Endpoint Security Posture Assessment: Enterprise Use Cases

Abstract

This memo documents a sampling of use cases for securely aggregating configuration and operational data and evaluating that data to determine an organization's security posture. From these operational use cases, we can derive common functional capabilities and requirements to guide development of vendor-neutral, interoperable standards for aggregating and evaluating data relevant to security posture.

Status of This Memo

This document is not an Internet Standards Track specification; it is published for informational purposes.

This document is a product of the Internet Engineering Task Force (IETF). It represents the consensus of the IETF community. It has received public review and has been approved for publication by the Internet Engineering Steering Group (IESG). Not all documents approved by the IESG are a candidate for any level of Internet Standard; see Section 2 of RFC 5741.

Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at http://www.rfc-editor.org/info/rfc7632.

Copyright Notice

Copyright © 2015 IETF Trust and the persons identified as the document authors. All rights reserved.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Endpoint Posture Assessment . . . . . . . . . . . . . . . . .   4
     2.1.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . .   5
       2.1.1.  Define, Publish, Query, and Retrieve Security
               Automation Data . . . . . . . . . . . . . . . . . . .   6
       2.1.2.  Endpoint Identification and Assessment Planning . . .   9
       2.1.3.  Endpoint Posture Attribute Value Collection . . . . .  11
       2.1.4.  Posture Attribute Evaluation  . . . . . . . . . . . .  11
     2.2.  Usage Scenarios . . . . . . . . . . . . . . . . . . . . .  13
       2.2.1.  Definition and Publication of Automatable
               Configuration Checklists  . . . . . . . . . . . . . .  13
       2.2.2.  Automated Checklist Verification  . . . . . . . . . .  14
       2.2.3.  Detection of Posture Deviations . . . . . . . . . . .  17
       2.2.4.  Endpoint Information Analysis and Reporting . . . . .  18
       2.2.5.  Asynchronous Compliance/Vulnerability Assessment at
               Ice Station Zebra . . . . . . . . . . . . . . . . . .  18
       2.2.6.  Identification and Retrieval of Guidance  . . . . . .  20
       2.2.7.  Guidance Change Detection . . . . . . . . . . . . . .  21
   3.  Security Considerations . . . . . . . . . . . . . . . . . . .  22
   4.  Informative References  . . . . . . . . . . . . . . . . . . .  22
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  23
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  23

1. Introduction

This document describes the core set of use cases for endpoint posture assessment for enterprises. It provides a discussion of these use cases and associated building-block capabilities. The described use cases support:

• securely collecting and aggregating configuration and operational data, and

• evaluating that data to determine the security posture of individual endpoints.

Additionally, this document describes a set of usage scenarios that provide examples for using the use cases and associated building blocks to address a variety of operational functions.

These operational use cases and related usage scenarios cross many IT security domains. The use cases enable the derivation of common:

• concepts that are expressed as building blocks in this document,

• characteristics to inform development of a requirements document,

• information concepts to inform development of an information model document, and

• functional capabilities to inform development of an architecture document.

Together, these ideas will be used to guide development of vendor- neutral, interoperable standards for collecting, aggregating, and evaluating data relevant to security posture.

Using this standard data, tools can analyze the state of endpoints as well as user activities and behaviour, and evaluate the security posture of an organization. Common expression of information should enable interoperability between tools (whether customized, commercial, or freely available), and the ability to automate portions of security processes to gain efficiency, react to new threats in a timely manner, and free up security personnel to work on more advanced problems.

The goal is to enable organizations to make informed decisions that support organizational objectives, to enforce policies for hardening systems, to prevent network misuse, to quantify business risk, and to collaborate with partners to identify and mitigate threats.

It is expected that use cases for enterprises and for service providers will largely overlap. When considering this overlap, there are additional complications for service providers, especially in handling information that crosses administrative domains.

The output of endpoint posture assessment is expected to feed into additional processes, such as policy-based enforcement of acceptable state, verification and monitoring of security controls, and compliance to regulatory requirements.

2. Endpoint Posture Assessment

Endpoint posture assessment involves orchestrating and performing data collection and evaluating the posture of a given endpoint. Typically, endpoint posture information is gathered and then published to appropriate data repositories to make collected information available for further analysis supporting organizational security processes.

