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Waiting in Line at Airport Security This Summer?        AI Could Make Screening More Effective and Efficient.

Waiting in Line at Airport Security This Summer? AI Could Make Screening More Effective and Efficient.

With summer air travel expected to hit a new record between June 1 and August 31 this year, travelers should expect to see longer lines at security checkpoints.

To address these challenges, the Transportation Security Administration (TSA) is hiring an additional 2,000 employees and employing new technologies like automated screening lanes and computed tomography

360-degree Security View

As these changes move forward, implementing artificial intelligence and machine learning technologies can also help reduce wait times and increase the effectiveness of security screening.

In fact, experts suggest that AI and big data analytics can move the screening process from the current single point in time analysis to a 360-degree view of a person’s behavior over a broader time range by linking data sets to identify risky behavior even before a potential bad actor gets to the airport.

This thinking is in line with the recommendations from the White House’s 2018 National Strategy for Aviation Security (NSAS).  NSAS highlighted the importance of strengthening aviation domain awareness through integration of open-source data into existing air surveillance and law enforcement intelligence, collection and analysis of advanced and anticipatory information, and layered and risk-based security measures.

The International Air Transport Association is also working on a program to facilitate the exchange of critical security data.  According to the Director General and CEO Alexandre de Juniac, “This is similar to the way that our safety colleagues work with data to do predictive risk analysis. This tool will provide early detection of changes to security environments in different parts of the world, so we can effectively deal with emerging threats and the impacts of changes to security procedures.”

The Role of AI

AI-driven technologies, like Lumina’s Radiance platform are another facet to the solutions being implemented in the U.S. and globally.

Radiance has the ability to comprehensively mine unstructured data sources, whether across the open web, or among disparate, legacy data systems. It ingests, integrates and analyzes those data sets, searching against more than 6,500 terms related to aviation security.

The platform conducts nearly 135,000 searches across all publicly-available data on the web, correlating names with these associated risk behaviors and cross-referencing over 1 million queries into Lumina’s proprietary databases of risk. 

Then add to this open source search internal data sets such as passenger bookings and travel history – or in the case of insider-threats, employee-related data – and airline and airport security experts have an important tool to help predict and prevent threats.

Looking ahead

To be sure, integrating AI driven technologies like Radiance are not a thing of the far of future. Research shows that 66% of airlines and 79% of airports plan to implement these capabilities across a wide variety of use cases by 2021. In fact, AI in aviation was valued at $152 million in 2018 and expected to increase to $2.2 billion by 2025.  And, passengers are ready for these technologies to help expedite their time at  airport security and make their travel more seamless. An online poll of UK passengers found that 68 percent of respondents would welcome AI at airports, and another study found 65 percent would share additional personal information to speed up processing at the airport.

Learn more about Radiance’s capabilities for the airline industry here.

Modernizing the Security Clearance Process through Machine Learning and AI

Modernizing the Security Clearance Process through Machine Learning and AI

Late last month, President Donald Trump signed an Executive Order transferring responsibility for security clearance screening from the Office of Management and Budget to the Defense Department. 

The Administration had previously called the clearance process a target for government reform, noting in 2018 that “background investigations are critical to enabling national security missions and ensuring public trust in the workforce across the Government.”

The Administration’s efforts are part of an ongoing focus on reforming the clearance process, and reducing the existing backlog. 

That is because the current backlog peaked at 725,000 open investigations in 2018, with some Americans waiting more than 500 days just to start their first day at work.  As part of these efforts, the Federal Government hired 2,500 additional investigators in 2018 to address the backlog.


Re-thinking Security Clearance

In addition to the Executive Order, in February, Senator Mark Warner (D-VA), reintroduced The Modernizing the Trusted Workforce for the 21st Century Act (S.314).

The legislation calls for a major overhaul of the system.

It also sets targets to reduce the backlog to 200,000 by the end of 2020, and shorten the time required to issue a secret level clearance to 30 days or fewer and top secret level clearance to 90 days. 

The legislation also establishes the “clearance in person” or “one-clearance” concept. This would enable – within two weeks or fewer – clearances to follow employees who change agencies.

Similarly, the legislation calls for continuous evaluation. It would move from the existing periodic reviews, to dynamic and ongoing reviews in the future.

In many ways, these recommendations represent a complete re-thinking of the security clearance process.

As Senator Warner notes in his legislation, technologies will play a critical role in preventing, detecting and monitoring threats. He also notes the role data integration and analytics can play in expediting or focusing
re-investigations through delta reporting and continuous evaluation. 


