Security Automation and Continuous Monitoring M. Hansbury
Internet-Draft D. Haynes
Intended status: Informational The MITRE Corporation
Expires: May 12, 2016 J. Gonzalez
Department of Homeland Security
November 9, 2015

OVAL and the SACM Information Model


The OVAL community has spent more than ten years developing and employing the OVAL Language. During this time, the community has made a number of design decisions and learned a number of lessons that should be leveraged as next generation endpoint posture assessment is formulated. There are a number of places throughout the SACM Information Model document that could be fulfilled by portions of the OVAL Language, either in its current state or, in some cases, with modifications. Another output of the work executed under the OVAL project is a number of lessons that are applicable to the SACM work. These lessons include a clear separation of data collection and evaluation; a call to focus on ensuring both primary source vendors and third party security experts feel invited to the discussion and are empowered to leverage their unique domain knowledge; and to strive for simplicity and flexibility, where possible. Finally, the OVAL community has a set of clear recommendations with respect to which parts of OVAL should be used by SACM as a means to make best use of the efforts of those that have worked on and supported OVAL over the past ten years. Those recommendations are:

Status of This Memo

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Table of Contents

1. Introduction

The Security Automation and Continuous Monitoring (SACM) IETF Working Group [SACM] has been chartered with standardizing the mechanisms by which endpoint security assessment is performed. This includes software inventory, compliance and vulnerability management, and other related activities. The Working Group has created a series of artifacts [SACM-DOCUMENTS] to capture the important concepts required to accomplish this goal. In addition to Use Cases, Requirements, and Architecture documents, the Working Group has created an initial draft of an Information Model that describes the high-level components and concepts that fulfill the already defined requirements.

This white paper discusses how the Open Vulnerability and Assessment Language (OVAL) [OVAL-WEBSITE] can be leveraged in order to implement the Information Model defined by the SACM group. This paper is not meant to suggest that the entire OVAL Data Model could-or even should-be supported by SACM; rather, it breaks apart the various components of the OVAL Language and discusses how each could be used to satisfy parts of the Information Model.

This document assumes that the reader is already familiar with OVAL and its structures. For those readers that require more in-depth information about OVAL, please review the OVAL Tutorial documentation [OVAL-DEFINITION-TUTORIAL] and other related documentation on the OVAL website. This document describes how these structures can be thought of as data models whose scopes and activities overlap with the SACM Information Model.

Additionally, in later sections, the paper presents lessons learned from the ten plus years of OVAL development and curation, as well as related gaps.

1.1. Requirements Language

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119 [RFC2119].

2. SACM Information Model

The information model defined by the SACM Working Group captures the types of objects and data required to fulfill the defined SACM Requirements [I-D.ietf-sacm-requirements]. It additionally provides details on the flow of data to and from the different objects in the system, in conjunction with the SACM Architecture document [I-D.ietf-sacm-architecture]. The document describes all of these things in a protocol and data format neutral manner.

The document provides descriptions of the various components that are required to perform endpoint assessments, along with some usage scenarios, and the potential mapping from OVAL to any of these defined components wherever OVAL may be relevant.

3. OVAL Language

The OVAL Language is made up of several parts, each responsible for encapsulating a part of the assessment model. Each part is discussed briefly below [STRUCTURE-OF-OVAL].

Note: A word about Core vs. Platform Extensions. OVAL can be broadly split into Core structures, which are those that are foundational and give the overall structure to the OVAL Language, and the Platform Extensions, which are platform-specific structures that extend the Core in order to provide ways to encode the underlying low-level, platform-specific tests used by OVAL Content. This paper is chiefly focused on mapping the Core into the SACM Information Model.

In a similar fashion, while thinking about how to implement the SACM Information Model, two distinct levels must be considered:

  1. Platform-agnostic, high level concepts
  2. Platform-specific concepts

3.1. Core OVAL Data Models

There are a number of models defined as part of OVAL. This section discusses the three most important models.

