Risk Assessment

Supply chain-related events include disruption, use of defective components, insertion of counterfeits, theft, malicious development practices, improper delivery practices, and insertion of malicious code. These events can have a significant impact on the confidentiality, integrity, or availability of a system and its information and, therefore, can also adversely impact organizational operations (including mission, functions, image, or reputation), organizational assets, individuals, other organizations, and the Nation. The supply chain-related events may be unintentional or malicious and can occur at any point during the system life cycle. An analysis of supply chain risk can help an organization identify systems or components for which additional supply chain risk mitigations are required.

Organizations employ all-source intelligence to inform engineering, acquisition, and risk management decisions. All-source intelligence consists of information derived from all available sources, including publicly available or open-source information, measurement and signature intelligence, human intelligence, signals intelligence, and imagery intelligence. All-source intelligence is used to analyze the risk of vulnerabilities (both intentional and unintentional) from development, manufacturing, and delivery processes, people, and the environment. The risk analysis may be performed on suppliers at multiple tiers in the supply chain sufficient to manage risks. Organizations may develop agreements to share all-source intelligence information or resulting decisions with other organizations, as appropriate.

The threat awareness information that is gathered feeds into the organization’s information security operations to ensure that procedures are updated in response to the changing threat environment. For example, at higher threat levels, organizations may change the privilege or authentication thresholds required to perform certain operations.

A properly resourced Security Operations Center (SOC) or Computer Incident Response Team (CIRT) may be overwhelmed by the volume of information generated by the proliferation of security tools and appliances unless it employs advanced automation and analytics to analyze the data. Advanced automation and analytics capabilities are typically supported by artificial intelligence concepts, including machine learning. Examples include Automated Threat Discovery and Response (which includes broad-based collection, context-based analysis, and adaptive response capabilities), automated workflow operations, and machine assisted decision tools. Note, however, that sophisticated adversaries may be able to extract information related to analytic parameters and retrain the machine learning to classify malicious activity as benign. Accordingly, machine learning is augmented by human monitoring to ensure that sophisticated adversaries are not able to conceal their activities.