ISACA Journal
Volume 1, 2,015 

Features 

Implementing an Information Security Continuous Monitoring Solution—A Case Study 

Tieu Luu 

The threats to government computer systems and networks continue to evolve and grow due to steady advances in the sophistication of attack technology, the ease of obtaining such technology, and the increasing use of these techniques by state and nonstate actors to gain intelligence and/or disrupt operations. The US Government Accountability Office (GAO) cites that from 2006 to 2012, the number of cyberincidents reported by federal agencies to the US Computer Emergency Readiness Team (US-CERT) grew from 5,503 to 48,562, an increase of 782 percent.1

As one of the responses to this growing threat, the executive branch of the US government has established as one of its cross agency priority (CAP) goals2 the continuous monitoring of federal information systems to enable departments and agencies to maintain an ongoing near-real-time awareness and assessment of information security risk and rapidly respond to support organizational risk management decisions. In November 2013, the US Office of Management and Budget (OMB) issued memorandum M-14-03 requiring all federal departments and agencies to establish an information security continuous monitoring (ISCM) program.3 The US Department of Homeland Security (DHS) has been tasked to work with all of the departments and agencies to help them implement continuous monitoring through the Continuous Diagnostics and Mitigation (CDM) program.

To help it comply with the OMB mandate, one large US government agency has contracted with SuprTEK, an IT engineering and professional services firm, to develop a continuous monitoring system that is responsible for monitoring millions of devices across a globally distributed network. The system has enabled the client to improve its processes for risk and vulnerability management, certification and accreditation (C&A), compliance and reporting, and secure configuration management, greatly improving the security posture of its systems and saving countless work hours by automating many of the previously manual processes.

Defining ISCM

So what exactly is ISCM? “Information security continuous monitoring is defined as maintaining ongoing awareness of information security, vulnerabilities and threats to support organizational risk management decisions.”4 This means continuously collecting information to provide a comprehensive understanding of everything that is deployed on an enterprise’s networks and using this information to assess compliance against security policies and exposure to threats and vulnerabilities. This information provides IT managers with a comprehensive and up-to-date inventory of assets and how they are configured so that they understand what is on their networks and where the networks may be vulnerable. It helps system administrators properly prioritize vulnerabilities based on how pervasive they may be across the enterprise and their potential impact to the mission or business, rather than trying to patch everything and continuously play catch-up with newly discovered vulnerabilities. The information provides auditors with up-to-the-minute information on each system’s security posture so that they can properly decide whether or not a system should be approved to go live on the production network or be taken offline if a critical finding is not properly remediated or mitigated. The collected information is also entered into a set of risk-scoring algorithms to quantify the security posture across the entire enterprise and identify and prioritize the worst problems to fix first so that executives can focus their scarce IT resources.

Implementation Architecture

Figure 1A continuous monitoring system is essentially a data analytics application, so at a high level, the architecture for a continuous monitoring system, depicted in figure 1, resembles that of most typical data analytics/business intelligence (BI) applications. DHS has defined a technical reference architecture for continuous monitoring called the Continuous Asset Evaluation, Situational Awareness, and Risk Scoring (CAESARS) reference architecture5 based on the work of three leading US federal agencies that have successfully implemented continuous monitoring solutions: the US Department of State (DOS), the US Internal Revenue Service (IRS) and the US Department of Justice (DOJ).