3.1.1. OVAL Definitions Model

The Definitions Model is the central component of the OVAL Language. The structures in this model allow an author to encode what data to collect, the expected values for the data, and the rules by which to evaluate that data. This represents one of the chief limitations of the OVAL Language: Since an author must include both what data must be collected, and how that data is to be assessed in an OVAL Definition, these two related-but separate-elements are directly coupled to one another. For more information, see the related "Collection and Evaluation Must Be De-coupled" section below.

It is in the OVAL Definitions Model that the Definition object is defined. A Definition is the root element for any OVAL check. It contains a set of criteria, either simple or complex, to define how the check should operate. In addition, the OVAL Definitions Model defines the base structures that are used by the Platform Extensions to extend OVAL, as well as Functions, and other high-level concepts.

3.1.2. OVAL System Characteristics Model

The OVAL System Characteristics Model defines structures to encode the actual data that is collected. It provides basic structures for capturing this data, including the Item, which is the base structure for recording collected data in OVAL. It also provides structures for capturing information about the endpoint from which the data was collected, including OS information, endpoint identification information (such as IP and MAC addresses), and other relevant endpoint metadata.

3.1.3. OVAL Results Model

Finally, OVAL provides a third model to encode the results of the assessment: the OVAL Results Model. In addition to providing structures to capture essential information about the evaluation results, such as the overall results of each assessment and when the assessment occurred, the model also provides ways to include both the guidance (Definitions) and collected data (System Characteristics) used for the assessments. This model provides a comprehensive way to capture not only the results themselves, but also the information used to determine the result.

3.2. Additional Core OVAL Data Models

Additional data models are defined that support specific capabilities and are sometimes useful in conjunction with the OVAL models previously discussed. The models discussed in this section are not intended to stand alone, and require the use of one or more of the core OVAL models.

3.2.1. OVAL Common Model

The Common Model is a very simple collection of global building blocks, such as enumerations used throughout the other models, along with some other foundational pieces. Common values are defined in this model once and then applied within other OVAL models, thus reducing redundancy between each OVAL data model. Examples of the elements provided by the OVAL Common Model are enumerations that provide useful value sets for use within OVAL, such as family types ("windows", "unix", etc.), data types (e.g., "string," "boolean," "int," etc.), and class types (e.g., "vulnerability," "compliance," etc.).

3.2.2. OVAL Variables Model

The OVAL Variables Model provides a simple framework for externally specifying variable values to an OVAL Definitions document at runtime.

3.2.3. OVAL Directives Model

The OVAL Directives Model provides a very simple model with structures to indicate the level of detail that should be present in an OVAL Results document.

4. Relating the OVAL Models to the SACM Information Model

The following section discusses each piece of the SACM Information Model, where one or more OVAL models can be used to implement the piece, wholly or in part.

4.1. Attribute Collector

The SACM Information Model defines both Internal and External Attribute Collectors. Both are components that perform the collection of posture information about an endpoint. The Information Model lists a number of examples of Collectors such as Network Intrusion Detection Systems (NIDS), NEA posture collectors, and vulnerability scanners. While OVAL is not directly applicable for some types of Attribute Collectors such as NIDS, it is certainly applicable for items such as configuration or vulnerability scanners.

An Attribute Collector needs to be instructed as to what specific posture attributes must be collected, when or how often those attributes must be collected, and how to share the collected attributes. In some cases, an Attribute Collector may simply collect data and directly respond to the caller with the required results. In others, it may need to execute the data collection at a future time or at some interval, and thus will need to know how to share the collected data. The OVAL Language does not provide any mechanism for instructing tools where to send collected data, but the OVAL Definitions Model can (among other things) encode what data must be collected; however, it does not allow (as currently constructed) for providing any notion of what constitutes valid data collection (i.e., how recent data must be to be considered acceptable, and how and where it was collected).