Endpoint posture assessment typically includes:

• collecting the attributes of a given endpoint;

• making the attributes available for evaluation and action; and

• verifying that the endpoint's posture is in compliance with enterprise standards and policy.

As part of these activities, it is often necessary to identify and acquire any supporting security automation data that is needed to drive and feed data collection and evaluation processes.

The following is a typical workflow scenario for assessing endpoint posture:

1. Some type of trigger initiates the workflow. For example, an operator or an application might trigger the process with a request, or the endpoint might trigger the process using an event-driven notification.

2. An operator/application selects one or more target endpoints to be assessed.

3. An operator/application selects which policies are applicable to the targets.

4. For each target:

A. The application determines which (sets of) posture attributes

need to be collected for evaluation. Implementations should be able to support (possibly mixed) sets of standardized and proprietary attributes.

B. The application might retrieve previously collected

information from a cache or data store, such as a data store populated by an asset management system.

C. The application might establish communication with the

target, mutually authenticate identities and authorizations, and collect posture attributes from the target.

D. The application might establish communication with one or

more intermediaries or agents, which may be local or external. When establishing connections with an intermediary or agent, the application can mutually authenticate their identities and determine authorizations, and collect posture attributes about the target from the intermediaries or agents.

E. The application communicates target identity and (sets of)

collected attributes to an evaluator, which is possibly an external process or external system.

F. The evaluator compares the collected posture attributes with

expected values as expressed in policies.

G. The evaluator reports the evaluation result for the requested

assessment, in a standardized or proprietary format, such as a report, a log entry, a database entry, or a notification.

2.1. Use Cases

The following subsections detail specific use cases for assessment planning, data collection, analysis, and related operations pertaining to the publication and use of supporting data. Each use case is defined by a short summary containing a simple problem statement, followed by a discussion of related concepts, and a listing of associated building blocks that represent the capabilities needed to support the use case. These use cases and building blocks identify separate units of functionality that may be supported by different components of an architectural model.

2.1.1. Define, Publish, Query, and Retrieve Security Automation Data

This use case describes the need for security automation data to be defined and published to one or more data stores, as well as queried and retrieved from these data stores for the explicit use of posture collection and evaluation.

Security automation data is a general concept that refers to any data expression that may be generated and/or used as part of the process of collecting and evaluating endpoint posture. Different types of security automation data will generally fall into one of three categories:

   Guidance:  Instructions and related metadata that guide the attribute
   
         collection and evaluation processes.  The purpose of this data
         is to allow implementations to be data-driven, thus enabling
         their behavior to be customized without requiring changes to
         deployed software.

This type of data tends to change in units of months and days. In cases where assessments are made more dynamic, it may be necessary to handle changes in the scope of hours or minutes. This data will typically be provided by large organizations, product vendors, and some third parties. Thus, it will tend to be shared across large enterprises and customer communities. In some cases, access may be controlled to specific authenticated users. In other cases, the data may be provided broadly with little to no access control.

This includes:

• Listings of attribute identifiers for which values may be collected and evaluated.

• Lists of attributes that are to be collected along with metadata that includes: when to collect a set of attributes based on a defined interval or event, the duration of collection, and how to go about collecting a set of attributes.

• Guidance that specifies how old collected data can be when used for evaluation.

• Policies that define how to target and perform the evaluation of a set of attributes for different kinds or groups of endpoints and the assets they are composed of. In some cases, it may be desirable to maintain hierarchies of policies as well.

• References to human-oriented data that provide technical, organizational, and/or policy context. This might include references to: best practices documents, legal guidance and legislation, and instructional materials related to the automation data in question.

   Attribute Data:  Data collected through automated and manual
         mechanisms describing organizational and posture details
         pertaining to specific endpoints and the assets that they are
         composed of (e.g., hardware, software, accounts).  The purpose
         of this type of data is to characterize an endpoint (e.g.,
         endpoint type, organizationally expected function/role) and to
         provide actual and expected state data pertaining to one or
         more endpoints.  This data is used to determine what posture
         attributes to collect from which endpoints and to feed one or
         more evaluations.

This type of data tends to change in units of days, minutes, and seconds, with posture attribute values typically changing more frequently than endpoint characterizations. This data tends to be organizationally and endpoint specific, with specific operational groups of endpoints tending to exhibit similar attribute profiles. Generally, this data will not be shared outside an organizational boundary and will require authentication with specific access controls.