The CAESARS reference architecture represents the essential functional components of an ISCM and risk-scoring system, as depicted in figure 1. The four functional subsystems defined by CAESARS are:

  • Sensor subsystem—Responsible for collecting data such as hardware and software inventory, configurations, compliance and vulnerabilities from the targets (i.e., assets or devices such as the computing, network and mobile devices on an enterprise’s networks). The sensor subsystem may be composed of agent-based and agentless software, as well as hardware devices that scan the devices and networks and send data back to the database/repository subsystem.
  • Database/repository subsystem—Responsible for storing the findings collected by the sensor subsystem. The database/repository subsystem is also responsible for storing and managing the technical security policies and implementation guidance that define how the targets should be configured. Targets are assessed against these baseline configurations to determine compliance and how well they are secured.
  • Analysis/risk-scoring subsystem—Responsible for correlating, fusing, deconflicting and deduplicating the findings collected by the sensor subsystem in addition to assessing compliance of the findings against the baselines. Once the collected data have been processed by the analysis capabilities, the risk-scoring capabilities are responsible for using this information to quantify security posture and risk of the enterprise using algorithms that take into account the severity of the findings, the probability of exploit and the impact of successful exploit.
  • Presentation and reporting subsystem—Responsible for presenting the results of the analysis and risk-scoring subsystem through various dashboards and reports to “motivate administrators to reduce risk; motivate management to support risk reduction; inspire competition; and measure and recognize improvement.”6 The subsystem has to be able to present information at an aggregate level across the enterprise as well as to be able to drill down into specific devices and findings to support remediation.

Data Integration Challenges

As with most data analytics/BI applications, data integration presents many challenges for a continuous monitoring system. Most large enterprises have multiple tools that make up the sensor subsystem, e.g., they may use a network access control (NAC) solution to detect devices, vulnerability scanners to detect vulnerabilities on devices, code analyzers and scanners to detect software flaws, and configuration scanners to assess compliance against security policies. Thus, it becomes the classic master data management (MDM) problem where the complete picture of an IT asset (e.g., hardware, operating system, software applications, patches, configuration, vulnerabilities) has to be pieced together from disparate systems. Some of the key challenges with trying to piece together all of the required data from these types of tools are described in figure 2.

Figure 2

A data ingest capability was implemented as an asynchronous layer around the database/repository subsystem with a Secure Content Automation Protocol (SCAP)-based7 interface to consume data from the sensor subsystem. As mentioned, the use of SCAP alleviated some integration challenges by enabling a common format, but also created other challenges due to variations in implementation by the different sensors. Ultimately, those variations were accounted for via the use of different interpreters based on version information in the data that are received by the ingester. Techniques from MDM were applied to address some of the other data integration challenges. For example, cross-referencing is a common technique in MDM where a master table is defined for an entity that contains all of the potential identifiers for that entity across the disparate systems. In this case, the cross-reference capability defined a master identifier for devices and also contained all of the other identifiers for devices used by the various sensor tools (e.g., MAC address, Internet Protocol [IP] address, host name) that were used to match the findings from the sensors to the correct device. There was no panacea to address the challenges with data completeness and quality. It required a great deal of close monitoring and validation when integrating sensor data from a new site and working with the site’s administrators to correct the issues that were identified. Various system reports were used to check for completeness and quality (e.g., what sites were publishing data and what data they were publishing). To deal with issues around overlapping and conflicting findings from different sensors, a trust model that defined which sensors to trust for which types of findings (i.e., for findings of this type, trust the results from sensor A over the results from sensor B) was implemented. For example, for vulnerability assessments, the results from authenticated, agent-based scanners were considered more credible than the results from agentless, network-based scanners.

Data Architecture Challenges

The database/repository subsystem needs a robust architecture that can support multiple interaction models—a lot of writes to ingest data from the sensor subsystem, batch and real-time processing to support the analytics, and ad hoc queries from users. Additionally, it needs to be able to accommodate a rich and evolving set of information that is collected about an enterprise’s IT assets. For example, the initial phase of the DHS’s CDM program is focused on hardware and software asset management, configuration settings, known vulnerabilities and malware. The dataset required to support these use cases includes devices, software applications, patches, configurations, vulnerabilities and operational metadata (e.g., owning/administering organizations, locations, supported systems). Subsequent phases of the program add other use cases, such as auditing, event and incident detection, privilege management, and ports/protocols/services, which greatly expand the dataset that the database/repository subsystem will have to support. Key data architecture challenges presented by these requirements are described in figure 3.

