Fast Analysis Infrastructure Tool (FAIT)
NISAC analysts are regularly tasked by the Directorate for Preparedness in the Department of Homeland Security (DHS) with determining the significance and interdependencies associated with elements of the nation’s critical infrastructure. The FAST Analysis Infrastructure Tool (FAIT) has been developed to support this need.
Analysis Elements
FAIT is designed as a synthesis of infrastructure data and expert knowledge on the operation and interactions of infrastructure. FAIT brings together a wide variety of analytic elements for the end user, which aid in gaining a complete picture of the function of infrastructure:
Interdependency: FAIT utilizes system expert-defined object-oriented interdependencies, encoded in a rule-based expert systems software language (JESS), to define relationships between infrastructure assets across different infrastructures. These interdependencies take into account proximity, known service boundaries, ownership, and other unique characteristics of assets found in their associated metadata. Interdependencies are expressed in plain languageand graphical (map) products.
- Co-Location: In a similar fashion, co-location of assets can be analyzed based exclusively on available spatial data. The association process is dynamic, allowing for the substitution of data sets and the inclusion of new rules reflecting additional infrastructures, as data accuracy is improved and infrastructure analysis requirements expand.
Information Association: FAIT provides end users with the means to search other data sources, both within NISAC (e.g., the Knowledge Management portal) and in the world at large (the Internet via tools such as Google Search), and develop links to relevant information to provide a more complete understanding of particular assets at the fingertips for future analyses.
- Economic Impact: FAIT also utilizes established Input/Output (I/O) methods for estimating the economic
consequence of the disruption of an asset. FAIT’s regional economic analysis takes as input economic data (from the Bureau of the Census) for the disrupted area. When coupled with other NISAC modeling results (estimates for the duration of the disruption and recovery, and the range of magnitude of disruption for the disrupted region), FAIT creates a regional economic analysis, an understanding of the direct and indirect economic consequences, for each sector of the economy in each county in the analysis area.
Each of FAIT's analysis elements (interdependency, co-location, economic analysis) have been extended from their original ‘asset-level’ analysis, to allow for the analysis of a specified region. Here, rules written for individual assets are executed en masse on classes of demand infrastructures (for instance, assets of the emergency services [fire and police stations] and public health [hospitals]) which lie in a defined analysis area, such as a hurricane damage zone, to identify those elements of supply infrastructures (e.g., electric power and telecommunications) which serve the largest number of particular sets of demand infrastructures.
System Output
FAIT analysis results are presented in a Web-based, printer-friendly format, and include a plain language description of assets, their interdependencies, economic consequence of disruption, and other information associated with the asset by system users.
Development efforts
The FAIT development team is constantly modifying their development goals to best support the requirements of NISAC analysts, in responding to questions from DHS. Current development efforts include the following:
- Expansion of existing FAIT capabilities to cover infrastructures not in the current analysis set
- Enhancement of economic analysis capability to more accurately represent the consequences of the loss of infrastructure services on the performance of individual industrial sectors
- Incorporation of infrastructure-specific models to define areas of consequence due to the failure of asset(s) in a given infrastructure
- Development of a network ‘metacrawler’ designed to associate sparse metadata (e.g., transportation system commodity throughput) with fragmented system elements (e.g., segments of the national rail network)