This includes:

• Endpoint characterization data that describes the endpoint type, organizationally expected function/role, etc.

• Collected endpoint posture attribute values and related context including: time of collection, tools used for collection, etc.

• Organizationally defined expected posture attribute values targeted to specific evaluation guidance and endpoint characteristics. This allows a common set of guidance to be parameterized for use with different groups of endpoints.

   Processing Artifacts:  Data that is generated by, and is specific to,
         an individual assessment process.  This data may be used as
         part of the interactions between architectural components to
         drive and coordinate collection and evaluation activities.  Its
         lifespan will be bounded by the lifespan of the assessment.  It
         may also be exchanged and stored to provide historic context

around an assessment activity so that individual assessments can be grouped, evaluated, and reported in an enterprise context.

This includes:

• The identified set of endpoints for which an assessment should be performed.

• The identified set of posture attributes that need to be collected from specific endpoints to perform an evaluation.

• The resulting data generated by an evaluation process including the context of what was assessed, what it was assessed against, what collected data was used, when it was collected, and when the evaluation was performed.

The information model for security automation data must support a variety of different data types as described above, along with the associated metadata that is needed to support publication, query, and retrieval operations. It is expected that multiple data models will be used to express specific data types requiring specialized or extensible security automation data repositories. The different temporal characteristics, access patterns, and access control dimensions of each data type may also require different protocols and data models to be supported furthering the potential requirement for specialized data repositories. See [RFC3444] for a description and discussion of distinctions between an information and data model. It is likely that additional kinds of data will be identified through the process of defining requirements and an architectural model. Implementations supporting this building block will need to be extensible to accommodate the addition of new types of data, whether proprietary or (preferably) using a standard format.

The building blocks of this use case are:

   Data Definition:  Security automation data will guide and inform
         collection and evaluation processes.  This data may be designed
         by a variety of roles -- application implementers may build
         security automation data into their applications;
         administrators may define guidance based on organizational
         policies; operators may define guidance and attribute data as
         needed for evaluation at runtime; and so on.  Data producers
         may choose to reuse data from existing stores of security
         automation data and/or may create new data.  Data producers may
         develop data based on available standardized or proprietary
         data models, such as those used for network management and/or
         host management.
   
   Data Publication:  The capability to enable data producers to publish
         data to a security automation data store for further use.
         Published data may be made publicly available or access may be
         based on an authorization decision using authenticated
         credentials.  As a result, the visibility of specific security
         automation data to an operator or application may be public,
         enterprise-scoped, private, or controlled within any other
         scope.
   
   Data Query:  An operator or application should be able to query a
         security automation data store using a set of specified
         criteria.  The result of the query will be a listing matching
         the query.  The query result listing may contain publication
         metadata (e.g., create date, modified date, publisher, etc.)
         and/or the full data, a summary, snippet, or the location to
         retrieve the data.
   
   Data Retrieval:  A user, operator, or application acquires one or
         more specific security automation data entries.  The location
         of the data may be known a priori, or may be determined based
         on decisions made using information from a previous query.
   
   Data Change Detection:  An operator or application needs to know when
         security automation data they are interested in has been
         published to, updated in, or deleted from a security automation
         data store that they have been authorized to access.

These building blocks are used to enable acquisition of various instances of security automation data based on specific data models that are used to drive assessment planning (see Section 2.1.2), posture attribute value collection (see Section 2.1.3), and posture evaluation (see Section 2.1.4).

2.1.2. Endpoint Identification and Assessment Planning

This use case describes the process of discovering endpoints, understanding their composition, identifying the desired state to assess against, and calculating what posture attributes to collect to enable evaluation. This process may be a set of manual, automated, or hybrid steps that are performed for each assessment.

The building blocks of this use case are:

   Endpoint Discovery:  To determine the current or historic presence of
         endpoints in the environment that are available for posture
         assessment.  Endpoints are identified in support of discovery
         by using information previously obtained or using other
         collection mechanisms to gather identification and
         characterization data.  Previously obtained data may originate
         from sources such as network authentication exchanges.
   