Figure 3

This system started with a single database architecture, but evolved into a three-stage data architecture to support the diverse and sometimes conflicting requirements described herein. The purpose of the first stage was to provide a warehouse or collection area to quickly write the data coming in from the sensors, assemble all the messages and reconcile them with existing records in the repository. A great deal of data transformation at the point of data ingestion could create a bottleneck, so the schema for this first stage was designed to closely resemble the data models used by Asset Reporting Format (ARF )8 and Asset Summary Reporting (ASR).9 Once the data were ingested, a separate set of jobs would perform the consolidation, correlation and fusion to create the complete, up-to-date profile of the asset. Next the data were extracted, transformed and loaded (ETL) into the second stage, which was a dimensional (e.g., star and snowflake schema) database that was optimized for the analytics and to support the presentation and reporting subsystem. The third stage was a set of Online Analytical Processing (OLAP) cubes that were built from the dimensional database to support the hierarchical dashboards with high-speed roll-up and drill-down analysis of the data.

Analytics Challenges

The main types of analytics required in a continuous monitoring solution include correlation, fusion and deconfliction of sensor findings; compliance assessment; risk scoring; historical trending; and ad hoc queries. In addition to helping identify the vulnerabilities that an enterprise is exposed to, along with the scope of exposure and potential impact, these analytics capabilities also help an enterprise assess how well it has implemented the security controls defined in its policies, e.g., the SANS Top 20 Critical Security Controls.10 Risk scoring is applied to these assessments to quantify how well the organization is doing and prioritizes the worst problems to fix first. The risk-scoring algorithms can get quite complex when taking into consideration the different types of defects/findings, the severities of the findings, the threats and the impact on the affected assets. Additionally, the organization has to consider whether or not the findings can be remediated, mitigated and accepted, or whether the risk can be transferred to another organization. The analytics and risk scoring have to be applied at multiple levels, from the individual asset or device level, to the network enclave level, to the department level and, finally, up to the enterprise level. This enables the comparative analyses required to identify the worst areas to fix first and enables administrators to drill down into specific assets that have to be remediated. Some of the challenges that may be encountered when implementing these analytics capabilities are described in figure 4.

Figure 4

Rigorous engineering discipline combined with agile development methodologies were key to overcoming the challenges associated with the complexity of the analytics’ algorithms, as well as to continuously correct and/or evolve the analytics to keep up with changes in the operational environment. Accounting for the quality and consistency issues in the sensor data published from the various sites required a combination of technical and nontechnical solutions. For example, the algorithms were implemented to be robust enough to account for missing data, but then were assigned default values that would penalize the sites for missing data and this was used to drive behavior to ensure that the organization would publish their sensor data correctly in the future. Ensuring that the data could be properly aggregated from multiple sites across the enterprise ultimately required the centralization of the definition of the taxonomies that were used to organize the assets for reporting. So while this took away some flexibility for the sites to dynamically define their own taxonomies, the ability to correctly and reliably aggregate the data outweighed this drawback.

Performance and Scalability Challenges

Figure 5While not on the same scale that large Internet companies face in their applications, in general, a continuous monitoring solution still stores and processes large amounts of data so there are performance and scalability challenges. For example, the client agency described here has somewhere between 5 million and 10 million assets with thousands of software applications and patches, thousands of compliance and configuration settings, and thousands of vulnerabilities to assess against these assets on a daily basis. Figure 5 depicts these key datasets and the order of magnitude in the number of records that were collected.

