The recent Los Angeles Airport shooting is a stark reminder of the challenges that local, state, and federal government officials face in enacting and maintaining strict security measures.
Government entities such as Orange County, Florida are increasingly relying on big data analytics to parse through volumes of security data to better identify and act on potential threats.
For instance, Orange County is able to use analytics to help crunch and analyze data that’s collected from 3,000 surveillance cameras.
Meanwhile, the county is also using a tool to capture and analyze texts sent by public officials and employees, in part, to determine whether they’re texting passwords or other sensitive information.
The use of big data and analytics to strengthen national security is hardly new.
In March 2012, the White House announced a $200 million big data initiative across six federal departments and agencies. As part of these efforts, the U.S. Department of Defense (DoD) is investing in recruiting and developing data scientists to recognize patterns that may exist in large volumes of data.
In addition, the DoD is investing in tools to help its analysts develop effective surveillance methods that can enable them to detect a threat before it materializes.
For its part, the Department of Homeland Security is exploring the use of real-time data to respond more effectively to emergency situations. This includes the use of an Automated Targeting System administered by U.S. Customs and Border Protection that’s applied to inbound and outbound cargo, land border crossings, and passengers.
As smart grid technologies become more pervasive in the US and elsewhere, the vulnerability of national and local energy infrastructures is increased. Given the geographic distribution of energy facilities, one approach to addressing these threats that’s gaining interest is the use of geospatial predictive analytics.
Energy and government security professionals can use these tools to uncover relevant patterns and relationships.
“If you can discover the spatial and temporal factors that correlate with a given type of event – such as a theft or sabotage – it is possible to anticipate where similar events are most likely to occur in the future,” as Jim Stokes notes in a recent post for Government Security News.
Geographic factors such as the target type, terrain, presence of law enforcement, and distance from major roads have been used by security and energy officials to deliver highly accurate and effective guidance that can reduce a threat area by more than 90%, says Stokes.
Meanwhile, local police departments and law enforcement officials are able to use big data and analytics not only to analyze areas where certain types of crimes are clustered but also to identify crime patterns that may help them predict and plan for future crime sprees and security threats.
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