Additionally, the OVAL Definitions Model could be modified to support monitoring of events. As it is today, OVAL doesn't have any explicit way to include this instruction, but it would be simple to modify the model to include this notion.

The OVAL System Characteristics Model allows the encoding of collected information and can be used to implement a data format for sharing collected data. While OVAL does not require that tools store data using a standardized format (though they are free to do so), a standardized format is required to allow tools to share data. The OVAL System Characteristics Model provides a standardized way to encode this information.

4.2. Evaluator

An Evaluator is the component that analyzes inputs such as Posture Attributes and Evaluation Guidance to determine the result of a particular assessment. It is the piece that answers a question about the security posture of one more endpoints. The Evaluator must be able to ingest inputs of various types, understand the question or questions asked of it, and analyze the inputs to make a determination.

In this case, OVAL could be used to provide several of the required inputs to an Evaluator. The format defined in the OVAL Definitions Model could be used to express Evaluation Guidance. Note that when mapping the OVAL Definitions Data Model to the SACM Information Model, it is important to distinguish between Collection and Evaluation within the OVAL Definitions Model. The OVAL Definitions Model structures currently combine both the Collection ("what to collect") and Evaluation ("what the data should look like"). One of the key concepts within the SACM Information Model is that Collection and Evaluation should be separate concepts. Nonetheless, OVAL contains building blocks that could be modified to satisfy this need.

Additionally, the structures defined in the OVAL System Characteristics Model could be used to encode the Posture Attributes input to the Evaluator. Finally, when the Evaluator makes its determination and must encode the result of the assessment, the OVAL Results Model structures could be used as well.

4.3. Endpoint Attribute Assertion

According to the SACM Information Model, an Endpoint Attribute Assertion is a way to indicate that a specified set of posture attributes or events were present on an endpoint during a specific interval of time. For example, an Assertion could be made that a particular Windows server had the following attributes from 1/1/2015 - 1/8/2015:

OVAL does not have a direct corollary to this construct; however, the structures defined by the OVAL System Characteristics Model could provide a base from which such a construct could be built. The System Characteristics Data Model is designed to capture posture attributes, and as such, could be extended or modified to include the concept of a time interval.

Additionally, it is important to note that the SACM Information Model also states that Events can be included within an Endpoint Attribute Assertion. While "event" and "attribute" are often used interchangeably, in the SACM Information Model, these two concepts are considered distinct. The distinction is that an "event" is something that has a value that does not change until something causes a change, whereas an "attribute" is something that occurs at a moment in time. The Endpoint Attribute Assertion deals with both posture attributes and events during a time interval. No special treatment is given to Events within OVAL as it is currently constructed, although, as stated previously, adding a time interval to support Events is simple to do.

4.4. Evaluation Result

An Evaluation Result is the representation of the analysis of a given set of Posture Attributes against Evaluation Guidance. The OVAL Results Model structures can be used to encode one or more Evaluation Results.

4.5. Collection Guidance

Within the SACM Information Model, Collection Guidance is defined as information that describes which Posture Attributes must be collected from one or more endpoints. It is the means by which an Attribute Collector determines what information it must collect, as well as when that information must be collected (including intervals for repeated collection activities).

The OVAL Definitions Model provides structures capable of expressing information about what data must be collected for an assessment. It is important to note that the method by which the OVAL Definitions Model accomplishes this will not necessarily directly apply to the SACM Information Model in its current state. In many cases, which specific posture attributes should be collected is not distinct from its evaluation guidance. For the OVAL Definitions Model to be used to implement the SACM Information Model, work would need to be undertaken to de-couple these concepts.

While the model provides the ability to encode details such as what data must be collected from the endpoint, it does not currently provide the ability to include information such as collection interval. The model can be extended, however, to add this capability. Adding the concept of an "interval" to the model to capture the concept may be a way to accomplish this goal.

