF should provide 

37 taxonomy, terminology, definitions, metrics, and characterizations for aspects of AI risk that are 

38 common and relevant across sectors. 

39 4. Be easily usable. Enable organizations to manage AI risk through desired actions and outcomes. Be 

40 readily adaptable as part of an organization’s broader risk management strategy and processes.

41 5. Be appropriate for both technology agnostic (horizontal) as well as context-specific (vertical) use cases 

42 to be useful to a wide range of perspectives, sectors, and technology domains.
4

1 6. Be risk-based, outcome-focused, cost-effective, voluntary, and non-prescriptive. It should provide a 

2 catalog of outcomes and approaches to be used voluntarily, rather than a set of one-size-fits-all 

3 requirements. 

4 7. Be consistent or aligned with other approaches to managing AI risks. The AI RMF should, when 

5 possible, take advantage of and foster greater awareness of existing standards, guidelines, best 

6 practices, methodologies, and tools for managing AI risks as well as illustrate the need for additional, 

7 improved resources. It should be law- and regulation-agnostic to support organizations' abilities to 

8 operate under applicable domestic and international legal or regulatory regimes.

9 8. Be a living document. The AI RMF should be capable of being readily updated as technology, 

10 understanding, and approaches to AI trustworthiness and uses of AI change and as stakeholders learn 

11 from implementing AI risk management generally and this framework, in particular. 

12 5 AI RMF Structure

13 The proposed structure for the AI RMF is composed of three components: 1) Core, 2) Profiles, and 3) 

14 Implementation Tiers. This structure, and intended definitions for Core, Profiles and Implementation Tiers, are 

15 similar to the structure used in the NIST Framework for Improving Critical Infrastructure Cybersecurity 

(Cybersecurity Framework)3 and the NIST Privacy Framework.

4

16

17 5.1 Core

18 The Core provides a granular set of activities and outcomes that enable an organizational dialogue about 

19 managing AI risk. As with the NIST Cybersecurity and Privacy Frameworks, the core is not a checklist of actions 

20 to perform. Rather, it defines key outcomes identified as helpful in addressing AI risks. The Core will comprise 

21 three elements: functions, categories, and subcategories. Figure 1 illustrates the proposed structure of the 

22 Core. Table 1 provides examples to help clarify the intent of these functions.

23 5.1.1 Functions

24 Functions organize AI risk management activities at their highest level to establish the context and enumerate, 

25 assess, treat, monitor, review, and report risk. Categories are the subdivisions of a function into groups of 

26 outcomes closely tied to programmatic needs and particular activities. Subcategories further divide a category 

27 into specific outcomes of technical and/or management activities.

28 NIST proposes the following functions:

29 1. Map: Context is established, and risks related to the context are enumerated.

The purpose of this function is “to find, recognize, and describe risks”

5

30 posed by an AI system. The baseline 

31 information gathered as part of this function informs decisions about model management, including the 

32 decisions that an AI solution is unwarranted or inappropriate versus the status quo, per a qualitative or 

33 more formal quantitative analysis of benefits, costs, and risks, and to stop development or to refrain from 

34 deployment.

35 NOTE 1: Context refers to the domain and intended use, as well as the scope of the system, which could be 

36 associated with a timeframe, a geographical area, social environment, and cultural norms within which the 

37 expected benefits or harms exist, specific sets of users along with expectation of users, and any other 

38 system or environmental specifications.

3 https://www.nist.gov/cyberframework

4 https://www.nist.gov/privacy-framework

5

See Clause 6.4.2 Risk identification at ISO 31000 2018 Risk management — Guidelines www.iso.org/obp/ui#iso:std:iso:31000:ed2:v1:en
5

1 NOTE 2: Enumeration of risks refers to understanding risks posed by the AI system to individuals, groups, 

2 organizations, and society – including but not limited to risk associated with the data, model, the way in 

3 which the AI system is used, and its impact on individuals, groups, and society.

4 NOTE 3: This function should be performed by a team who is sufficiently diverse and multidisciplinary, 

5 representing multiple departments of the organization, and ideally includes a sufficiently diverse set of 

6 stakeholders from outside the organization.

7 2. Measure: Enumerated risks are analyzed, quantified, or tracked where possible.

8 The purpose of this function is to comprehend the nature of risk or impact and its characteristics and to 

9 facilitate the management of such risk as set forth below.

10 This function forms the basis for determining how the enumerated risk should be managed.

11 NOTE 4: Risk analysis and quantification may include the level of risk and involve a detailed consideration 

12 of uncertainties, tradeoffs, consequences, likelihood, events, scenarios, controls, and their effectiveness. 

An event can have multiple causes and consequences and can affect multiple objectives.613

14 3. Manage: Risks are prioritized, and either avoided, mitigated, shared, transferred, or accepted based on 

15 measured severity.

16 The purpose of this function is to support decisions and to select and implement options for addressing 

17 risk. Decisions should take account of the context and the actual and perceived consequences to external 

18 and internal stakeholders, as well as interactions of the proposed system with the status quo world, and 

19 potential transitional costs that may be addressed in advance of deployment or changes in status quo 

20 (including other systems, organizational structures, etc.) that may need to be made to ensure benefits are 

21 achieved and risks minimized.

