Endpoint Security Posture Assessment - Enterprise Use Cases
draft-ietf-sacm-use-cases-06
The information below is for an old version of the document.
| Document | Type |
This is an older version of an Internet-Draft that was ultimately published as RFC 7632.
|
|
|---|---|---|---|
| Authors | David Waltermire , David Harrington | ||
| Last updated | 2014-03-03 | ||
| Replaces | draft-waltermire-sacm-use-cases | ||
| RFC stream | Internet Engineering Task Force (IETF) | ||
| Formats | |||
| Reviews | |||
| Additional resources | Mailing list discussion | ||
| Stream | WG state | WG Document | |
| Document shepherd | (None) | ||
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| Consensus boilerplate | Unknown | ||
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
draft-ietf-sacm-use-cases-06
Security Automation and Continuous Monitoring WG D. Waltermire
Internet-Draft NIST
Intended status: Informational D. Harrington
Expires: September 4, 2014 Effective Software
March 3, 2014
Endpoint Security Posture Assessment - Enterprise Use Cases
draft-ietf-sacm-use-cases-06
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 Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on September 4, 2014.
Copyright Notice
Copyright (c) 2014 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
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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
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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 . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Endpoint Posture Assessment . . . . . . . . . . . . . . . . . 3
2.1. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1. Define, Publish, 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 Evaluation . . . . . . . . . . . . . . . . . 11
2.1.5. Mining the Database . . . . . . . . . . . . . . . . . 12
2.2. Usage Scenarios . . . . . . . . . . . . . . . . . . . . . 12
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 . . . . . . . . . . . 16
2.2.4. Endpoint Information Analysis and Reporting . . . . . 17
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
2.2.8. Others... . . . . . . . . . . . . . . . . . . . . . . 21
3. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 21
4. Security Considerations . . . . . . . . . . . . . . . . . . . 21
5. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 21
6. Change Log . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.1. -05- to -06- . . . . . . . . . . . . . . . . . . . . . . 22
6.2. -04- to -05- . . . . . . . . . . . . . . . . . . . . . . 22
6.3. -03- to -04- . . . . . . . . . . . . . . . . . . . . . . 23
6.4. -02- to -03- . . . . . . . . . . . . . . . . . . . . . . 23
6.5. -01- to -02- . . . . . . . . . . . . . . . . . . . . . . 24
6.6. -00- to -01- . . . . . . . . . . . . . . . . . . . . . . 24
6.7. draft-waltermire-sacm-use-cases-05 to draft-ietf-sacm-
use-cases-00 . . . . . . . . . . . . . . . . . . . . . . 25
6.8. waltermire -04- to -05- . . . . . . . . . . . . . . . . . 26
7. Informative References . . . . . . . . . . . . . . . . . . . 27
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 27
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 that
support securely aggregating configuration and operational data and
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evaluating that data to determine the security posture of individual
endpoints, and, in the aggregate, the security posture of an
enterprise. 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 use cases and usage sceneries cross many IT security
information domains. From these operational use cases, we can derive
common concepts, common information expressions, functional
capabilities and requirements to guide development of vendor-neutral,
interoperable standards for aggregating and evaluating data relevant
to security posture.
Using this standard data, tools can analyze the state of endpoints,
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, but 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:
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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.
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, mutually authenticate their
identities and determine authorizations, and collect posture
attributes about the target from the intermediary/agents.
Such agents might be local or external.
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E. The application communicates target identity and (sets of)
collected attributes to an evaluator, 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 represent the
capabilities needed to support the use case. These use cases and
building blocks identify separate units of functionality that may be
supported by different components of an architectural model.
2.1.1. Define, Publish, Query and Retrieve Security Automation Data
This use case describes the need for security automation data to be
defined and published to one or more data stores, as well as queried
and retrieved from these data stores for the explicit use of posture
collection and evaluation.
Security automation data is a general concept that refers to any data
expression that may be generated and/or used as part of the process
of collecting and evaluating endpoint posture. Different types of
security automation data will generally fall into one of three
categories:
Guidance: Instructions and related metadata that guide the attribute
collection and evaluation processes. The purpose of this data
is to allow implementations to be data-driven 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. Thus, it will tend to
be shared across large enterprises and customer communities.
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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 to be
used for evaluation.