Important Note: One of the key drawbacks to OVAL is that Platform Extensions (using the OVAL Definitions Model as a base) must be created for each platform and data source to capture any Posture Attributes that must be collected for a given platform and data source. As a result, it is not easy or scalable to create or update extensions for rapidly changing platforms and products in a timely manner.

With this in mind, it is important that any use of the OVAL Definitions Model to satisfy Collection Guidance for SACM should warrant consideration of updates that change this from a solution where the low-level platform details are part of the language itself, to one where the format provides a way for domain experts (ideally primary source vendors) to instruct tools what Posture Attributes to collect.

This also applies to the next section (Evaluation Guidance).

4.6. Evaluation Guidance

The Evaluation Guidance component contains the information that directs an Evaluator how to perform one or more assessments based on collected data. Evaluation Guidance must direct the Evaluator on what the expected state of collected data should be. Additionally, it must be able to specify desired characteristics of the data. That is, it must be able to not only cite the specific posture attributes under evaluation, but also to specify characteristics such as the type of tool that was used to collect the data, how old the data is, etc.

The Evaluator must then ingest this guidance, locate the required data-whether locally or remotely available-and then execute the analysis required.

OVAL offers the OVAL Definitions Model to provide the structures for encoding the expected state or values for the collected data. The OVAL Language does not currently provide a way to specify the expected characteristics of the data, but the OVAL Definitions Model could be augmented to include this type of information. Alternatively, the concept could be added elsewhere and re-used as appropriate. Allowing for characteristics information will be important to allow evaluation to do things like only query data if it's been collected within the past x days or only query data that is collected by a credentialed scan.

Again, as Collection and Evaluation are intertwined currently in OVAL Language, some work will be required to de-couple them for use with the Evaluation Guidance component.

4.7. Report Generator

Within the SACM Information Model, a Report Generator is a component that constructs a collection of artifacts such as Endpoint Attribute Assertions, Evaluation Results, etc. The reports that are generated by this component can be used to either report on a collection of assessments, or to provide a summary of previously run assessments or queries.

The structures defined in the OVAL Results Model could be used by the Report Generator as a means to encode the required report information. The OVAL Results structures can be used to encode Evaluation Results, although it would need to be extended to include Endpoint Attribute Assertions; as such, a construct does not exist within OVAL today.

4.8. Report

A Report is the tangible output from the Report Generator. It contains formatted information that satisfies one or more assessments or queries. A data format that implements the Report component must be able to support Evaluation Results, Endpoint Attribute Assertions, Events, and other related reporting data.

As mentioned in the Report Generator section, the OVAL Results Model structures can be used to encode some of these details today, and can be extended to handle Events and Endpoint Attribute Assertions. It is important to note that while OVAL provides this capability, historically, the OVAL Results Model structures have been considered by many to be too verbose to be used in a large scale enterprise environment. These structures could be revised to allow more granular and flexible ways to encode this information in order to satisfy the needs of SACM, or a different data model could be used to implement this capability.

4.9. Provenance

Within the SACM Information Model, Provenance is defined as a metadata item that contains information such as the time when an artifact is produced, what produced it, the policies that govern it, and the method used to produce it.

Within the OVAL Common Model, a Generator structure is defined to express both what created the content, and when it was created. While the purpose of this structure does not meet all the needs for Provenance in SACM, it could be used as a building block to achieve this goal. This structure would need to be extended to include the policy information and the method used to produce a given artifact, but the base structure is in place.

5. SACM Constructs with No OVAL Mapping

Finally, while there are many similarities between what is defined by the SACM Information Model and that supported by OVAL models, there are some things discussed in the SACM Information Model document that are either different from-or not supported within-OVAL.

5.1. Tasking

The SACM Information Model discusses Tasks in a few places, including the Collector, Evaluator, and Reporting sections. Tasks represent of notion of "do something at this time." OVAL does not support any notion of a tasking model as currently defined.