   Endpoint Characterization:  The act of acquiring, through automated
         collection or manual input, and organizing attributes
         associated with an endpoint (e.g., type, organizationally
         expected function/role, hardware/software versions).
   
   Endpoint Target Identification:  Determine the candidate endpoint
         target(s) against which to perform the assessment.  Depending
         on the assessment trigger, a single endpoint or multiple
         endpoints may be targeted based on characterized endpoint
         attributes.  Guidance describing the assessment to be performed
         may contain instructions or references used to determine the
         applicable assessment targets.  In this case, the Data Query
         and/or Data Retrieval building blocks (see Section 2.1.1) may
         be used to acquire this data.
   
   Endpoint Component Inventory:  To determine what applicable desired
         states should be assessed, it is first necessary to acquire the
         inventory of software, hardware, and accounts associated with
         the targeted endpoint(s).  If the assessment of the endpoint is
         not dependent on the these details, then this capability is not
         required for use in performing the assessment.  This process
         can be treated as a collection use case for specific posture
         attributes.  In this case, the building blocks for
         Endpoint Posture Attribute Value Collection (see Section 2.1.3)
         can be used.
   
   Posture Attribute Identification:  Once the endpoint targets and
         their associated asset inventory is known, it is then necessary
         to calculate what posture attributes are required to be
         collected to perform the desired evaluation.  When available,
         existing posture data is queried for suitability using the Data
         Query building block (see Section 2.1.1).  Such posture data is
         suitable if it is complete and current enough for use in the
         evaluation.  Any unsuitable posture data is identified for
         collection.

If this is driven by guidance, then the Data Query and/or Data Retrieval building blocks (see Section 2.1.1) may be used to acquire this data.

At this point, the set of posture attribute values to use for evaluation are known, and they can be collected if necessary (see Section 2.1.3).

2.1.3. Endpoint Posture Attribute Value Collection

This use case describes the process of collecting a set of posture attribute values related to one or more endpoints. This use case can be initiated by a variety of triggers including:

1. a posture change or significant event on the endpoint.

   2.  a network event (e.g., endpoint connects to a network/VPN,
   
       specific netflow [RFC3954] is detected).

3. a scheduled or ad hoc collection task.

The building blocks of this use case are:

   Collection Guidance Acquisition:  If guidance is required to drive
         the collection of posture attributes values, this capability is
         used to acquire this data from one or more security automation
         data stores.  Depending on the trigger, the specific guidance
         to acquire might be known.  If not, it may be necessary to
         determine the guidance to use based on the component inventory
         or other assessment criteria.  The Data Query and/or Data
         Retrieval building blocks (see Section 2.1.1) may be used to
         acquire this guidance.
   
   Posture Attribute Value Collection:  The accumulation of posture
         attribute values.  This may be based on collection guidance
         that is associated with the posture attributes.

Once the posture attribute values are collected, they may be persisted for later use or they may be immediately used for posture evaluation.

2.1.4. Posture Attribute Evaluation

This use case represents the action of analyzing collected posture attribute values as part of an assessment. The primary focus of this use case is to support evaluation of actual endpoint state against the expected state selected for the assessment.

This use case can be initiated by a variety of triggers including:

1. a posture change or significant event on the endpoint.

   2.  a network event (e.g., endpoint connects to a network/VPN,
   
       specific netflow [RFC3954] is detected).

3. a scheduled or ad hoc evaluation task.

The building blocks of this use case are:

   Collected Posture Change Detection:  An operator or application has a
         mechanism to detect the availability of new posture attribute
         values or changes to existing ones.  The timeliness of
         detection may vary from immediate to on-demand.  Having the
         ability to filter what changes are detected will allow the
         operator to focus on the changes that are relevant to their use
         and will enable evaluation to occur dynamically based on
         detected changes.
   
   Posture Attribute Value Query:  If previously collected posture
         attribute values are needed, the appropriate data stores are
         queried to retrieve them using the Data Query building block
         (see Section 2.1.1).  If all posture attribute values are
         provided directly for evaluation, then this capability may not
         be needed.
   
   Evaluation Guidance Acquisition:  If guidance is required to drive
         the evaluation of posture attributes values, this capability is
         used to acquire this data from one or more security automation
         data stores.  Depending on the trigger, the specific guidance
         to acquire might be known.  If not, it may be necessary to
         determine the guidance to use based on the component inventory
         or other assessment criteria.  The Data Query and/or Data
         Retrieval building blocks (see Section 2.1.1) may be used to
         acquire this guidance.
   