SCAP standards such as ARF, ASR and the Extensible Configuration Checklist Description Format (XCCDF) are rather verbose XML formats and can be very central processing unit (CPU)- and memory-intensive to process. This system has a fixed-time window each night for running the batch jobs that process all of the data collected from the sensors and there have been occasions when the processing duration exceeded the allotted time. These problems are not unique to continuous monitoring and there are many available solutions to address them (e.g., the use of fast-streaming XML parsers to quickly write the ARF, ASR and XCCDF data to the database and have separate jobs to do the consolidation and correlation so that no bottleneck is created at ingestion). Data are stored in multiple formats that are specifically optimized for the analytics they are supporting. Wherever possible, preprocessing is used to speed up response times (e.g., precomputed results in OLAP cubes to drive the dashboards). And then, of course, portions of the architecture have been migrated to Hadoop (e.g., HBase for the data warehouse and Map/Reduce and Pig for some of the analytics) to increase the scalability.

Conclusion

An ISCM solution applies many of the technologies from data analytics, business intelligence and MDM applications to the complex domain of cybersecurity. Thus, one may encounter many of the same challenges faced by these types of applications around data integration, data architecture, analytics, and performance and scalability, with additional complexities introduced by the use cases, datasets and standards that are specific to cybersecurity.

Implementing an ISCM solution across a large enterprise is a complex undertaking and there are many other challenges from the deployment, operations and governance perspectives that need to be considered. For example, the deployment approach needs to ensure that sensors are deployed in such a way that provides complete coverage of an enterprise’s IT landscape. From an operations perspective, an ISCM solution has a broad set of stakeholders (e.g., chief information officers [CIOs], chief information security officers [CISOs], program managers, system administrators) and they all need to be trained to properly operate and use the capabilities provided. Executives such as CIOs and CISOs need to know how to interpret the results that are displayed in the dashboards, while the system administrators need to know how to properly scan their assets and publish findings. And perhaps most important, governance is needed to make all of this work: First, to require that all of the departments use the tool to inventory and scan their assets in accordance with enterprise security policies and, finally, to enforce the necessary mitigating or remediating actions to address the findings.

Endnotes

1 Government Accountability Office, Report to Congressional Committees, “High-Risk Series: An Update,” USA, February 2013, www.gao.gov/assets/660/652133.pdf
2 Performance.gov, “Cross-Agency Priority Goal—Cybersecurity,” www.performance.gov/content/cybersecurity#overview
3 Office of Budget Management, “M-14-03. Enhancing the Security of Federal Information and Information Systems,” USA, www.whitehouse.gov/sites/default/files/omb/memoranda/2014/m-14-03.pdf
4 National Institute of Standards and Technology, Special Publication 800-137, “Information Security Continuous Monitoring (ISCM) for Federal Information Systems and Organizations,” USA, http://csrc.nist.gov/publications/nistpubs/800-137/SP800-137-Final.pdf
5 Department of Homeland Security, “Continuous Asset Evaluation, Situational Awareness, and Risk Scoring (CAESARS) Reference Architecture Report,” USA, www.federalcybersecurity.org/CourseFiles/ContinuousMonitoring/fns-caesars.pdf
6 Ibid.
7 National Institute of Standards and Technology, “The Security Content Automation Protocol (SCAP),” USA, http://scap.nist.gov/
8 National Institute of Standards and Technology, “ARF—The Asset Reporting Format,” USA, http://scap.nist.gov/specifications/arf/
9 National Institute of Standards and Technology, “ASR—The Asset Summary Reporting,” USA, http://scap.nist.gov/specifications/asr/
10 SANS Institute, “Top 20 Critical Security Controls,” USA, www.sans.org/critical-security-controls
11 Department of State, “iPost,” USA, www.state.gov/documents/organization/156865.pdf
12 Department of Energy, “Cybersecurity Capability Maturity Model (C2M2),” USA, http://energy.gov/oe/services/cybersecurity/cybersecurity-capability-maturity-model-c2m2-program/cybersecurity

Tieu Luu is director of research and product development for SuprTEK, where he leads the development of innovative products and services for the company, including the PanOptes Continuous Monitoring Platform.

 

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