22 NOTE 5: This function should address the enumerated risks but also include detecting, monitoring, and 

23 tracking for risks that were not enumerated initially and process for introducing them into the updated 

24 enumerated list of risks.

25 4. Govern: Appropriate organizational measures, set of policies, processes, and operating procedures, and 

26 specification of roles and responsibilities are in place. 

27 The purpose of this function is to cultivate and implement a culture of risk management and to help 

28 ensure the risk responses are effectively and consistently carried out.

29 NOTE 6: Governance should be part of each function and a function of its own, reflecting the importance 

30 of infusing governance considerations throughout risk management processes and procedures. While 

31 governance could therefore be listed as the first function, it is listed after the first three to emphasize its 

32 importance as a continual requirement for effective AI risk management over AI system lifespan. Effective 

33 risk management cannot occur where governance is robust only in the early stages of an AI system, and 

34 not as the AI system evolves or is updated over time.

35 5.1.2 Categories

36 Categories are subdivisions of a function into groups of outcomes closely tied to programmatic needs and 

37 particular activities. See Table 1 for possible examples of categories for each of the proposed functions to 

38 better explain the intent of each function. Table 1 as presented here is not intended to be exhaustive. (NIST 

39 especially invites comments on potential categories that should be included in the AI RMF.)

40 5.1.3 Subcategories

41 Subcategories further divide a Category into specific outcomes of technical and/or management activities.

6 See Clause 6.4.3 Risk analysis at ISO 31000 2018 Risk management — Guidelines www.iso.org/obp/ui#iso:std:iso:31000:ed-2:v1:en
6

1 5.2 Profiles

2 Profiles enable users to prioritize AI-related activities and outcomes that best meet an organization’s values, 

3 mission, or business needs and risks. Profiles can be technical and non-technical guidance for managing AI risks 

4 for context-specific use cases. They may illustrate how risk could be managed at various stages of the AI life 

5 cycle or in sector, technology, or end use applications.

6 5.3 Implementation Tiers

7 Implementation Tiers support decision-making and communication about the sufficiency of organizational 

8 processes and resources, including engineering tools and infrastructure and engineers with appropriate AI 

9 expertise, to manage AI risks deemed appropriate for the organization or situation and achieve outcomes and 

10 activities in the Profile(s).

11

12 Figure 1 Risk management should be performed throughout the AI system life cycle to ensure risk management is 

13 continuous and timely. Governance is suggested as a function of its own and to be infused throughout other 

14 functions of the risk management process. Example activities for each stage of the AI life cycle follow. Pre-design: 

15 data collection, curation, or selection, problem formulation, and stakeholder discussions. Design and 

16 development: data analysis, data cleaning, model training, and requirement analysis. Test and Evaluation: 

17 technical validation and verification. Deployment: user feedback and override, post deployment monitoring, and 

18 decommissioning.

19

20
7

1 Table 1. Possible example of categories for each of the proposed functions.

ID Category Sub-category

Map: Context is recognized, and risks related to the context are enumerated.

1 Context is established and understood.

2 AI capabilities, targeted usage, goals, and expected benefits over status 

quo are understood.

3 Technical, socio-technical risks7 and harms from individual, 

organizational, and societal perspectives are enumerated.

Measure: Enumerated risks are analyzed, quantified, or tracked where possible.

Methods and metrics for quantitative or qualitative measurement of the 

enumerated risks, including sensitivity, specificity, and confidence levels

for specific inferences are identified and applied to the enumerated 

risks.

The likelihood of events and their consequences are assessed.

The effectiveness of existing security controls is evaluated.

Manage: Enumerated risks are prioritized, mitigated, shared, transferred, or accepted based on measured 

severity.

Cost/benefit analysis (including the cost of not using AI or an assessment 

of whether an AI system should be developed or deployed in the first 

place) is performed.

Appropriate responses to enumerated and measured risks are 

identified, prepared, and implemented.

Govern: Appropriate organizational measures, set of policies, processes, and operating procedures, and 

specification of roles and responsibilities are in place.

The resources – including engineering tools and infrastructure and 

engineers with appropriate AI expertise required for risk management, 

including contingencies – are identified.

Ongoing monitoring and periodic review of the risk management 

process and its outcomes are planned, with responsibilities clearly 

defined.

The risk management process and its outcomes are documented and 

reported through transparent mechanisms as appropriate.

7 For a discussion of AI technical and socio-technical risks see Draft-AI Risk Taxonomy

www.nist.gov/system/files/documents/2021/10/15/taxonomy_AI_risks.pdf
8

Decision making throughout the AI lifecycle is informed by a 

demographically and disciplinarily diverse team.

1

2 6 Effectiveness of the AI RMF

3 Organizations are encouraged to periodically evaluate AI RMF performance against their purpose, 

4 implementation plans, indicators, and expected behavior. Sharing feedback about the AI RMF’s effectiveness 

5 with NIST and others will promote continually improvement of the suitability, adequacy, and effectiveness of 

the AI RMF86 .

8

See Clause 5.6 Evaluation and 5.7 Improvement at ISO 31000 2018 Risk management — Guidelines

www.iso.org/obp/ui#iso:std:iso:31000:ed-2:v1:en