A world of artificial intelligence and augmented reality provides criminals an avenue to avoid detection by law enforcement and continue an unlawful lifestyle. A lack of essential technical skills, artificial intelligence, and augmented reality capabilities within law enforcement will provide an advantage to criminals within cyber and physical environments. Local, state, and federal law enforcement agencies that solely focus on “brick-and-mortar” crimes, or crimes committed in the physical sense, will diminish their usefulness within the next decade.
As technology evolves so does crime, and the question is whether state and local police lack capabilities to safely prevent and mitigate crime within their communities. Cold cases, forensic bias, and human error will be history within the next decade. The emergence of 5G connectivity will enable artificial intelligence and augmented reality to enhance law enforcement interaction with their respective community, consequentially resolving crime with evidence-based tactics at unprecedented speed.
First, it is crucial to recognize that police will require technical skills to resolve issues and solve crimes as law enforcement agencies strive to understand malicious cyber activity and the effects on citizens. Identity theft, financial crimes, unlawful access to personal and business software and web-based platforms, and confidential information leakage are only a few of the many cybercriminals’ targets. Federal law enforcement does not have the capacity or capabilities to prevent nor prohibit cyber criminality at the local level. Therefore, it is the responsibility of a chief of police and staff to ensure police are trained and remain ready in the cyber realm.
Facial recognition technology, also known as FRT, compares multiple images to match a human’s face, confirming the identity of a suspect or victim (Service, 2020). Law enforcement agencies need this capability to eliminate errors in human bias and witness manipulation. The images are pulled from a database of mugshots, body camera images, social media photos, and other public information sources within legal capacity (Service, 2020). The storage of biometrics poses security concerns for the server and data privacy, which will be addressed later.
Augmented reality, if used correctly, keeps the officer, residents, and local patrons safe. For example, officers arriving at a patrol scene can utilize AR glasses to obtain information regarding previous criminal history in the area, scan license plates, and even find exits to a building in pursuit of a suspect (Mirkow & Gelles, 2020). Additionally, an officer can identify whether a person has an immediate health condition that needs attention, preparing the officer to perform life-saving procedures with advised medical equipment (Mirkow & Gelles, 2020).
Evidence-based policing is arguably the most revolutionary technology for law enforcement. The technology can provide real-time advisory to law enforcement agencies, giving researchers and computer programmers access to the most significant outcome of different tactics (Mirkow & Gelles, 2020). The theory proves vital at the local and federal levels, including drug raids, border patrol, and even domestic violence disputes (Mirkow & Gelles, 2020). Not only will this capability increase efficiency in crime-solving, but it will also provide an accurate account of the crime with sufficient resolution.
While many technologies await law enforcement utilization, three key opportunities lie in bringing innovative technology into law enforcement. Quality risk management, effective crime prevention, and fostering a positive community-police relationship create a holistic approach to building an effective and accountable police force while preventing and solving criminal activity promptly.
Quality Risk Management
According to the Department of Justice (2018), the use of force and pursuit operations rank as the highest-risk activities in law enforcement. Incorporating technology in risk management can minimize physical harm to citizens and officers and property damage by removing human error and potentially extreme lethality. The data collected from technology can also be used in a court of law to determine guilt or innocence. For example, a pursuit operation incorporating an unarmed drone and thermal imaging will assist law enforcement in tracking a suspect regardless of the time of day. Additionally, facial recognition technology with accurate data management can confirm whether the right person is in custody.
The use of force is a controversial and sensitive topic and promotes strain between the police force and the local community. While there is no technology to eliminate human bias, automated tools help reduce the risk of bias during traffic and routine police stops. Emerging technology, such as artificial intelligence, machine learning, and augmented reality, provides evidence-based policing, which provides analytical hypotheses on the location and linkage of suspects with advanced forensic techniques in a fraction of the time it takes for detectives to conclude a solution (Mirkow & Gelles, 2020). The analysis also provides police a buffer between unconscious bias and their duty to the community in crime prevention (Mirkow & Gelles, 2020).
Cloud storage and edge computing can provide real-time data to police patrols to identify whether a vehicle or the driver has previous law violations or warrants without pulling the vehicle over and obtaining identification, an emerging tool known as the Fog-to-Cloud System (Rejiba, Masip-Bruin, Jurnet, Marin-Tordera, & Ren, 2018). A mobile server connected to a secured cloud database providing real-time information on a vehicle and its driver reduces potentially dangerous interaction between citizens and police and reduces the number of resources and amount of time in traffic stops.