   Posture Attribute Evaluation:  The comparison of posture attribute
         values against their expected values as expressed in the
         specified guidance.  The result of this comparison is output as
         a set of posture evaluation results.  Such results include
         metadata required to provide a level of assurance with respect
         to the posture attribute data and, therefore, evaluation
         results.  Examples of such metadata include provenance and or
         availability data.

While the primary focus of this use case is around enabling the comparison of expected vs. actual state, the same building blocks can support other analysis techniques that are applied to collected posture attribute data (e.g., trending, historic analysis).

Completion of this process represents a complete assessment cycle as defined in Section 2.

2.2. Usage Scenarios

In this section, we describe a number of usage scenarios that utilize aspects of endpoint posture assessment. These are examples of common problems that can be solved with the building blocks defined above.

2.2.1. Definition and Publication of Automatable Configuration

Checklists

A vendor manufactures a number of specialized endpoint devices. They also develop and maintain an operating system for these devices that enables end-user organizations to configure a number of security and operational settings. As part of their customer support activities, they publish a number of secure configuration guides that provide minimum security guidelines for configuring their devices.

Each guide they produce applies to a specific model of device and version of the operating system and provides a number of specialized configurations depending on the device's intended function and what add-on hardware modules and software licenses are installed on the device. To enable their customers to evaluate the security posture of their devices to ensure that all appropriate minimal security settings are enabled, they publish automatable configuration checklists using a popular data format that defines what settings to collect using a network management protocol and appropriate values for each setting. They publish these checklists to a public security automation data store that customers can query to retrieve applicable checklist(s) for their deployed specialized endpoint devices.

Automatable configuration checklists could also come from sources other than a device vendor, such as industry groups or regulatory authorities, or enterprises could develop their own checklists.

This usage scenario employs the following building blocks defined in Section 2.1.1 above:

   Data Definition:  To allow guidance to be defined using standardized
         or proprietary data models that will drive collection and
         evaluation.
   
   Data Publication:  Providing a mechanism to publish created guidance
         to a security automation data store.
   
   Data Query:  To locate and select existing guidance that may be
         reused.
   
   Data Retrieval  To retrieve specific guidance from a security
         automation data store for editing.

While each building block can be used in a manual fashion by a human operator, it is also likely that these capabilities will be implemented together in some form of a guidance editor or generator application.

2.2.2. Automated Checklist Verification

A financial services company operates a heterogeneous IT environment. In support of their risk management program, they utilize vendor- provided automatable security configuration checklists for each operating system and application used within their IT environment. Multiple checklists are used from different vendors to ensure adequate coverage of all IT assets.

To identify what checklists are needed, they use automation to gather an inventory of the software versions utilized by all IT assets in the enterprise. This data gathering will involve querying existing data stores of previously collected endpoint software inventory posture data and actively collecting data from reachable endpoints as needed, utilizing network and systems management protocols. Previously collected data may be provided by periodic data collection, network connection-driven data collection, or ongoing event-driven monitoring of endpoint posture changes.

Appropriate checklists are queried, located, and downloaded from the relevant guidance data stores. The specific data stores queried and the specifics of each query may be driven by data including:

• collected hardware and software inventory data, and

• associated asset characterization data that may indicate the organizationally defined functions of each endpoint.

Checklists may be sourced from guidance data stores maintained by an application or OS vendor, an industry group, a regulatory authority, or directly by the enterprise.

The retrieved guidance is cached locally to reduce the need to retrieve the data multiple times.

Driven by the setting data provided in the checklist, a combination of existing configuration data stores and data collection methods are used to gather the appropriate posture attributes from (or pertaining to) each endpoint. Specific posture attribute values are gathered based on the defined enterprise function and software inventory of each endpoint. The collection mechanisms used to collect software inventory posture will be used again for this purpose. Once the data is gathered, the actual state is evaluated against the expected state criteria defined in each applicable checklist.

A checklist can be assessed as a whole, or a specific subset of the checklist can be assessed resulting in partial data collection and evaluation.