An Antiquated System

As many security experts have pointed out, the current system is not only time consuming and slow, it is also out of sync with how people live today.  For example, as it currently works, a field investigator is assigned to confirm information from the applicant’s form, and to make sure that individual does not represent a threat to national security.

These determinations are based on the 13 adjudicative guideline criteria, which among others include, financial considerations, foreign preference and influence, alcohol consumption, and drug involvement.

To be sure, 50 years ago, interviews with neighbors, colleagues and other associates could help provide meaningful insights into our lives and habits.  But today, we share these very same insights publicly, willingly and knowingly across a variety of online platforms, making the Internet a useful, but largely untapped resource.


Challenges to Reform

In fact, according to Gary Reid, Director of Defense Intelligence patterns of life, including scans of public-facing social media could one day be considered.

A significant challenge is the volume of data on the web. 

With more than 2.5 quintillion bytes of data created on the Internet every day, searching for relevant content can be like looking for the proverbial needle in a haystack.


The role of AI and Machine Learning

One way to solve for this is through machine learning and AI capabilities – a super-charged web search, allowing for all that publicly available, open-source data to be searched for risk behaviors – in this case, associated with the 13 established adjudicative guidelines.

But rather than having to weed through thousands of pages of search results, these technologies can quickly synthesize the data and cull out high priority risks associated with guideline selectors. 

As a result, analysts receive the most critical data first, helping streamline their search process and gather the most relevant information.


Call it the Radiance Solution
              

Lumina’s AI-powered Radiance technology is specifically designed to overcome the challenges of massive unstructured data ingestion, evaluation, and prioritization. This provides a rapidly deployable, scalable and user-friendly solution for the security clearance process. 

The technology is comprised of three modules, for edge-to-edge risk detection.


Radiance Open Source Intelligence (OS-INT)

OS-INT is a deep-web listening tool that uses machine learning and artificial intelligence to assess and prioritize risk.  OS-INT scours publicly available data across the entire Internet, correlating names entered into the system with content related to its exclusive behavioral risk profiles (BRPS). It then cross-references that information with more than one million queries into Lumina’s proprietary databases of risk.  And, unlike social media monitoring, OS-INT is not reliant on a single platform or social media API, allowing for continuous ingestion of all open source data.

OS-INT’s security clearance bundle includes more than 16,220 terms related to the adjudicative guidelines. OS-INT performs nearly 325,000 searches across the entire web. It then correlates names with associated risk behaviors. Similar results would take an individual running a manual web query more than 18 years to read and analyze.

OS-INT completes searches in an average of 4-5 minutes, providing prioritized, high resolution, and actionable results. In addition, the system allows for continuous monitoring and evaluation, mapping previous results against results from more recent queries.

The configuration of BRPs only collects publicly available information, within the scope of the investigation. And, it does not use account creation or digital interaction with a person of interest. As a result, the collection of information adheres to Security Executive Agent Directive 5 guidelines.


Radiance Internet Intelligence (NET-INT)

NET-INT’s proprietary algorithms continuously identify, monitor, capture, and prioritize IP addresses exhibiting anomalous behavior across multiple risk dimensions.  In addition, its massive system of data ingestion has the capability to catalogue, index and redeploy Internet content related associated with the adjudicative guidelines.

The system captures an IP addresses’ pattern of life data, prioritizing anomalous behavior. NET-INT also screens IP addresses associated with an entity or person of interest against all IP addresses displaying anomalous behavior collected over the system’s lifespan. 

NET-INT’s continuous monitoring of a POI’s Internet research behavior then helps predict emergent behavior indicative of a violation of the guidelines.


Radiance Human Intelligence (HUM-INT)

HUM-INT is powered by the S4 app, a crowd-sourced, mobile application that allows users to confidentially report concerns in real time. The S4 app can be configured as a workplace tool, allowing employees to submit information related to potential risk behaviors exhibited by co-workers. A centralized management portal allows clients to access real-time threats to geo-fenced facility locations.


The Way Forward

As Washington continues its efforts to reduce the security backlog, and modernize the existing process, machine learning and artificial intelligence will play an important role.

Senator Warner recently said, “There is much more we can do to reform decades-old policies and processes to reflect today’s threat environment, adapt to the dynamic of a modern mobile workforce, and capitalize on opportunities offered by modern information technology.”

The Threat of Vehicular Attacks is Growing. AI Can Help Stop Them.

The Threat of Vehicular Attacks is Growing. AI Can Help Stop Them.

