wdiff rfc7632.alt-original rfc7632.txt

Security Automation and Continuous Monitoring WG
Internet Engineering Task Force (IETF) D. Waltermire
Internet-Draft
Request for Comments: 7632 NIST
Intended status:
Category: Informational D. Harrington
Expires: January 2, 2016
ISSN: 2070-1721 Effective Software
July 1,
September 2015

Endpoint Security Posture Assessment - Assessment: Enterprise Use Cases
draft-ietf-sacm-use-cases-10

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

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This Internet-Draft will expire on January 2, 2016.
http://www.rfc-editor.org/info/rfc7632.

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Table of Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Endpoint Posture Assessment . . . . . . . . . . . . . . . . . 4 3
2.1. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1. Define, Publish, Query Query, and Retrieve Security
Automation Data . . . . . . . . . . . . . . . . . . . 5
2.1.2. Endpoint Identification and Assessment Planning . . . 9
2.1.3. Endpoint Posture Attribute Value Collection . . . . . 10
2.1.4. Posture Attribute Evaluation . . . . . . . . . . . . 11
2.2. Usage Scenarios . . . . . . . . . . . . . . . . . . . . . 12
2.2.1. Definition and Publication of Automatable
Configuration Checklists . . . . . . . . . . . . . . 12
2.2.2. Automated Checklist Verification . . . . . . . . . . 13
2.2.3. Detection of Posture Deviations . . . . . . . . . . . 16
2.2.4. Endpoint Information Analysis and Reporting . . . . . 17
2.2.5. Asynchronous Compliance/Vulnerability Assessment at
Ice Station Zebra . . . . . . . . . . . . . . . . . . 18 17
2.2.6. Identification and Retrieval of Guidance . . . . . . 20 19
2.2.7. Guidance Change Detection . . . . . . . . . . . . . . 21 20
3. IANA Security Considerations . . . . . . . . . . . . . . . . . . . . . 21
4. Security Considerations Informative References . . . . . . . . . . . . . . . . . . . 21
5.
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 22
7. Informative References . . . . . . . . . . . . . . . . . . . 28 22
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 28 22

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 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 document,

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

o functional capabilities to inform development of an architecture
document.

Together

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, 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:

o Collecting collecting the attributes of a given endpoint;

o Making making the attributes available for evaluation and action; and

o Verifying 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 intermediary/agents, 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 intermediary/agents.
Such agents might be local intermediaries or external.
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 which 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 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 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 3rd-parties. third parties. Thus, it will tend to
be shared across large enterprises and customer communities.

In some cases 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 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 to 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 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, a
seconds
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. This Generally, this data will generally not be
shared outside an organizational boundary and will generally 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, both 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, 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 which 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 Section 2.1.2),
posture attribute value collection (see section Section 2.1.3), and posture
evaluation (see section 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 by 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).

Identify

Endpoint Targets: 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 case, the Data Query
and/or Data Retrieval building blocks (see section 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 case, the building blocks for
Endpoint Posture Attribute Value Collection (see section 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 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 Section 2.1.1) may be used to
acquire this data.

At this point point, the set of posture attribute values to use for
evaluation are known known, and they can be collected if necessary (see
section
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 a posture change or significant event on the endpoint.

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

3. A 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 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 a posture change or significant event on the endpoint.

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

3. A 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, new posture attribute
values or changes to
existing, posture attribute values. 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 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 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 an 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 checklist 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 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 insure 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
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 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
organizational
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- out of
scope for current SACM (Security Automation and Continuous
Monitoring) efforts. Irrespective of specific applications, the
availability, timeliness, and liveness of results
is 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: to identify:

* 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.

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

o Informing network access control decision making 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.

Identify

Endpoint Targets: 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 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 indicating 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
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 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. 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 continue
compliance with the security policies of the university, the
government, and the provider of the satellite network network, as well as
keep current on vulnerability testing. Interactive assessments are
therefore not reliable, and since the researchers have very limited
funding
funding, they need to minimize how much money they spend on network
data.

Prior to departure 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 vulnerabilities,
and collection is only initiated for those assets that could
potentially have a new vulnerability.

(Optional) Asset artifacts are cached in a local CMDB. 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 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. (e.g., publishing identity, create time,
modification time) and guidance data-specific criteria elements. elements specific to the guidance
data. Once the criteria is are defined, one or more security automation
data stores will need to be queried 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 to 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. IANA Considerations

This memo includes no request to IANA.

4. 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 identify 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 components, there should be considerations
for protecting the confidentiality, data integrity 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 draft. document.

Kathleen Moriarty, 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 text for various revisions
of this
draft.

7. Informative References

[RFC3444] Pras, A. and J. Schoenwaelder, "On the Difference between
Information Models and Data Models", RFC 3444, January
2003. document.

Authors' Addresses

David Waltermire
National Institute of Standards and Technology
100 Bureau Drive
Gaithersburg, Maryland 20877
USA
United States

Email: david.waltermire@nist.gov

David Harrington
Effective Software
50 Harding Rd
Portsmouth, NH New Hampshire 03801
USA
United States

Email: ietfdbh@comcast.net