Threat on national security
There is no formula to tell intelligence analysts where to focus monitoring efforts and which variables are most important to monitor. It shows that across these types of phenomena there are substantial differences in the level of consensus within the relevant communities of experts about which are the key variables from which a small and useful set of indicators could be developed.
One of the objectives of this effort should be to build the scientific basis for indicators in this domain. Substantial ongoing monitoring activities may prove useful for measuring aspects of the key phenomena.
In setting priorities for indicator development and improvement, the intelligence community should take into account the gaps between the existing and the desired resolution and should invest in improved resolution for those indicators judged to be the most needed and the most useful in places of concern.
There is strong reason to try to identify a small number of reliable and valid indicators to cover a great variety of phenomena. Our information networks and technology are constantly at risk from a variety of bad actors using a multitude of techniques — remote hacking intrusions, the placement of malware, spearphishing and other means of gaining access to networks and information.
The procedures for analysing, comparing and dealing with threats are explored in a dedicated subsection.
Why is national security important
Another is the widely recognized but difficult problem of making estimates from data of widely differing quality when that quality varies systematically rather than randomly, for example, across regions, types of government, and level of economic development. At the same time, we must ensure that we protect our own national security information from those who would do us harm. Setting Data Priorities Because of the multifaceted nature of the phenomena that might connect climate events to national security concerns and because of the complexity of these connections, setting priorities for monitoring is a significant challenge. With its National Security Strategy, it is examining the threats, how to prevent them, and what to do if a disaster occurs. More broadly, as the Political Instability Task Force forecasting tournament indicated, there is a need to identify the relative merits of the competing data analysis approaches currently prevalent: frequentist relying on significance testing ; Bayesian using probability distributions ; and machine learning pattern recognition, broadly defined. In many instances, existing knowledge does not yet support reliance only on quantitative indicators. Validating Indicators Validation involves determining the extent to which an indicator actually measures the phenomenon it purports to measure—a task that can be extremely challenging. Recommendation 6. For example, composite indicators are sometimes created to summarize knowledge about various phenomena of interest, such as drought, susceptibility to damage from flooding, health status of a population, emergency coping capacity, or political instability. Critical infrastructure is vital for the essential functioning of a country. The scope and nature of environmental threats to national security and strategies to engage them are a subject of debate. Despite progress in developing indicators of several of the key phenomena and the connections among them, it is premature to settle on a small number of variables to monitor that will be sufficient to meet the needs of analysis. It should also be kept in mind that existing datasets may provide—or may be analyzed to provide—useful information. Understanding the Threat Understanding the Threat The United States today faces very real, very grave national security threats.
At the same time, we must ensure that we protect our own national security information from those who would do us harm. More broadly, as the Political Instability Task Force forecasting tournament indicated, there is a need to identify the relative merits of the competing data analysis approaches currently prevalent: frequentist relying on significance testing ; Bayesian using probability distributions ; and machine learning pattern recognition, broadly defined.
The cost of cybercrime — already in the billions of dollars — rises each year.
based on 104 review