In October 2017, Sayfullo Saipov carried out an ISIS-inspired vehicular attack in New York City that was the deadliest terror attack in NYC since 9/11. This attack was just one in a recent global pattern of vehicular attacks that have proven to be a growing threat worldwide, impacting countries such as the U.S., the U.K, Canada, Germany, Sweden, and Spain in 2017. Since 2009, 169 terror attacks have involved vehicles as a weapon, and since 2006, 194 people have been killed with 1,048 injured globally in vehicular terror attacks. Because of this, vehicular attacks have become one of the most dangerous forms or terror. The more we understand about what makes these attacks soft targets, what motivates would-be-attackers and how we can safeguard our communities from them in the future, the closer we get to mitigating these threats.

Soft Targets

Vehicular attacks are particularly concerning because they often strike “soft targets,” or easily accessible locations containing large numbers of people with little security measures to protect them. In fact, from 1968 to 2005, 73% of terrorist attacks worldwide struck soft targets while only 27% percent of attacks struck hard targets. In the U.S. alone, 90% of attacks were aimed at soft targets from 2001 to 2005.

The reason vehicular attacks occur so frequently is due to a lack of known prevention techniques. Three main factors make vehicular attacks particularly hard to prevent: the minimal preparations required make perpetrators hard to detect, the accessibility of vehicles, and the difficulty of fully securing open pedestrian areas close to roadways.

External Motivators

Perpetrators have used this style of attack for numerous reasons, including religious extremism, far-right extremism, anger, and one particular attack may have been linked to the influence of drugs. No matter the reason, these attacks can be deadly and have large implications.

Religious Extremism

Religious extremism is one of the most prominent reasons individuals are motivated to carry out a vehicular attack. In 2010, Al Qaeda’s magazine, Inspire called for followers to use vehicles to “mow down the enemies of Allah” in crowded locations. Similarly, In 2014, ISIS made a similar call to run over nonbelievers with vehicles. Since then, there has been a growing wave of vehicular attacks across Western Europe and the U.S., with 18 attacks since 2014, and 11 in 2017 alone. 

From January 2014 through May 2017, 7% of all radical religious extremism-related terror plots in Western Europe were vehicular, yet they caused 45% of injuries and 37% of deaths. While most of these attacks were claimed by ISIS or individuals inspired by radical extremism, other groups have been known to use this attack method.

Radical Beliefs

Far-right extremists have also utilized vehicular attacks. One of these instances included the white supremacist rallies in Charlottesville, Virginia. During this attack James Alex Fields, Jr. drove his car into pedestrians in a crowded street, killing one and injuring at least 35 others.

Anger, Drugs & More

Some of the other attackers in Houston and New York were motivated by anger, while Richard Rojas may have carried out his 2017 attack in Times Square under the influence of drugs. The media has hypothesized that Alek Minassian’s vehicular attack in Toronto may have been motivated by anti-female beliefs. These examples demonstrate that vehicular attacks are becoming an increasing threat to public safety from individuals inspired by a variety of ideologies.

Preventative Measures

Given the accessibility of vehicles and the lack of training needed to carry out a vehicular attack, these threats are incredibly hard to predict or prevent. The New York Police Department and the FBI have implemented programs to make owners of rental vehicle businesses aware of suspicious behaviors that could potentially be related to attack planning. In an effort to reduce the threat, the British government has considered checking names of individuals renting vehicles against terrorist watch lists. However, in the 11 vehicular attacks carried out in Western Europe since 2014, only five involved rented vehicles – the others were either owned or stolen.

Additionally, physical safety measures such as bollards or steel tire spikes in public places can stop a vehicular attack but may not entirely prevent one. The recent redesign of Times Square in New York City included 196 bollards, granite benches, and raised granite curb caps to protect the large pedestrian area. Even with the added security, these measures did not thwart an incident there in May 2017. In this attack one person was killed and 20 injured by a vehicle aimed at pedestrians. Officials do agree, though, that these barriers stopped the vehicle from continuing after it hit a bollard, likely saving more from being killed or injured.

Given that vehicular attacks are highly effective in terms of fatalities and injuries, new methods are needed to help identify these threats in advance, allowing law enforcement to thwart vehicular attacks before they occur.

Lumina’s risk sensing capabilities illuminate areas of emergent unrest by monitoring online behavioral patterns consistent with the means and motivation of attack planning. By predictively identifying these online behavioral patterns, Lumina empowers organizations and venues to identify and mitigate potential threats to their physical security.