The results of checklist evaluation are provided to appropriate operators and applications to drive additional business logic. Specific applications for checklist evaluation results are out of scope for current SACM (Security Automation and Continuous Monitoring) efforts. Irrespective of specific applications, the availability, timeliness, and liveness of results are often of general concern. Network latency and available bandwidth often create operational constraints that require trade-offs between these concerns and need to be considered.

Uses of checklists and associated evaluation results may include, but are not limited to:

• Detecting endpoint posture deviations as part of a change management program to identify:

• missing required patches,

• unauthorized changes to hardware and software inventory, and

• unauthorized changes to configuration items.

• Determining compliance with organizational policies governing endpoint posture.

• Informing configuration management, patch management, and vulnerability mitigation and remediation decisions.

• Searching for current and historic indicators of compromise.

• Detecting current and historic infection by malware and determining the scope of infection within an enterprise.

• Detecting performance, attack, and vulnerable conditions that warrant additional network diagnostics, monitoring, and analysis.

• Informing network access control decision-making for wired, wireless, or VPN connections.

This usage scenario employs the following building blocks defined in Section 2.1.1 above:

   Endpoint Discovery:  The purpose of discovery is to determine the
         type of endpoint to be posture assessed.
   
   Endpoint Target Identification:  To identify what potential endpoint
         targets the checklist should apply to based on organizational
         policies.
   
   Endpoint Component Inventory:  Collecting and consuming the software
         and hardware inventory for the target endpoints.
   
   Posture Attribute Identification:  To determine what data needs to be
         collected to support evaluation, the checklist is evaluated
         against the component inventory and other endpoint metadata to
         determine the set of posture attribute values that are needed.
   
   Collection Guidance Acquisition:  Based on the identified posture
         attributes, the application will query appropriate security
         automation data stores to find the "applicable" collection
         guidance for each endpoint in question.
   
   Posture Attribute Value Collection:  For each endpoint, the values
         for the required posture attributes are collected.
   
   Posture Attribute Value Query:  If previously collected posture
         attribute values are used, they are queried from the
         appropriate data stores for the target endpoint(s).
   
   Evaluation Guidance Acquisition:  Any guidance that is needed to
         support evaluation is queried and retrieved.
   
   Posture Attribute Evaluation:  The resulting posture attribute values
         from previous collection processes are evaluated using the
         evaluation guidance to provide a set of posture results.

2.2.3. Detection of Posture Deviations

Example Corporation has established secure configuration baselines for each different type of endpoint within their enterprise including: network infrastructure, mobile, client, and server computing platforms. These baselines define an approved list of hardware, software (i.e., operating system, applications, and patches), and associated required configurations. When an endpoint connects to the network, the appropriate baseline configuration is communicated to the endpoint based on its location in the network, the expected function of the device, and other asset management data. It is checked for compliance with the baseline, and any deviations are indicated to the device's operators. Once the baseline has been established, the endpoint is monitored for any change events pertaining to the baseline on an ongoing basis. When a change occurs to posture defined in the baseline, updated posture information is exchanged, allowing operators to be notified and/or automated action to be taken.

Like the Automated Checklist Verification usage scenario (see Section 2.2.2), this usage scenario supports assessment based on automatable checklists. It differs from that scenario by monitoring for specific endpoint posture changes on an ongoing basis. When the endpoint detects a posture change, an alert is generated identifying the specific changes in posture, thus allowing assessment of the delta to be performed instead of a full assessment as in the previous case. This usage scenario employs the same building blocks as Automated Checklist Verification (see section 2.2.2). It differs slightly in how it uses the following building blocks:

   Endpoint Component Inventory:  Additionally, changes to the hardware
         and software inventory are monitored, with changes causing
         alerts to be issued.
   
   Posture Attribute Value Collection:  After the initial assessment,
         posture attributes are monitored for changes.  If any of the
         selected posture attribute values change, an alert is issued.
   
   Posture Attribute Value Query:  The previous state of posture
         attributes are tracked, allowing changes to be detected.
   
   Posture Attribute Evaluation:  After the initial assessment, a
         partial evaluation is performed based on changes to specific
         posture attributes.

This usage scenario highlights the need to query a data store to prepare a compliance report for a specific endpoint and also the need for a change in endpoint state to trigger Collection and Evaluation.