* Policies that define how to target and perform the
evaluation of a set of attributes for different kinds or
groups of endpoints and the assets they are composed of. In
some cases it may be desirable to maintain hierarchies of
policies as well.
* References to human oriented-data that provide technical,
organizational, and/or policy context. This might include
references to: best practices documents, legal guidance and
legislation, and instructional materials related to the
automation data in question.
Attribute Data: Data collected through automated and manual
mechanisms describing organizational and posture details
pertaining to specific endpoints and the assets that they are
composed of (e.g., hardware, software, accounts). The purpose
of this type of data is to characterize an endpoint (e.g.,
endpoint type, organizationally expected function/role) and to
provide actual and expected state data pertaining to one or
more endpoints. This data is used to determine what posture
attributes to collect from which endpoints and to feed one or
more evaluations.
This type of data tends to change in units of days, minutes, a
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 data will generally not be shared
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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
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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
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 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: An 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 interested in has been published
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to, updated in, or deleted from a security automation data
store which they have been authorized to access.
These building blocks are used to enable acquisition of various
instances of security automation data based on specific data models
that are used to drive assessment planning (see section 2.1.2),
posture attribute value collection (see section 2.1.3), and posture
evaluation (see section 2.1.4).
2.1.2. Endpoint Identification and Assessment Planning
This use case describes the process of discovering endpoints,
understanding their composition, identifying the desired state to
assess against, and calculating what posture attributes to collect to
enable evaluation. This process may be a set of manual, automated,
or hybrid steps that are performed for each assessment.
The building blocks of this use case are:
Endpoint Discovery: To determine the current or historic presence of
endpoints in the environment that are available for posture
assessment.
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: 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.
QUESTION: Should this include authentication of the target?
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 dependant on the component inventory, 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
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Endpoint Posture Attribute Value Collection (see section 2.1.3)
can be used.
Posture Attribute Identification: Once the endpoint targets and
component inventory is known, it is then necessary to calculate
what posture attributes are required to be collected to perform
the evaluation. 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.
QUESTION: Are we missing a building block that determines what
previously collected data, if any, is suitable for evaluation and
what data needs to be actually collected? Should a building block be
identified that evaluates existing data to determine if it is current
enough for use in the evaluation or if current data should be
collected anyway according to a policy?
COMMENT(DR): Probably yes, taking into account usage scenarios like
2.2.2, 2.2.3 which rely on historical 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 is detected).
3. Due to 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.
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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 Evaluation
This use case describes the process of evaluating collected posture
attribute values representing actual endpoint state against the
expected state selected for the assessment. This use case can be
initiated by a variety of triggers including:
1. A posture change or significant event on the endpoint.
2. A network event (e.g., endpoint connects to a network/VPN,
specific netflow is detected).
3. Due to a scheduled or ad hoc evaluation task.
The building blocks of this use case are:
Posture Attribute Value Query: If previously collected posture
attribute values are needed, the appropriate data stores are
queried to retrieve them. 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 results as expressed in the
specified guidance. The result of this comparison is output as
a set of posture evaluation results.
QUESTION: What if data is unavailable or is not current enough
to support the evaluation? This could be caused if collection
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did not occur (for some reason) and previous collection was too
old.
Completion of this process represents a complete assessment cycle as
defined in Section 2.
QUESTION: Since this indicates completion of the section 2 process, I
would expect section 3 to follow. But section continues with 2.1.5?
2.1.5. Mining the Database
This use case describes the need to analyze previously collected
posture attribute values from one or more endpoints. This is an
alternate use case to Posture Evaluation (see section 2.1.4) that
uses collected posture attributes values for analysis processes that
may do more than evaluating expected vs. actual state(s).
The building blocks of this use case are:
Query: Query a data store for specific posture attribute values.
Change Detection: An operator should have a mechanism to detect the
availability of new or changes to existing posture attribute
values. 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.
QUESTION: Does this warrant a separate use case, or should this be
incorporated into the previous use case?
COMMENT(DBH): I think the 2.1.5 use case is a subset of 2.1.4 use
case, specifically, the query of existing data is covered in Posture
Attribute Value Query, condition 1. I think Posture Attribute Value
Query should be modified to include the change detection, as part of
establishing what needs to be queried.
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.