While the OVAL Definitions Model (or some derivative) could be referenced by a model that captures tasking, it may be difficult to support all of the needs of tasking in this way. Tasking may already be well defined by another, existing model, and if so, it might be best to leverage that existing work.

5.2. Event-driven Actions

Within the SACM Information Model, in addition to posture attributes, events are also often part of the data collection activities. Events are discussed as both part of an Endpoint Attribute Assertion, and an Endpoint Attribute Collector. In each case, it is clear that, in addition to the collection of posture attribute data, event data must also be taken into account.

The OVAL Language does not have any notion of capturing events directly. It is constructed to allow the representation of Posture Attribute data within the OVAL System Characteristics Model, but event data is absent from that model. OVAL can be modified to support Events in large part by simply extending it to include a time interval.

5.3. User and Authorization

The Information Model talks about Users (i.e., one or more end users or roles) and Authorizations (i.e., their authority to undertake actions). While OVAL includes some entities that may relate to these types of concepts, they appear in very specific low-level tests like Windows and UNIX user-related tests. OVAL lacks any general concept of Users or Authorizations that could be applied across its core data structures. The recommendation is to integrate an external solution into relevant OVAL models to achieve required capabilities in this area.

5.4. Location

Similar to Users and Authorization, Locations are defined in the Information Model. Locations include authentication points, wall-jacks to which an endpoint is connected, geographical location, etc.

Again, as for Users and Authorization, the recommendation is for the relevant OVAL models to be integrated with other solutions to meet these requirements.

6. Lessons Learned and Gaps

Over the course of ten-plus years in moderating the OVAL project, those involved in the project have released over 15 distinct versions of the Language, 25 versions of the OVAL Interpreter, and have processed over 25,000 OVAL Definitions in the OVAL Repository. In addition, the team has spent a lot of time interacting with security tool vendors, researchers, primary source vendors, and commercial and government end users, discussing their needs and struggles. As such, the following lessons learned are presented to help ensure that the collective experience of the group is shared with the larger community.

In addition to a description of the lesson, each also has a suggested application for the SACM work.

6.1. Simplicity is Key

6.1.1. Lesson

Endpoint assessment covers a broad set of activities. From organization to organization, assessment has different meanings, and what is "good enough" for one group, barely scratches the surface for another. Experience suggested that caution must be used to avoid unnecessary complexity as a means to address this diversity.

The team has seen that when information sharing is required across diverse parties, the simpler the exchange mechanism design, the more successful the sharing effort will be.

6.1.2. SACM Implications

Review both the diversity of the different organizations that are sharing information within the SACM framework, and the types and volume of information that must be shared. Include only the information that is required to successfully implement the desired Use Cases.

6.2. Collection and Evaluation Must Be De-coupled

6.2.1. Lesson

As OVAL - and the security automation space in general - has evolved, it has become clear that the close coupling found in OVAL between the OVAL Object and OVAL State (i.e., what to collect and what the collected data should look like) is an undesirable feature. By forcing these two concepts into a single model, the Language does not easily allow for dynamic querying of previously collected data, nor does it easily allow for efficiencies in data collection.

6.2.2. SACM Implications

Keep the mechanism by which data is collected and evaluated separate.

6.3. Keep Separate Core and Extensions

6.3.1. Lesson

OVAL, by design, must be frequently updated to keep up with new and expanding sets of assessment platforms. However, tool vendors incurred great cost in updating to new versions of the Language, including implementing new tests in the updated version, as well as general quality testing, updating release and deployment, etc.

As the project matured, so too did the Core Models that define the building blocks for endpoint assessment. Over the past few years, the Core Models rarely changed-in some cases, going years without any required update. The Platform Extension Models, however, will always require a frequent revision cycle, and often were out of date very quickly. Despite the fact that these two models had distinct release cycle requirements-one continually getting longer in the Core Models, and one requiring agility in the Platform Extensions-a full release of both was required to include changes to any part of the OVAL Language.