2.2.4. Endpoint Information Analysis and Reporting

Freed from the drudgery of manual endpoint compliance monitoring, one of the security administrators at Example Corporation notices (not using SACM standards) that five endpoints have been uploading lots of data to a suspicious server on the Internet. The administrator queries data stores for specific endpoint posture to see what software is installed on those endpoints and finds that they all have a particular program installed. She then queries the appropriate data stores to see which other endpoints have that program installed. All these endpoints are monitored carefully (not using SACM standards), which allows the administrator to detect that the other endpoints are also infected.

This is just one example of the useful analysis that a skilled analyst can do using data stores of endpoint posture.

This usage scenario employs the following building blocks defined in Section 2.1.1 above:

   Posture Attribute Value Query:  Previously collected posture
         attribute values for the target endpoint(s) are queried from
         the appropriate data stores using a standardized method.

This usage scenario highlights the need to query a repository for attributes to see which attributes certain endpoints have in common.

2.2.5. Asynchronous Compliance/Vulnerability Assessment at Ice Station

Zebra

A university team receives a grant to do research at a government facility in the Arctic. The only network communications will be via an intermittent, low-speed, high-latency, high-cost satellite link. During their extended expedition, they will need to show continued compliance with the security policies of the university, the government, and the provider of the satellite network, as well as keep current on vulnerability testing. Interactive assessments are therefore not reliable, and since the researchers have very limited funding, they need to minimize how much money they spend on network data.

Prior to departure, they register all equipment with an asset management system owned by the university, which will also initiate and track assessments.

On a periodic basis -- either after a maximum time delta or when the security automation data store has received a threshold level of new vulnerability definitions -- the university uses the information in the asset management system to put together a collection request for all of the deployed assets that encompasses the minimal set of artifacts necessary to evaluate all three security policies as well as vulnerability testing.

In the case of new critical vulnerabilities, this collection request consists only of the artifacts necessary for those vulnerabilities, and collection is only initiated for those assets that could potentially have a new vulnerability.

(Optional) Asset artifacts are cached in a local configuration management database (CMDB). When new vulnerabilities are reported to the security automation data store, a request to the live asset is only done if the artifacts in the CMDB are incomplete and/or not current enough.

The collection request is queued for the next window of connectivity. The deployed assets eventually receive the request, fulfill it, and queue the results for the next return opportunity.

The collected artifacts eventually make it back to the university where the level of compliance and vulnerability exposed is calculated and asset characteristics are compared to what is in the asset management system for accuracy and completeness.

Like the Automated Checklist Verification usage scenario (see section 2.2.2), this usage scenario supports assessment based on checklists. It differs from that scenario in how guidance, collected posture attribute values, and evaluation results are exchanged due to bandwidth limitations and availability. This usage scenario employs the same building blocks as Automated Checklist Verification (see section 2.2.2). It differs slightly in how it uses the following building blocks:

   Endpoint Component Inventory:  It is likely that the component
         inventory will not change.  If it does, this information will
         need to be batched and transmitted during the next
         communication window.
   
   Collection Guidance Acquisition:  Due to intermittent communication
         windows and bandwidth constraints, changes to collection
         guidance will need to batched and transmitted during the next
         communication window.  Guidance will need to be cached locally
         to avoid the need for remote communications.
   
   Posture Attribute Value Collection:  The specific posture attribute
         values to be collected are identified remotely and batched for
         collection during the next communication window.  If a delay is
         introduced for collection to complete, results will need to be
         batched and transmitted.
   
   Posture Attribute Value Query:  Previously collected posture
         attribute values will be stored in a remote data store for use
         at the university.
   
   Evaluation Guidance Acquisition:  Due to intermittent communication
         windows and bandwidth constraints, changes to evaluation
         guidance will need to batched and transmitted during the next
         communication window.  Guidance will need to be cached locally
         to avoid the need for remote communications.
   
   Posture Attribute Evaluation:  Due to the caching of posture
         attribute values and evaluation guidance, evaluation may be
         performed at both the university campus as well as the
         satellite site.

This usage scenario highlights the need to support low-bandwidth, intermittent, or high-latency links.

2.2.6. Identification and Retrieval of Guidance

In preparation for performing an assessment, an operator or application will need to identify one or more security automation data stores that contain the guidance entries necessary to perform data collection and evaluation tasks. The location of a given guidance entry will either be known a priori or known security automation data stores will need to be queried to retrieve applicable guidance.