COMMENT(DBH): I don't see "Search for signs of Infection",
"Vulnerability Endpoint Identification", "Compromised Endpoint
Identification", and "Suspicious Endpoint behavior", which were in
-04-. They were moved into "Automated Checklist Verification". But
the original usage scenarios did not mention checklists. Are we now
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limiting SACM to a checklist-driven approaches? Do the authors of
the text in -04- agree that their use cases/usage scenarios are
adequately captured in -05-?
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 devices 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 checklist to a public security
automation data store that customers can query to retrieve applicable
checklist for their deployed specialized endpoint devices.
Automatable configuration checklist 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.
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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 insure
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.
Using the collected hardware and software inventory data and
associated asset characterization data that may indicate the
organizational defined functions of each endpoint, checklist guidance
is queried, located and downloaded from the appropriate vendor and
3rd-party security automation data store for the appropriate
checklists. This 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. The results of this
evaluation are provided to appropriate operators and applications to
drive additional business logic.
Checklists could include searching for indicators of compromise on
the endpoint (e.g., file hashes); identifying malicious activity
(e.g. command and control traffic); detecting presence of
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unauthorized/malicious software, hardware, and configuration items;
and other indicators.
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.
Checklists could also come from sources other than the application or
OS vendor, such as industry groups or regulatory authorities, or
enterprises could develop their own checklists.
While specific applications for checklists results are out-of-scope
for current SACM efforts, how the data is used may illuminate
specific latency and bandwidth requirements. For this purpose use of
checklist assessment results may include, but are not limited to:
o Detecting endpoint posture deviations as part of a change
management program to include changes to hardware and software
inventory including patches, changes to configuration items, and
other posture aspects.
o Determining compliance with organizational policies governing
endpoint posture.
o Searching for current and historic signs of infection by malware
and determining the scope of infection within an enterprise.
o Informing configuration management, patch management, and
vulnerability mitigation and remediation decisions.
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.
Identify Endpoint Targets: 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.
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Posture Attribute Identification: To determine what data needs to be
collected to support evaluation, the checklist is evaluated
against the component inventory and other endpoint metadata to
determine the set of posture attribute values that are needed.
Collection Guidance Acquisition: Based on the identified posture
attributes, the application will query appropriate security
automation data stores to find the "applicable" collection
guidance for each endpoint in question.
Posture Attribute Value Collection: For each endpoint, the values
for the required posture attributes are collected.
Posture Attribute Value Query: If previously collected posture
attribute values are used, they are queried from the
appropriate data stores for the target endpoint(s).
Evaluation Guidance Acquisition: Any guidance that is needed to
support evaluation is queried and retrieved.
Posture Attribute Evaluation: The resulting posture attribute values
from previous Collection processes are evaluated using the
evaluation guidance to provide a set of posture results.
2.2.3. Detection of Posture Deviations
Example corporation has established secure configuration baselines
for each different type of endpoint within their enterprise
including: network infrastructure, mobile, client, and server
computing platforms. These baselines define an approved list of
hardware, software (i.e., operating system, applications, and
patches), and associated required configurations. When an endpoint
connects to the network, the appropriate baseline configuration is
communicated to the endpoint based on its location in the network,
the expected function of the device, and other asset management data.
It is checked for compliance with the baseline indicating any
deviations 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
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specific changes in posture allowing assessment of the delta to be
performed instead of a full assessment 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 are queried from the appropriate data stores
for the target endpoint(s).
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QUESTION: Should we include other building blocks here?
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 continue
compliance with the security policies of the university, the
government, and the provider of the satellite network as well as keep
current on vulnerability testing. Interactive assessments are
therefore not reliable, and since the researchers have very limited
funding they need to minimize how much money they spend on network
data.
Prior to departure they register all equipment with an asset
management system owned by the university, which will also initiate
and track assessments.
On a periodic basis -- either after a maximum time delta or when the
security automation data store has received a threshold level of new
vulnerability definitions -- the university uses the information in
the asset management system to put together a collection request for
all of the deployed assets that encompasses the minimal set of
artifacts necessary to evaluate all three security policies as well
as vulnerability testing.
In the case of new critical vulnerabilities this collection request
consists only of the artifacts necessary for those vulnerabilities
and collection is only initiated for those assets that could
potentially have a new vulnerability.
[Optional] Asset artifacts are cached in a local 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 expose is calculated
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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 in the same way.
COMMENT(DBH): Why "in the same way"? Maybe results could be
handled in a different way.