6.3.2. SACM Implications

SACM should focus on providing the foundational building blocks that allow those that know best to express what data must be collected to assess an endpoint. The SNMP standard [RFC1157] could be used as a model for this type of separation. SNMP defines the building blocks for sharing information about network devices, but defers the low-level details of this information sharing to those that best understand the products via Management Information Bases (MIBs). While this is not a perfectly analogous model for the SACM work, this clean separation of core building blocks and protocols from the low-level details of products should be emulated, if possible.

6.4. Empower Subject Matter Experts

6.4.1. Lesson

As the security automation field has matured, more primary source vendors and other subject matter experts have taken increased responsibility in ownership of how their products are assessed. This step in maturity is critical and, within OVAL, as these vendors have become more involved, the quality in tests available to tools and end users has greatly increased.

6.4.2. SACM Implications

Ensure that usage of SACM means that those that best understand the component being assessed are empowered to instruct what data must be collected for the assessment, along with the meaning of this data. As much as possible, keep the mechanism by which this information is conveyed as simple as possible to ensure that it is as easy as possible for subject matter experts to participate.

6.5. Carrots Work Better than Sticks

6.5.1. Lesson

As much as possible, ensure that usage and compliance with the defined standards is encouraged by offering primary source vendors and subject matter experts incentive to do so. Forced compliance typically encourages organizations to do the least possible, and does not entice them to continually stay engaged.

6.5.2. SACM Implications

Find ways to encourage participation that drives long term engagement and willing participation. Engage with vendors to understand their problems and, where possible, construct SACM use cases and requirements that not only address the needs of the SACM end users, but also those of the vendors. Build a compelling story for use of SACM that not only shows value to end users, but shows a clear return on investment for vendors.

6.6. Use Caution Defining Data Collection

6.6.1. Lesson

When providing information about what data must be collected as part of an assessment, it can be quite easy to provide this information in a way that dictates how to collect the required data. Doing so can limit innovation and architectural choices for organizations implementing security automation tools.

On the other hand, it is not always feasible to express what data must be collected without implying or instructing specific data collection mechanisms. Over the years, there have been a few cases where the OVAL community could not agree on significant issues related to data collection. Discussions on whether to allow open scripting in the Language and how best to support both third party and primary source contributions were very challenging. With good arguments on both sides of these issues, it was difficult to achieve consensus.

6.6.2. SACM Implications

This will be one of the bigger challenges for SACM to navigate. SACM must allow those that best understand platforms and products to instruct what data must be collected for assessment. At the same time, third party support will be critical in some cases as well, and allowances must be made for this.

Additionally, deciding how many, if any, collection methods are allowed as part of the collection instructions will be challenging. Again, a balance should be struck to best allow clarity in data collection instructions, without limiting innovation and product-specific decisions.

6.7. Perspective Matters

6.7.1. Lesson

When evaluating collected posture attributes, it is important to be able to include additional context to this evaluation in some cases. For example, the method by which data was collected could be an important piece of information when performing evaluation. If the scanner was a remote, unauthorized scanner of an endpoint, it is entirely possible that the scanner could not properly scan for a number of posture attributes. If, however, the scanner ran locally on the endpoint as an administrative user, it is much more likely that it accurately collected posture attributes from the endpoint.

Other examples of this type of perspective and context include how old the collected data is, and whether the scanner was active or passive.

6.7.2. SACM Implications

Ensure information that provides necessary context can be provided as part of data collection, thereby allowing context-based decisions to be made.

6.8. Flexible Reporting Fidelity is Important

6.8.1. Lesson

After data collection and evaluation is complete, the results of the evaluation must be shared, often with multiple parties, and in multiple ways. It is important to provide a reasonable amount of flexibility with respect to what levels of fidelity are allowed with results. While OVAL did try to achieve a reasonable amount of flexibility with reporting fidelity, challenges still exist.

6.8.2. SACM Implications

As much as possible, allow the end users of the reporting capability to determine exactly what level of fidelity they need to achieve their goals.