Predicting and Preventing Suicide Through AI

Predicting and Preventing Suicide Through AI

It’s not always easy for young people to articulate their problems. A student who regularly attends class and receives good grades could also be fighting an addiction. A teen constantly smiling for Instagram photos could actually be depressed. For friends and family of the person struggling, recognizing the warning signs of distress might not come easily.

 

Artificial intelligence can act as a voice for people dealing with various internal issues. It can also notify loved ones or even officials when a person needs help. The following two stories serve as examples of potential tragedies that could be avoided thanks to artificial intelligence:

 

Using Artificial Intelligence to Fight Cyber Bullying

Hailey was in her dorm room staring at her phone. A stranger had posted another fake story about her. Hailey knew if she reported it, the imposter would just create a new account or use a website that allows anonymous posts.

Hailey is one of more than 20% of college students being cyberbullied. She struggled with bullying and depression throughout her first two years of college before her friends and family were able to help her, but she could have gotten help a lot sooner with artificial intelligence. As soon as the menacing messages appeared, cutting-edge predictive analytics paired with human analysis could have combatted the issue much earlier.

 

Catch Suicidal Tendencies Early with Artificial Intelligence

Ana had been a star student in high school. She held a part-time job, ran track and was in a serious relationship. During her freshman year of college, she became increasingly depressed. One night she texted heart emojis to all her friends, wrote a goodbye letter to her parents, and attempted suicide. Ana’s friends found her and called 911 in time.

While she was lucky, suicide has risen to become the second-leading cause of death among Ana’s age group. Ana, and so many others like her, could have benefited from help and treatment as soon as predictive analytics powered by artificial intelligence flagged her online searches and habits as possible suicidal tendencies.

 

Meet Radiance.

As mental health problems become more common, and troubling behavior migrates online where it is harder to identify using traditional methods, many schools are struggling to adapt. To face these new challenges, innovative solutions are needed.

What if a sophisticated system could immediately alert student services to the problems their students face, like what should have happened for Hailey and Ana. The idea of counselors and health care professionals being guided to students’ darkest struggles is not some distant future. It’s possible today thanks to Lumina – a Predictive Analytics firm which uses artificial intelligence and open-source data to combat some of society’s most pressing issues. Powered by cutting-edge artificial intelligence and human analysis, Lumina’s newest solution can identify harmful behavior online and alert people who can help.

By working with schools, Lumina can help counselors, student services, and even security officers adapt to new digital landscapes related to bullying, mental health, drug misuse, and other challenges. With new threats emerging every day, taking full advantage of artificial intelligence will allow schools to meet these challenges head-on.   

 

3 Reasons Why Today’s Music Events are so Vulnerable to Terror

3 Reasons Why Today’s Music Events are so Vulnerable to Terror

The very nature of popular music events makes them attractive for terrorists and extremely difficult to defend. Here are a few reasons why these targets aren’t going away.Open-air festivals and concerts provide a particular challenge for law enforcement officials charged with keeping people safe. Violent extremists have targeted these events to sow chaos and destruction in places where people should feel comfort and enjoyment. 

Sprawling Event Venues & Loud Volume

Today’s event venues are often held in large areas of open space. In such circumstances, there are simply too many people unprotected from outside elements. Following an Ariana Grande concert in May 2017, Salman Abedi, a British citizen of Libyan descent, detonated a suicide bomb during concertgoer’s exit from the show.  More than 800 people were injured, and Abedi took the lives of 22 individuals.  Abedi had been a “subject of interest” for MI5 in 2015 and had been reported to authorities as many as five times by leaders of the Muslim community in Manchester, but the service had no reason to take further action at the time. The attack took place at the Manchester Arena, where approximately 14,200 people were attending the event.  The improvised explosive device, packed with nuts, bolts, and screws to act as shrapnel, was detonated in the foyer of the arena following the last performance of the evening.  The bomb was so deadly that it killed people over 65 feet away from the explosion’s source.  The attack had added tragedy due to the type of casualties: out of the 139 people who needed hospitalization or were severely injured, 79 were children. 

Cultural Significance

Soft targets like music festivals and concerts offer terrorists practical and symbolic value. The symbolism of attacking Westerners who are enjoying themselves is what makes it an attractive target.  On November 13, 2015, three gunman stormed into the Bataclan theater in Paris and killed 89 people attending a heavy metal concert.  In a night that was coordinated to the last detail, the brunt of the damage came in the tight, dark spaces of the concert hall. There was little security, as the perpetrators killed three people on the sidewalk in front of the venue and then simply walked in to carry out the rest of their attack. The killers were part of an ISIS cell operating out of Belgium and France and had come in response to French and American airstrikes in Syria. In the nearly three years following the incident, Paris’ music scene has almost returned to normal, but the ubiquitous police presence is a reminder that danger still remains.