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.
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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 guidance data-specific criteria elements.
Once the criteria is defined, one or more security automation data
stores will need to be queried 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 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.
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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 alerts to changes 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 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.
2.2.8. Others...
Additional usage scenarios will be identified as we work through
other domains.
3. IANA Considerations
This memo includes no request to IANA.
4. Security Considerations
This memo documents, for Informational purposes, use cases for
security automation. While it is about security, it does not affect
security.
5. Acknowledgements
The National Institute of Standards and Technology (NIST) and/or the
MITRE Corporation have developed specifications under the general
term "Security Automation" including languages, protocols,
enumerations, and metrics.
Adam Montville edited early versions of this draft.
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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 use cases text for various revisions of this
draft.
6. Change Log
6.1. -05- to -06-
Updated the "Introduction" section to better reflect the use case,
building block, and usage scenario structure changes from previous
revisions.
Updated most uses of the terms "content" and "content repository" to
use "guidance" and "security automation data store" respectively.
In section 2.1.1, added a discussion of different data types and
renamed "content" to "data" in the building block names.
In section 2.1.2, separated out the building block concepts of
"Endpoint Discovery" and "Endpoint Characterization" based on mailing
list discussions.
Addressed some open questions throughout the draft based on consensus
from mailing list discussions and the two virtual interim meetings.
Changed many section/sub-section names to better reflect their
content.
6.2. -04- to -05-
Changes in this revision are focused on section 2 and the subsequent
subsections:
o Moved existing use cases to a subsection titled "Usage Scenarios".
o Added a new subsection titled "Use Cases" to describe the common
use cases and building blocks used to address the "Usage
Scenarios". The new use cases are:
* Define, Publish, Query and Retrieve Content
* Endpoint Identification and Assessment Planning
* Endpoint Posture Attribute Value Collection
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* Posture Evaluation
* Mining the Database
o Added a listing of building blocks used for all usage scenarios.
o Combined the following usage scenarios into "Automated Checklist
Verification": "Organizational Software Policy Compliance",
"Search for Signs of Infection", "Vulnerable Endpoint
Identification", "Compromised Endpoint Identification",
"Suspicious Endpoint Behavior", "Traditional endpoint assessment
with stored results", "NAC/NAP connection with no stored results
using an endpoint evaluator", and "NAC/NAP connection with no
stored results using a third-party evaluator".
o Created new usage scenario "Identification and Retrieval of
Repository Content" by combining the following usage scenarios:
"Repository Interaction - A Full Assessment" and "Repository
Interaction - Filtered Delta Assessment"
o Renamed "Register with repository for immediate notification of
new security vulnerability content that match a selection filter"
to "Content Change Detection" and generalized the description to
be neutral to implementation approaches.
o Removed out-of-scope usage scenarios: "Remediation and Mitigation"
and "Direct Human Retrieval of Ancillary Materials"
Updated acknowledgements to recognize those that helped with editing
the use case text.
6.3. -03- to -04-
Added four new use cases regarding content repository.
6.4. -02- to -03-
Expanded the workflow description based on ML input.
Changed the ambiguous "assess" to better separate data collection
from evaluation.
Added use case for Search for Signs of Infection.
Added use case for Remediation and Mitigation.
Added use case for Endpoint Information Analysis and Reporting.
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Added use case for Asynchronous Compliance/Vulnerability Assessment
at Ice Station Zebra.
Added use case for Traditional endpoint assessment with stored
results.
Added use case for NAC/NAP connection with no stored results using an
endpoint evaluator.
Added use case for NAC/NAP connection with no stored results using a
third-party evaluator.
Added use case for Compromised Endpoint Identification.
Added use case for Suspicious Endpoint Behavior.
Added use case for Vulnerable Endpoint Identification.
Updated Acknowledgements
6.5. -01- to -02-
Changed title
removed section 4, expecting it will be moved into the requirements
document.
removed the list of proposed capabilities from section 3.1
Added empty sections for Search for Signs of Infection, Remediation
and Mitigation, and Endpoint Information Analysis and Reporting.
Removed Requirements Language section and rfc2119 reference.
Removed unused references (which ended up being all references).
6.6. -00- to -01-
o Work on this revision has been focused on document content
relating primarily to use of asset management data and functions.
o Made significant updates to section 3 including:
* Reworked introductory text.