6.9. Evaluation Guidance is Platform-Specific

6.9.1. Lesson

In the early days of OVAL, initial adoption of the effort was spearheaded by third party security vendors, as opposed to the primary source vendors for software. As the effort matured, more primary source vendors became involved and adopted OVAL in some way. It quickly became evident that, while third party vendors made great strides in determining how to evaluate the security posture of many platforms and products, understanding the best way to evaluate is hard, and very platform-specific. Additionally, OVAL content is costly to create, even for seasoned content authors, due to the need to understand these very low-level product and platform complexities.

6.9.2. SACM Implications

As cited above, the primary source vendors are best suited to provide evaluation guidance. It is very challenging for third party organizations to truly understand platform-specific evaluation. Empower primary source vendors and other subject matter experts by providing simple and effective ways to provide this information. Also, as discussions on complex topics arise, engage these primary source vendors to understand their valuable views.

7. Recommendations

In order to successfully standardize the mechanisms by which endpoint posture assessment is performed, the following recommendations are offered to SACM for consideration.

7.1. Use the OVAL System Characteristics Model for Encoding Collection Data

The OVAL System Characteristics Model is used within the OVAL Language in order to encode the underlying data collected as part of endpoint posture assessment. Each of the posture attributes collected by an OVAL-enabled tool can be represented using the OVAL System Characteristics Model. As such, this model should be used as the basis for implementing at least one of the formats used to encode collected posture attributes within SACM.

Within the OVAL System Characteristics Model, information such as metadata about the document (who/what created the document, creation timestamp, etc.), endpoint identification information (OS name, host name, and other asset-related information), and the foundational constructs to allow the encoding of posture attributes can be found. It is understood that modifications to the model will be required in order for it to fully implement all of the requirements for SACM. However, the use of this well-supported, standardized mechanism for encoding collected data is recommended as SACM begins moving from Information Model into Data Models and actual implementations.

The expectation is that SACM will need to make use of multiple types of standardized formats to encompass a complete solution for endpoint posture assessment. As such, the OVAL System Characteristics Model is likely to be used as one of multiple possible formats for encoding collected data.

7.2. Use the OVAL Definitions Model for Collection and Evaluation Guidance

Similar to the OVAL System Characteristics Model, the OVAL Definitions Model also has aspects that could be very useful in jump starting the development of a model to capture Collection Guidance. Collection Guidance is the mechanism by which a content author can dictate what rules should be used for collecting data from an endpoint. While the OVAL Definitions Model, as it is today, is used for guidance of both Collection and Evaluation, it is well suited to serve as the base for Collection Guidance.

This model provides several key features that should be used as building blocks for this capability. For instance, within the OVAL Definitions Model, there is a series of structures that can serve as the base for instructing tools as to what data must be collected, including abstract structures for identifying required posture attributes, Variables, and Functions (which allow several types of data manipulation during collection). The model also supports a number of different data types, such as strings, Booleans, integers, records, and others.

While the recommendation is to make use of many of the structures found within the OVAL Definitions Model, it is equally important to note that the current approach for extending OVAL into various platforms is flawed, and should be fixed. Specifically, for every new check that is to be added to the Language, a new concrete test must be created. OVAL provides an abstract Test structure that must be extended to create checks (e.g., "registry_test," "file_test," "ldap_test," etc.). For SACM, it is imperative that a more scalable and flexible approach be implemented.

One aspect of SACM that has been discussed, but only partially worked into the Information Model at the time, is the concept of high-level, platform-agnostic configuration items and low-level platform-specific configuration items. In the discussed concept, the high-level items will capture the concepts of configuration that must be defined by those who write the guidance, while the low-level items will be provided by the appropriate vendors and/or subject matter experts to allow those that best know the platforms and products to instruct data collection. With this approach in place, some of the concepts defined within the OVAL Definitions Model (e.g., Objects, which instruct tools as to what data to collect) will need to be modified or removed to accommodate the shift in how posture attributes are defined for Collection. As such, the recommendation is to use many of the underlying structures in the OVAL Definitions Model, including the data types, Variables, Functions, etc., as a base from which to build a complete solution for fulfilling the SACM Information Model.

