It was back in 1956 when Philip K. Dick wrote the science fiction short story ‘Minority Report’, later adapted for the cinema. The story is about a future society where three mutants attached to a machine can foresee crimes before they occur, allowing the ‘Precrime Division’ to arrest suspects before any offence is actually committed.

As it happens, science fiction sometimes becomes reality. But instead of using mutants ‒ they are as unreliable as they are imaginary ‒ today’s security solution providers rely on complex algorithms to predict the future. The principle is relatively simple; the practice way less.

In today’s world, we consume information as much as we produce it: news websites publish millions of articles daily, while billions of posts appear on social media each day. Twitter alone has an average of around 500 million tweets every day. The ‘internet of things’ revolution – devices connected to the internet – is also producing a huge amount of data.

Companies in different sectors have found ways to leverage this gold mine of information to increase sales (think Amazon, Walmart), to suggest to us what to read, watch or do (Google, Netflix), or indeed to predict the outcome of future events.

“Science fiction sometimes becomes reality: instead of mutants, today’s security solution providers rely on complex algorithms”

Predictive algorithms are all around us, although we do not fully realise it. A very simple application can be found in our smartphones: when we type a word, the phone suggests the next word or corrects a typo. This process is possible thanks to millions of sentences and corrections done previously by other users who have ‘trained’ the algorithm with their repetitive actions. The algorithm is capable of guessing what you are about write ‒ or wanted to write.

Security solution providers have been using similar, big data-related technologies for a while now. Media and social media monitoring platforms are not only capable of harvesting information and alerting us in real time when a disaster ‒ man-made or natural ‒ occurs, but can also alert us if an event is likely to happen in the future.

The Los Angeles, Atlanta and Chicago police departments, among others, are using software that allow them to perform so-called ‘predictive policing’. The system uses historical data on location, time, place and type of crime to identify patterns and give law enforcement agents clear indications on where and when criminal offences are most likely to happen. By adopting predictive policing software, the Los Angeles Police Department registered a 20% drop in crimes in the period from January 2013 to January 2014.

But crime is not the only social phenomenon that monitoring platforms can predict. By using a large set of open sources, such as tweets, news, blogs, food prices, currency rates and so on, software called EMBERS (‘Early Model Based Event Recognition using Surrogates’) is capable of forecasting civil unrest days or weeks in advance.

“Crime is not the only social phenomenon that monitoring platforms can predict”

It is important to remember that this new revolution in data-driven security intelligence is not meant to replace the role played by humans. The human factor in intelligence and policymaking will still play a major role, as no machine is currently capable of replacing human intuition and qualitative judgment.

Despite this technology still being at an initial stage of development and exploitation, monitoring solutions can provide security practitioners and decision-makers with state-of-the-art technology and help them to perform their tasks with a higher degree of efficiency. In fact, the intelligence provided by monitoring applications could be extremely useful to monitor extremism and counter online radicalisation, to prepare and respond to natural and man-made disasters, and, for the private sector, to avoid supply chain disruption and to protect facilities and employees. Monitoring applications can strongly improve the performance of budget-constrained agencies and reduce waste of human and financial resources by allocating them exactly where they are needed the most.

For this technology to reach its full potential, small and medium-sized enterprises and start-ups will need to play a key role. But Europe is still lagging behind in terms of academic research and commercial exploitation of big data technologies for the security sector, when compared to the United States.

The European Union’s framework programme for research and innovation, Horizon 2020, could be the perfect platform to foster the capabilities of European start-ups in research and development. The platform could allow them to come out with the next generation of data-driven security intelligence applications. It is also of paramount importance to foster collaboration between academia, such as data and social scientists, and the private sector.

If successful, Europe will not become trouble-free. But such steps will certainly contribute to reducing uncertainties and making our continent safer.

IMAGE CREDIT: iunewind/Bigstock