* Replaced the single example with multiple use cases that focus
on more discrete uses of asset management data to support
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hardware and software inventory, and configuration management
use cases.
* For one of the use cases, added mapping to functional
capabilities used. If popular, this will be added to the other
use cases as well.
* Additional use cases will be added in the next revision
capturing additional discussion from the list.
o Made significant updates to section 4 including:
* Renamed the section heading from "Use Cases" to "Functional
Capabilities" since use cases are covered in section 3. This
section now extrapolates specific functions that are needed to
support the use cases.
* Started work to flatten the section, moving select subsections
up from under asset management.
* Removed the subsections for: Asset Discovery, Endpoint
Components and Asset Composition, Asset Resources, and Asset
Life Cycle.
* Renamed the subsection "Asset Representation Reconciliation" to
"Deconfliction of Asset Identities".
* Expanded the subsections for: Asset Identification, Asset
Characterization, and Deconfliction of Asset Identities.
* Added a new subsection for Asset Targeting.
* Moved remaining sections to "Other Unedited Content" for future
updating.
6.7. draft-waltermire-sacm-use-cases-05 to draft-ietf-sacm-use-cases-00
o Transitioned from individual I/D to WG I/D based on WG consensus
call.
o Fixed a number of spelling errors. Thank you Erik!
o Added keywords to the front matter.
o Removed the terminology section from the draft. Terms have been
moved to: draft-dbh-sacm-terminology-00
o Removed requirements to be moved into a new I/D.
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o Extracted the functionality from the examples and made the
examples less prominent.
o Renamed "Functional Capabilities and Requirements" section to "Use
Cases".
* Reorganized the "Asset Management" sub-section. Added new text
throughout.
+ Renamed a few sub-section headings.
+ Added text to the "Asset Characterization" sub-section.
o Renamed "Security Configuration Management" to "Endpoint
Configuration Management". Not sure if the "security" distinction
is important.
* Added new sections, partially integrated existing content.
* Additional text is needed in all of the sub-sections.
o Changed "Security Change Management" to "Endpoint Posture Change
Management". Added new skeletal outline sections for future
updates.
6.8. waltermire -04- to -05-
o Are we including user activities and behavior in the scope of this
work? That seems to be layer 8 stuff, appropriate to an IDS/IPS
application, not Internet stuff.
o I removed the references to what the WG will do because this
belongs in the charter, not the (potentially long-lived) use cases
document. I removed mention of charter objectives because the
charter may go through multiple iterations over time; there is a
website for hosting the charter; this document is not the correct
place for that discussion.
o I moved the discussion of NIST specifications to the
acknowledgements section.
o Removed the portion of the introduction that describes the
chapters; we have a table of concepts, and the existing text
seemed redundant.
o Removed marketing claims, to focus on technical concepts and
technical analysis, that would enable subsequent engineering
effort.
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o Removed (commented out in XML) UC2 and UC3, and eliminated some
text that referred to these use cases.
o Modified IANA and Security Consideration sections.
o Moved Terms to the front, so we can use them in the subsequent
text.
o Removed the "Key Concepts" section, since the concepts of ORM and
IRM were not otherwise mentioned in the document. This would seem
more appropriate to the arch doc rather than use cases.
o Removed role=editor from David Waltermire's info, since there are
three editors on the document. The editor is most important when
one person writes the document that represents the work of
multiple people. When there are three editors, this role marking
isn't necessary.
o Modified text to describe that this was specific to enterprises,
and that it was expected to overlap with service provider use
cases, and described the context of this scoped work within a
larger context of policy enforcement, and verification.
o The document had asset management, but the charter mentioned
asset, change, configuration, and vulnerability management, so I
added sections for each of those categories.
o Added text to Introduction explaining goal of the document.
o Added sections on various example use cases for asset management,
config management, change management, and vulnerability
management.
7. Informative References
[RFC3444] Pras, A. and J. Schoenwaelder, "On the Difference between
Information Models and Data Models", RFC 3444, January
2003.
Authors' Addresses
David Waltermire
National Institute of Standards and Technology
100 Bureau Drive
Gaithersburg, Maryland 20877
USA
Email: david.waltermire@nist.gov
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David Harrington
Effective Software
50 Harding Rd
Portsmouth, NH 03801
USA
Email: ietfdbh@comcast.net
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