In addition to utility in supporting Collection Guidance, the same OVAL Definitions Model should also be used as the base for Evaluation Guidance. Again, with the current OVAL Language, Collection and Evaluation are wrapped together in the single model. The OVAL Definitions Model provides a series of structures that can be used to support Boolean logic statements, which could be useful for defining evaluation criteria and could be used as the basis for a further enhanced model for Evaluation.

7.3. Do NOT Use the OVAL Results Model for Results Sharing

Despite the fact that the Results Model could be used to share the results of the evaluation part of an endpoint posture assessment, the recommendation is to not use this model to represent this information within SACM. The OVAL Results Model has, over the years, been a source of contention at times within the OVAL Community. Some feel like it provides too little information, while others believe that it offers too much. While there is some flexibility, in the form of OVAL Directives, in how much or how little information is included in the results, it really is not flexible enough to handle the broad set of requirements for SACM without extensive re-working.

Furthermore, SACM is working hard at separating data collection and evaluation, which makes the OVAL Results Model a poor fit, as it was constructed with a more combined Collection and Evaluation framework. It is expected that to properly model all of the results requirements within SACM, an alternative solution will be required.

While considering an alternative way to encode the results of an assessment, the following requirements have been stated by the OVAL Community as critical factors:

8. Acknowledgements

The authors would like to thank Brant Cheikes (MITRE), Juan Gonzalez (DHS), Adam Montville (CIS), Charles Schmidt (MITRE), David Waltermire (NIST), and Kim Watson (DHS) for reviewing this document and providing helpful feedback.

9. IANA Considerations

This memo includes no request to IANA.

10. Security Considerations

This memo documents, for informational purposes, the mapping between the OVAL Data Models and the SACM Information Model as well as the lessons learned from the past 10+ years of developing OVAL. As a result, there are no specific security considerations.

11. Change Log

11.1. -00 to -01

There are no textual changes associated with this revision. This revision simply reflects a resubmission of the document so that it goes back into active status. The document expired on November 6, 2015.

12. References

12.1. Normative References

[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997.

12.2. Informative References

[I-D.ietf-sacm-architecture] Cam-Winget, N., Ford, B., Lorenzin, L., McDonald, I., and l., "Secure Automation and Continuous Monitoring (SACM) Architecture", 2015.
[I-D.ietf-sacm-requirements] Cam-Winget, N. and L. Lorenzin, "Secure Automation and Continuous Monitoring (SACM) Requirements", 2015.
[OVAL-DEFINITION-TUTORIAL] The MITRE Corporation, "The OVAL Definition Tutorial", 2011.
[OVAL-WEBSITE] The MITRE Corporation, "The Open Vulnerability and Assessment Language", 2015.
[RFC1157] Case, J., Fedor, M., Schoffstall, M., and J. Davin, "A Simple Network Management Protocol (SNMP)", 1990.
[SACM] The IETF SACM WG, "IETF Security Automation and Continuous Monitoring (sacm) Working Group Charter", 2015.
[SACM-DOCUMENTS] The IETF SACM WG, "IETF Security Automation and Continuous Monitoring (sacm) Working Group Documents", 2015.
[STRUCTURE-OF-OVAL] The MITRE Corporation, "Structure of the Language", 2012.

Authors' Addresses

Matthew Hansbury The MITRE Corporation 202 Burlington Road Bedford, MA 01730 USA EMail:
Daniel Haynes The MITRE Corporation 202 Burlington Road Bedford, MA 01730 USA EMail:
Juan Gonzalez Department of Homeland Security 245 Murray Lane Washington, DC 20548 USA EMail: