Privacy Framework v1.0

Version:

1.0

Publication Date:

January 16, 2020

Privacy Framework Introduction

For more than two decades, the Internet and associated information technologies have driven unprecedented innovation, economic value, and access to social services. Many of these benefits are fueled by data about individuals that flow through a complex ecosystem. As a result, individuals may not be able to understand the potential consequences for their privacy as they interact with systems, products, and services. Organizations may not fully realize the consequences either. Failure to manage privacy risks can have direct adverse consequences at both the individual and societal levels, with follow-on effects on organizations’ brands, bottom lines, and future prospects for growth. Finding ways to continue to derive benefits from data processing while simultaneously protecting individuals’ privacy is challenging, and not well-suited to one-size-fits-all solutions.

Privacy is challenging because not only is it an all-encompassing concept that helps to safeguard important values such as human autonomy and dignity, but also the means for achieving it can vary. For example, privacy can be achieved through seclusion, limiting observation, or individuals’ control of facets of their identities (e.g., body, data, reputation). Moreover, human autonomy and dignity are not fixed, quantifiable constructs; they are filtered through cultural diversity and individual differences. This broad and shifting nature of privacy makes it difficult to communicate clearly about privacy risks within and between organizations and with individuals. What has been missing is a common language and practical tool that is flexible enough to address diverse privacy needs.

This voluntary NIST Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management (Privacy Framework) is intended to be widely usable by organizations of all sizes and agnostic to any particular technology, sector, law, or jurisdiction. Using a common approach-adaptable to any organization’s role(s) in the data processing ecosystem-the Privacy Framework’s purpose is to help organizations manage privacy risks by:

  • Taking privacy into account as they design and deploy systems, products, and services that affect individuals;
  • Communicating about their privacy practices; and
  • Encouraging cross-organizational workforce collaboration-for example, among executives, legal, and information technology (IT)-through the development of Profiles, selection of Tiers, and achievement of outcomes.

Overview of the Privacy Framework

As shown in Figure 1, the Privacy Framework is composed of three parts: Core, Profiles, and Implementation Tiers. Each component reinforces how organizations manage privacy risk through the connection between business or mission drivers, organizational roles and responsibilities, and privacy protection activities. As further explained in section 2:

  • The Core is a set of privacy protection activities and outcomes that allows for communicating prioritized privacy protection activities and outcomes across an organization from the executive level to the implementation/operations level. The Core is further divided into key Categories and Subcategories-which are discrete outcomes-for each Function.
  • A Profile represents an organization’s current privacy activities or desired outcomes. To develop a Profile, an organization can review all of the outcomes and activities in the Core to determine which are most important to focus on based on business or mission drivers, data processing ecosystem role(s), types of data processing, and individuals’ privacy needs. An organization can create or add Functions, Categories, and Subcategories as needed. Profiles can be used to identify opportunities for improving privacy posture by comparing a “Current” Profile (the “as is” state) with a “Target” Profile (the “to be” state). Profiles can be used to conduct self- assessments and to communicate within an organization or between organizations about how privacy risks are being managed.
  • Implementation Tiers (“Tiers”) provide a point of reference on how an organization views privacy risk and whether it has sufficient processes and resources in place to manage that risk. Tiers reflect a progression from informal, reactive responses to approaches that are agile and risk informed. When selecting Tiers, an organization should consider its Target Profile(s) and how achievement may be supported or hampered by its current risk management practices, the degree of integration of privacy risk into its enterprise risk management portfolio, its data processing ecosystem relationships, and its workforce composition and training program.

Privacy Risk Management

While some organizations have a robust grasp of privacy risk management, a common understanding of many aspects of this topic is still not widespread. To promote broader understanding, this section covers concepts and considerations that organizations may use to develop, improve, or communicate about privacy risk management. Appendix D provides additional information on key privacy risk management practices. 

Cybersecurity and Privacy Risk Management

Since its release in 2014, the Cybersecurity Framework has helped organizations to communicate and manage cybersecurity risk. While managing cybersecurity risk contributes to managing privacy risk, it is not sufficient, as privacy risks can also arise by means unrelated to cybersecurity incidents, as illustrated by Figure 2. Having a general understanding of the different origins of cybersecurity and privacy risks is important for determining the most effective solutions to address the risks.

Cybersecurity Risks

associated with cybersecurity incidents arising from loss of confidentiality, integrity, or availability cyber security- related privacy events

Privacy Risks

associated with privacy events arising from data processing

The Privacy Framework approach to privacy risk is to consider privacy events as potential problems individuals could experience arising from system, product, or service operations with data, whether in digital or non-digital form, through a complete life cycle from data collection through disposal.

The Privacy Framework describes these data operations in the singular as a data action and collectively as data processing. The problems individuals can experience as a result of data processing can be expressed in various ways, but NIST describes them as ranging from dignity-type effects such as embarrassment or stigmas to more tangible harms such as discrimination, economic loss, or physical harm.

The basis for the problems that individuals may experience can vary. As depicted in Figure 2, problems arise as an adverse effect of data processing that organizations conduct to meet their mission or business objectives. An example is the concerns that certain communities had about the installation of “smart meters” as part of the Smart Grid, a nationwide technological effort to increase energy efficiency. The ability of these meters to collect, record, and distribute highly granular information about household electrical use could provide insight into people’s behavior inside their homes. The meters were operating as intended, but the data processing could lead to people feeling surveilled.

In an increasingly connected world, some problems can arise simply from individuals’ interactions with systems, products, and services, even when the data being processed is not directly linked to identifiable individuals. For example, smart cities technologies could be used to alter or influence people’s behavior such as where or how they move through the city. Problems also can arise where there is a loss of confidentialityintegrity, or availability at some point in the data processing, such as data theft by external attackers or the unauthorized access or use of data by employees. Figure 2 shows these types of cybersecurity-related privacy events as the overlap between privacy and cybersecurity risks.

Once an organization can identify the likelihood of any given problem arising from the data processing, which the Privacy Framework refers to as a problematic data action, it can assess the impact should the problematic data action occur. This impact assessment is where privacy risk and organizational risk intersect. Individuals, whether singly or in groups (including at a societal level) experience the direct impact of problems. As a result of the problems individuals experience, an organization may experience impacts such as noncompliance costs, revenue loss arising from customer abandonment of products and services, or harm to its external brand reputation or internal culture. Organizations commonly manage these types of impacts at the enterprise risk management level; by connecting problems that individuals experience to these well-understood organizational impacts, organizations can bring privacy risk into parity with other risks they are managing in their broader portfolio and drive more informed decision- making about resource allocation to strengthen privacy programs. Figure 3 illustrates this relationship between privacy risk and organizational risk.

Privacy Risk Assessment

Privacy risk management is a cross-organizational set of processes that helps organizations to understand how their systems, products, and services may create problems for individuals and how to develop effective solutions to manage such risks. Privacy risk assessment is a sub-process for identifying and evaluating specific privacy risks. In general, privacy risk assessments produce the information that can help organizations to weigh the benefits of the data processing against the risks and to determine the appropriate response-sometimes referred to as proportionality. Organizations may choose to

prioritize and respond to privacy risk in different ways, depending on the potential impact to individuals and resulting impacts to organizations. Response approaches include:

  • Mitigating the risk (e.g., organizations may be able to apply technical and/or policy measures to the systems, products, or services that minimize the risk to an acceptable degree); 
  • Transferring or sharing the risk (e.g., contracts are a means of sharing or transferring risk to other organizations, privacy notices and consent mechanisms are a means of sharing risk with individuals); 
  • Avoiding the risk (e.g., organizations may determine that the risks outweigh the benefits, and forego or terminate the data processing); or 
  • Accepting the risk (e.g., organizations may determine that problems for individuals are minimal or unlikely to occur, therefore the benefits outweigh the risks, and it is not necessary to invest resources in mitigation). 

Privacy risk assessments are particularly important because, as noted above, privacy is a condition that safeguards multiple values. The methods for safeguarding these values may differ, and moreover, may be in tension with each other. Depending on its objectives, if an organization is trying to achieve privacy by limiting observation, this may lead to implementing measures such as distributed data architectures or privacy-enhancing cryptographic techniques that hide data even from the organization. If an organization is also trying to enable individual control, the measures could conflict. For example, if an individual requests access to data, the organization may not be able to produce the data if the data have been distributed or encrypted in ways the organization cannot access. Privacy risk assessments can help an organization understand in a given context the values to protect, the methods to employ, and how to balance implementation of different types of measures.

Lastly, privacy risk assessments help organizations distinguish between privacy risk and compliance risk. Identifying if data processing could create problems for individuals, even when an organization may be fully compliant with applicable laws or regulations, can help with ethical decision-making in system, product, and service design or deployment. Although there is no objective standard for ethical decision- making, it is grounded in the norms, values, and legal expectations in a given society. This facilitates optimizing beneficial uses of data while minimizing adverse consequences for individuals’ privacy and society as a whole, as well as avoiding losses of trust that damage organizations’ reputations, slow adoption, or cause abandonment of products and services.

See Appendix D for more information on the operational aspects of privacy risk assessment.

Document Overview

The remainder of this document contains the following sections and appendices:

  • Section 2 describes the Privacy Framework components: Core, Profiles, and Implementation Tiers.
  • Section 3 presents examples of how the Privacy Framework can be used.
  • The References section lists the references for the document.

Privacy Framework Basics

The Privacy Framework provides a common language for understanding, managing, and communicating privacy risk with internal and external stakeholders. It is adaptable to any organization’s role(s) in the data processing ecosystem. It can be used to help identify and prioritize actions for reducing privacy risk, and it is a tool for aligning policy, business, and technological approaches to managing that risk.

Core

Set forth in Appendix A, the Core provides an increasingly granular set of activities and outcomes that enable a dialogue about managing privacy risk. As depicted in Figure 4, the Core comprises Functions, Categories, and Subcategories.

The Core elements work together:

  • Functions organize foundational privacy activities at their highest level. They aid an organization in expressing its management of privacy risk by understanding and managing data processing, enabling risk management decisions, determining how to interact with individuals, and improving by learning from previous activities. They are not intended to form a serial path or lead to a static desired end state. Rather, the Functions should be performed concurrently and continuously to form or enhance an operational culture that addresses the dynamic nature of privacy risk.
  • Categories are the subdivisions of a Function into groups of privacy outcomes closely tied to programmatic needs and particular activities. 
  • Subcategories further divide a Category into specific outcomes of technical and/or management activities. They provide a set of results that, while not exhaustive, help support achievement of the outcomes in each Category. 

The five Functions, Identify-P, Govern-P, Control-P, Communicate-P, and Protect-P, defined below, can be used to manage privacy risks arising from data processing. Protect-P is specifically focused on managing risks associated with cybersecurity-related privacy events (e.g., privacy breaches). The Cybersecurity Framework, although intended to cover all types of cybersecurity incidents, can be  leveraged to further support the management of risks associated with cybersecurity-related privacy events by using the Detect, Respond, and Recover Functions. Alternatively, organizations may use all five of the Cybersecurity Framework Functions in conjunction with Identify-P, Govern-P, Control-P, and Communicate-P to collectively address privacy and cybersecurity risks. Figure 5 uses the Venn diagram from section 1.2.1 to demonstrate how the Functions from both frameworks can be used in varying combinations to manage different aspects of privacy and cybersecurity risks. The five Privacy Framework Functions are defined as follows:  

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Profiles

Profiles are a selection of specific Functions, Categories, and Subcategories from the Core that an organization has prioritized to help it manage privacy risk. Profiles can be used to describe the current state and the desired target state of specific privacy activities. A Current Profile indicates privacy outcomes that an organization is currently achieving, while a Target Profile indicates the outcomes needed to achieve the desired privacy risk management goals. The differences between the two Profiles enable an organization to identify gaps, develop an action plan for improvement, and gauge the resources that would be needed (e.g., staffing, funding) to achieve privacy outcomes. This forms the basis of an organization’s plan for reducing privacy risk in a cost-effective, prioritized manner. Profiles also can aid in communicating risk within and between organizations by helping organizations understand and compare the current and desired state of privacy outcomes.

PROFILES

The Privacy Framework does not prescribe Profile templates to allo for flexibility in implementation. Under the Privacy Framework’s risk-based approach, organizations may not need to achieve every outcome or activity reflected in the Core. When developing a Profile, a organization may select or tailor the Functions, Categories, and Subcategories to its specific needs, including developing its own additional Functions, Categories, and Subcategories to account for unique organizational risks. An organization determines these needs by considering its mission or business objectives, privacy values, and risk tolerance; role(s) in the data processing ecosystem or industry sector; legal/regulatory requirements and industry best practices; risk management priorities and resources; and the privacy needs of individuals who are directly or indirectly served or affected by an organization’s systems, products, or services.

As illustrated in Figure 6, there is no specified order of development of Profiles. An organization may first develop a Target Profile in order to focus on its desired outcomes for privacy and then develop a Current Profile to identify gaps; alternatively, an organization may begin by identifying its current activities, and then consider how to adjust these activities for its Target Profile. An organization may choose to develop multiple Profiles for different roles, systems, products, or services, or categories of individuals (e.g., employees, customers) to enable better prioritization of activities and outcomes where there may be differing degrees of privacy risk. Organizations in a certain industry sector or with similar roles in the data processing ecosystem may coordinate to develop common Profiles.

Implementation Tiers

Tiers support organizational decision-making about how to manage privacy risk by taking into account the nature of the privacy risks engendered by an organization’s systems, products, or services and the sufficiency of the processes and resources an organization has in place to manage such risks. When selecting Tiers, an organization should consider its Target Profile(s) and how achievement may be supported or hampered by its current risk management practices, the degree of integration of privacy risk into its enterprise risk management portfolio, its data processing ecosystem relationships, and its workforce composition and training program.

There are four distinct Tiers, Partial (Tier 1), Risk Informed (Tier 2), Repeatable (Tier 3), and Adaptive (Tier 4), descriptions of which are in Appendix E. The Tiers represent a progression, albeit not a compulsory one. Although organizations at Tier 1 will likely benefit from moving to Tier 2, not all organizations need to achieve Tiers 3 or 4 (or may only focus on certain areas of these Tiers). Progression to higher Tiers is appropriate when an organization’s processes or resources at its current Tier may be insufficient to help it manage its privacy risks.

An organization can use the Tiers to communicate internally about resource allocations necessary to progress to a higher Tier or as general benchmarks to gauge progress in its capability to manage privacy risks. An organization can also use Tiers to understand the scale of resources and processes of other organizations in the data processing ecosystem and how they align with the organization’s privacy risk management priorities. Nonetheless, successful implementation of the Privacy Framework is based upon achieving the outcomes described in an organization’s Target Profile(s) and not upon Tier determination.

Gaps in mappings can also be used to identify where additional or revised standards, guidelines, and practices would help an organization to address emerging needs. An organization implementing a given Subcategory, or developing a new Subcategory, might discover that there is insufficient guidance for a related activity or outcome. To address that need, an organization might collaborate with technology leaders and/or standards bodies to draft, develop, and coordinate standards, guidelines, or practices.

A repository of informative references can be found at https://www.nist.gov/privacy-framework. These resources can support organizations’ use of the Privacy Framework and achievement of better privacy practices.

Strengthening Accountability

Accountability is generally considered a key privacy principle, although conceptually it is not unique to privacy.Accountability occurs throughout an organization, and it can be expressed at varying degrees of abstraction, for example as a cultural value, as governance policies and procedures, or as traceability relationships between privacy requirements and controls. Privacy risk management can be a means of supporting accountability at all organizational levels as it connects senior executives, who can communicate an organization’s privacy values and risk tolerance, to those at the business/process manager level, who can collaborate on the development and implementation of governance policies an procedures that support organizational privacy values. These policies and procedures can then be communicated to those at the implementation/operations level, who collaborate on defining the privacy requirements that support the expression of the policies and procedures in an organization’s systems, products, and services. Personnel at the implementation/operations level also select, implement, and assess controls as the technical and policy measures that meet the privacy requirements, and report on progress, gaps and deficiencies, incident management, and changing privacy risks so that those at the business/process manager level and the senior executives can better understand and respond appropriately.

Figure 7 provides a graphical representation of this bi-directional collaboration and communication and how elements of the Privacy Framework can be incorporated to facilitate the process. In this way, organizations can use the Privacy Framework as a tool to support accountability. They can also use the Privacy Framework in conjunction with other frameworks and guidance that provide additional practices to achieve accountability within and between organizations.

Establishing or Improving a Privacy Program

Using a simple model of “ready, set, go” phases, the Privacy Framework can support the creation of a new privacy program or improvement of an existing program. As an organization goes through these phases, it may use informative references to provide guidance on prioritizing or achieving outcomes. See section 3.1 for more information about informative references. In addition, a repository can be found at https://www.nist.gov/privacy-framework.

Ready

Effective privacy risk management requires an organization to understand its mission or business environment; its legal environment; its risk tolerance; the privacy risks engendered by its systems, products, or services; and its role(s) in the data processing ecosystem. An organization can use the Identify-P and Govern-P Functions to “get ready” by reviewing the Categories and Subcategories, and beginning to develop its Current Profile and Target Profile. Activities and outcomes such as establishing organizational privacy values and policies, determining and expressing an organizational risk tolerance, and conducting privacy risk assessments (see Appendix D for more information on privacy risk assessments) provide a foundation for completing the Profiles in “Set.”

Set

An organization completes its Current Profile by indicating which Category and Subcategory outcomes from the remaining Functions are being achieved. If an outcome is partially achieved, noting this fact will help support subsequent steps by providing baseline information. Informed by the activities under Identify and Govern, such as organizational privacy values and policies, organizational risk tolerance, and privacy risk assessment results, an organization completes its Target Profile focused on the assessment of the Categories and Subcategories describing its desired privacy outcomes. An organization also may develop its own additional Functions, Categories, and Subcategories to account for unique organizational risks. It may also consider influences and requirements of external stakeholders such as business customers and partners when creating a Target Profile. An organization can develop multiple Profiles to support its different business lines or processes, which may have different business needs and associated risk tolerances.

An organization compares the Current Profile and the Target Profile to determine gaps. Next, it creates a prioritized action plan to address gaps-reflecting mission drivers, costs and benefits, and risks-to achieve the outcomes in the Target Profile. An organization using the Cybersecurity Framework and the Privacy Framework together may develop integrated action plans. It then determines resources, including funding and workforce needs, necessary to address the gaps, which can inform the selection of an appropriate Tier. Using Profiles in this manner encourages an organization to make informed decisions about privacy activities, supports risk management, and enables an organization to perform cost-effective, targeted improvements.

Go

With the action plan “set,” an organization prioritizes which actions to take to address any gaps, and then adjusts its current privacy practices in order to achieve the Target Profile.

An organization can go through the phases nonsequentially as needed to continuously assess and improve its privacy posture. For instance, an organization may find that more frequent repetition of the Ready phase improves the quality of privacy risk assessments. Furthermore, an organization may monitor progress through iterative updates to the Current Profile or the Target Profile to adjust to changing risks, subsequently comparing the Current Profile to the Target Profile.

Applying to the System Development Life Cycle

The Target Profile can be aligned with the system development life cycle (SDLC) phases of plan, design, build/buy, deploy, operate, and decommission to support the achievement of the prioritized privacy outcomes. Beginning with the plan phase the prioritized privacy outcomes can be transformed into the privacy capabilities and requirements for the system, recognizing that requirements are likely to evolve during the remainder of the life cycle. A key milestone of the design phase is validating that the privacy capabilities and requirements match the needs and risk tolerance of an organization as expressed in the Target Profile. That same Target Profile can serve as an internal list to be assessed when deploying the system to verify that all privacy capabilities and requirements are implemented. The privacy outcomes determined by using the Privacy Framework should then serve as a basis for ongoing operation of the system. This includes occasional reassessment, capturing results in a Current Profile, to verify that privacy capabilities and requirements are still fulfilled.

Privacy risk assessments typically focus on the data life cycle, the stages through which data passes, often characterized as creation or collection, processing, dissemination, use, storage, and disposition, to include destruction and deletion. Aligning the SDLC and the data lifecycle by identifying and understanding how data are processed during all stages of the SDLC helps organizations to better manage privacy risks and informs the selection and implementation of privacy controls to meet privacy requirements.

A key factor in the management of privacy risk is an entity’s role(s) in the data processing ecosystem, which can affect not only its legal obligations, but also the measures it may take to manage privacy risk. As depicted in Figure 8, the data processing ecosystem encompasses a range of entities and roles that may have complex, multi-directional relationships with each other and individuals. Complexity can increase when entities are supported by a chain of sub-entities; for example, service providers may be supported by a series of service providers, or manufacturers may have multiple component suppliers. Figure 8 displays entities as having distinct roles, but some may have multiple roles, such as an organization providing services to other organizations and providing retail products to consumers. The roles in Figure 8 are intended to be notional classifications. In practice, an entity’s role(s) may be legally codified-for example, some laws classify organizations as data controllers or data processors-or classifications may be derived from industry sector designations. 

By developing one or more Profiles relevant to its role(s), an entity can use the Privacy Framework to consider how to manage privacy risk not only with regard to its own priorities, but also in relation to how the measures it may take affect other data processing ecosystem entities’ management of privacy risk. For example:

  • An organization that makes decisions about how to collect and use data about individuals may use a Profile to express privacy requirements to an external service provider (e.g., a cloud provider to which it is exporting data); the external service provider that processes the data may use its Profile to demonstrate the measures it has adopted to process data in line with contractual obligations.
  • An organization may express its privacy posture through a Current Profile to report results or to compare with acquisition requirements.
  • An industry sector may establish a common Profile that can be used by its members to customize their own Profiles.
  • A manufacturer may use a Target Profile to determine the capabilities to build into its products so that its business customers can meet the privacy needs of their end users.
  • A developer may use a Target Profile to consider how to design an application that enables privacy protections when used within other organizations’ system environments.

The Privacy Framework provides a common language to communicate privacy requirements with entities within the data processing ecosystem. The need for this communication can be particularly notable when the data processing ecosystem crosses national boundaries, such as with international data transfers. Organizational practices that support communication may include:

  • Determining privacy requirements;
  • Enacting privacy requirements through formal agreement (e.g., contracts, multi-party frameworks);
  • Communicating how those privacy requirements will be verified and validated;
  • Verifying that privacy requirements are met through a variety of assessment methodologies; and
  • Governing and managing the above activities. Informing Buying Decisions

Since either a Current or Target Profile can be used to generate a prioritized list of privacy requirements, these Profiles can also be used to inform decisions about buying products and services. By first selecting outcomes that are relevant to its privacy goals, an organization then can evaluate partners’ systems, products, or services against this outcome. For example, if a device is being purchased for environmental monitoring of a forest, manageability may be important to support capabilities for minimizing the processing of data about people using the forest and should drive a manufacturer evaluation against applicable Subcategories in the Core (e.g., CT.DP-P4: system or device configurations permit selective collection or disclosure of data elements).

In circumstances where it may not be possible to impose a set of privacy requirements on the supplier, the objective should be to make the best buying decision among multiple suppliers, given a carefully determined list of privacy requirements. Often, this means some degree of trade-off, comparing multiple products or services with known gaps to the Profile. If the system, product, or service purchased did not meet all of the objectives described in the Profile, an organization could address the residual risk through mitigation measures or other management actions.

Framework Functions

ID-P: Identify-P

Develop the organizational understanding to manage privacy risk for individuals arising from data processing. The activities in the Identify-P Function are foundational for effective use of the Privacy Framework. Inventorying the circumstances under which data are processed, understanding the privacy interests of individuals directly or indirectly served or affected by an organization, and conducting riskā€¦

GV-P: Govern-P

Develop and implement the organizational governance structure to enable an ongoing understanding of the organization’s risk management priorities that are informed by privacy risk. The Govern-P Function is similarly foundational, but focuses on organizational-level activities such as establishing organizational privacy values and policies, identifying legal/regulatory requirements, and understanding organizational risk tolerance that enable an organization to focus and prioritizeā€¦

CT-P: Control-P

Develop and implement appropriate activities to enable organizations or individuals to manage data with sufficient granularity to manage privacy risks. The Control-P Function considers data processing management from the standpoint of both organizations and individuals.

CM-P: Communicate-P

Develop and implement appropriate activities to enable organizations and individuals to have a reliable understanding and engage in a dialogue about how data are processed and associated privacy risks. The Communicate-P Function recognizes that both organizations and individuals may need to know how data are processed in order to manage privacy risk effectively.

PR-P: Protect-P

Develop and implement appropriate data processing safeguards. The Protect-P Function covers data protection to prevent cybersecurity-related privacy events,the overlap between privacy and cybersecurity risk management.