





Internet Engineering Task Force (IETF)                     D. Waltermire
Request for Comments: 7632                                          NIST
Category: Informational                                    D. Harrington
ISSN: 2070-1721                                       Effective Software
                                                          September 2015


       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.

















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Copyright Notice

   Copyright (c) 2015 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

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












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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:

   o  securely collecting and aggregating configuration and operational
      data, and

   o  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:

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

   o  characteristics to inform development of a requirements document,

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

   o  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.




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   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:

   o  collecting the attributes of a given endpoint;

   o  making the attributes available for evaluation and action; and

   o  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.






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   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.






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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.



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         *  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





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         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.



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   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.











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   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.






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         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.




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   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.






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   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.




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   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:

   o  collected hardware and software inventory data, and

   o  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.



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   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:

   o  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.

   o  Determining compliance with organizational policies governing
      endpoint posture.

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

   o  Searching for current and historic indicators of compromise.

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





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

   o  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.









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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.



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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



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   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.







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   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.






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   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.







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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>.












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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: david.waltermire@nist.gov


   David Harrington
   Effective Software
   16 Bayview Drive
   Westerly, Rhode Island  02891
   United States

   Email: ietfdbh@gmail.com





















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