To query guidance it will be necessary to define a set of search criteria. This criteria will often utilize a logical combination of publication metadata (e.g., publishing identity, create time, modification time) and criteria elements specific to the guidance data. Once the criteria are defined, one or more security automation data stores will need to be queried, thus generating a result set. Depending on how the results are used, it may be desirable to return the matching guidance directly, a snippet of the guidance matching the query, or a resolvable location to retrieve the data at a later time. The guidance matching the query will be restricted based on the authorized level of access allowed to the requester.

If the location of guidance is identified in the query result set, the guidance will be retrieved when needed using one or more data retrieval requests. A variation on this approach would be to maintain a local cache of previously retrieved data. In this case, only guidance that is determined to be stale by some measure will be retrieved from the remote data store.

Alternately, guidance can be discovered by iterating over data published with a given context within a security automation data store. Specific guidance can be selected and retrieved as needed.

This usage scenario employs the following building blocks defined in Section 2.1.1 above:

   Data Query:  Enables an operator or application to query one or more
         security automation data stores for guidance using a set of
         specified criteria.
   
   Data Retrieval:  If data locations are returned in the query result
         set, then specific guidance entries can be retrieved and
         possibly cached locally.

2.2.7. Guidance Change Detection

An operator or application may need to identify new, updated, or deleted guidance in a security automation data store for which they have been authorized to access. This may be achieved by querying or iterating over guidance in a security automation data store, or through a notification mechanism that generates alerts when changes are made to a security automation data store.

Once guidance changes have been determined, data collection and evaluation activities may be triggered.

This usage scenario employs the following building blocks defined in Section 2.1.1 above:

   Data Change Detection:  Allows an operator or application to identify
         guidance changes in a security automation data store for which
         they have been authorized to access.
   
   Data Retrieval:  If data locations are provided by the change
         detection mechanism, then specific guidance entries can be
         retrieved and possibly cached locally.

3. Security Considerations

This memo documents, for informational purposes, use cases for security automation. Specific security and privacy considerations will be provided in related documents (e.g., requirements, architecture, information model, data model, protocol) as appropriate to the function described in each related document.

One consideration for security automation is that a malicious actor could use the security automation infrastructure and related collected data to gain access to an item of interest. This may include personal data, private keys, software and configuration state that can be used to inform an attack against the network and endpoints, and other sensitive information. It is important that security and privacy considerations in the related documents indicate methods to both identify and prevent such activity.

For consideration are means for protecting the communications as well as the systems that store the information. For communications between the varying SACM components, there should be considerations for protecting the confidentiality, data integrity, and peer entity authentication. For exchanged information, there should be a means to authenticate the origin of the information. This is important where tracking the provenance of data is needed. Also, for any systems that store information that could be used for unauthorized or malicious purposes, methods to identify and protect against unauthorized usage, inappropriate usage, and denial of service need to be considered.

4. Informative References

   [RFC3444]  Pras, A. and J. Schoenwaelder, "On the Difference between
              Information Models and Data Models", RFC 3444,
              DOI 10.17487/RFC3444, January 2003,
              <http://www.rfc-editor.org/info/rfc3444>.
   
   [RFC3954]  Claise, B., Ed., "Cisco Systems NetFlow Services Export
              Version 9", RFC 3954, DOI 10.17487/RFC3954, October 2004,
              <http://www.rfc-editor.org/info/rfc3954>.

Acknowledgements

Adam Montville edited early versions of this document.

Kathleen Moriarty and Stephen Hanna contributed text describing the scope of the document.

Gunnar Engelbach, Steve Hanna, Chris Inacio, Kent Landfield, Lisa Lorenzin, Adam Montville, Kathleen Moriarty, Nancy Cam-Winget, and Aron Woland provided text about the use cases for various revisions of this document.

Authors' Addresses

   David Waltermire
   National Institute of Standards and Technology
   100 Bureau Drive
   Gaithersburg, Maryland  20877
   United States
   
   Email: [email protected]
   
   David Harrington
   Effective Software
   16 Bayview Drive
   Westerly, Rhode Island  02891
   United States

Email:

          [email protected]