Masses of People

Events with large crowds will always be attractive targets to extremists, whether the reason stems from religious extremism or a political motive. In the deadliest mass shooting in U.S. history, 58 people were killed when Stephen Paddock opened fireon a Jason Aldean concert in Las Vegas, Nevada. Over 22,000 people were in attendance when Paddock began spraying bullets indiscriminately into the crowd. When a threat goes undetected before the attack, it can be very difficult to thwart once it is in motion due to the unorganized chaos that follows. Frighteningly, Paddock had reserved hotel rooms overlooking the Lollapalooza music festival in Chicago a few months before the Vegas massacre and was reported to have searched online for information regarding Fenway Park and associated Boston music festivals.

Despite the efforts of officials in recent years to prevent attacks on soft targets, large-scale casualties have still occurred at musical events with an alarming frequency. Officials recognize that these targets are difficult to harden by their very nature. Therefore, new approaches are needed to detect and monitor relevant activity that may indicate the planning of such attacks.

Lumina’s risk sensing capabilities illuminate areas of emergent unrest by monitoring online behavioral patterns consistent with the means and motivation of attack planning. By predictively identifying these online behavioral patterns, Lumina empowers organizations and venues to identify and mitigate potential threats to their physical security.

oyment. 

Sprawling Event Venues & Loud Volume

Today’s event venues are often held in large areas of open space. In such circumstances, there are simply too many people unprotected from outside elements. Following an Ariana Grande concert in May 2017, Salman Abedi, a British citizen of Libyan descent, detonated a suicide bomb during concertgoer’s exit from the show.  More than 800 people were injured, and Abedi took the lives of 22 individuals.  Abedi had been a “subject of interest” for MI5 in 2015 and had been reported to authorities as many as five times by leaders of the Muslim community in Manchester, but the service had no reason to take further action at the time. The attack took place at the Manchester Arena, where approximately 14,200 people were attending the event.  The improvised explosive device, packed with nuts, bolts, and screws to act as shrapnel, was detonated in the foyer of the arena following the last performance of the evening.  The bomb was so deadly that it killed people over 65 feet away from the explosion’s source.  The attack had added tragedy due to the type of casualties: out of the 139 people who needed hospitalization or were severely injured, 79 were children. 

Cultural Significance

Soft targets like music festivals and concerts offer terrorists practical and symbolic value. The symbolism of attacking Westerners who are enjoying themselves is what makes it an attractive target.  On November 13, 2015, three gunman stormed into the Bataclan theater in Paris and killed 89 people attending a heavy metal concert.  In a night that was coordinated to the last detail, the brunt of the damage came in the tight, dark spaces of the concert hall. There was little security, as the perpetrators killed three people on the sidewalk in front of the venue and then simply walked in to carry out the rest of their attack. The killers were part of an ISIS cell operating out of Belgium and France and had come in response to French and American airstrikes in Syria. In the nearly three years following the incident, Paris’ music scene has almost returned to normal, but the ubiquitous police presence is a reminder that danger still remains.

Masses of People

Events with large crowds will always be attractive targets to extremists, whether the reason stems from religious extremism or a political motive. In the deadliest mass shooting in U.S. history, 58 people were killed when Stephen Paddock opened fireon a Jason Aldean concert in Las Vegas, Nevada. Over 22,000 people were in attendance when Paddock began spraying bullets indiscriminately into the crowd. When a threat goes undetected before the attack, it can be very difficult to thwart once it is in motion due to the unorganized chaos that follows. Frighteningly, Paddock had reserved hotel rooms overlooking the Lollapalooza music festival in Chicago a few months before the Vegas massacre and was reported to have searched online for information regarding Fenway Park and associated Boston music festivals.

Despite the efforts of officials in recent years to prevent attacks on soft targets, large-scale casualties have still occurred at musical events with an alarming frequency. Officials recognize that these targets are difficult to harden by their very nature. Therefore, new approaches are needed to detect and monitor relevant activity that may indicate the planning of such attacks.

Lumina’s risk sensing capabilities illuminate areas of emergent unrest by monitoring online behavioral patterns consistent with the means and motivation of attack planning. By predictively identifying these online behavioral patterns, Lumina empowers organizations and venues to identify and mitigate potential threats to their physical security.