Effective Crime Prevention
Over 8 million tips to help locate missing children are provided to a team of approximately 25 people (Mirkow & Gelles, 2020). Sifting and analyzing this information could take decades to find these children, and in most cases that will be too late. Deploying artificial intelligence to analyze this data can exponentially reduce time spent and potentially locate the child to reunite them with their families and hold the perpetrators accountable (Mirkow & Gelles, 2020). Technology such as closed-circuit television, or CCTV, provides a live feed to police stations, which provides first-hand information in place of physical police presence (Mirkow & Gelles, 2020). This technology is implemented in numerous locations globally and already proves a powerful tool in crime prevention. CCTV assisted Indian law enforcement to find nearly 3,000 missing persons in just four days (Mirkow & Gelles, 2020).
Law enforcement and community relations are a sensitive topic in the 21st century. While there are historical aspects to consider, a way forward requires implementing augmented reality, or AR, to train, assess, and employ police officers — “research on adult learning theory suggests that hands-on, problem-solving learning approaches foster skills and knowledge that translate back into behavior change” (Office of Community Oriented Policing Services, 2018). Statistics have also shown that de-escalation exercises and tactics training significantly reduced the amount of injury and death of police officers and citizens over the past decade (Office of Community Oriented Policing Services, 2018), consequently reducing strained fear relationships within the local community. Augmented reality goggles provide hands-on training with real-world scenarios without exhausting police officers’ already strained resources in a fraction of the time. The AR toolkit can provide various scenarios, including cultural sensitivity, psychological crisis, and even pursuit operations.
While emerging technology is advantageous to law enforcement, technical and organizational challenges arise with implementation. The first challenge is the technical expertise required to maintain databases and networks of innovative technology. The resources and personnel necessary to maintain such technology consist of a financial budget that some precincts and cities may not afford without government assistance. Secondly, law enforcement will require an in-depth defense of database and network access. Cybersecurity professionals can provide training, access and authentication protocols, and advisory to law enforcement and local government leadership on why and how to protect the infrastructure from common and unprecedented malicious activity. The protection of cloud data is crucial in law enforcement as the justice system relies on the information, leaving little to no room for error in data misconfiguration, privacy breaches, and ransomware.
As mentioned before, the duty of the police requires preventing crime and protecting citizens from crime. Privacy and confidentiality breaches brought to public attention by brute force remain among the top cybersecurity threats in the United States. Shortly after the EU passed the General Data Protection Regulation (GDPR), the CEOs of Apple and Microsoft called on the U.S. to pass new privacy laws (Burt, 2019). The technologies mentioned above, facial recognition and augmented reality, retrieve and save sensitive data. Therefore, the security measure must be implemented through the development and deployment phase of police technology to prevent leaks. Considerations in information security are containerized applications, restricted access, and zero-trust security architecture (NIST, 2020).
As previously mentioned, sensitive data stored in physical or cloud servers pose a higher risk of manipulation, theft, and breaches. If law enforcement and the justice system rely on data to identify, confirm, and prosecute suspects or identify victims, data security’s vitality is critical to operations. Disruption, distortion, and denial of data are the ultimate threats to police investigations and prosecution (Belani, 2020). A robust cloud security strategy can mitigate threats. Furthermore, an emergency response plan and the backup server can eliminate disruption in operations (Belani, 2020).
Artificial intelligence, employed lawfully, can and will increase productivity and security. Machine learning, a derivative of artificial intelligence, studies the network’s packet traffic, detecting malicious packets’ intrusion. In conjunction with machine learning, AI can build reports and analysis for law enforcement within minutes, reducing the number of staff hours necessary to account for accurate data and analysis. AI/ML can pose a risk to security, as malicious actors utilize this technology to access networks (Belani, 2020). For example, sophisticated criminals can use machine learning to access law enforcement data and manipulate a potential suspect’s supporting crime violation evidence or remove a victim’s identity.
Belani, G. (2020). 5 Cybersecurity Threats to Be Aware of in 2020. Retrieved from IEEE Computer Society : https://www.computer.org/publications/tech-news/trends/5-cybersecurity-threats-to-be-aware-of-in-2020
Burt, C. (2018, September 5). Malware Targeting Biometric Security and Strong Authentication Observed in Brazil Bank Attack . Retrieved from BiometricUpdate : https://www.biometricupdate.com/201809/malware-targeting-biometric-security-and-strong-authentication-observed-in-brazil-bank-attacks
Copple, C., & Copple, J. (2018). Risk Management in Law Enforcement: Discussions on Identifying and Mitigating Risk for Officers, Departments and the Public. Washington DC: Office of Community Oriented Policing Services. Retrieved from https://cops.usdoj.gov/RIC/Publications/cops-w0865-pub.pdf
Mirkow, A., & Gelles, M. (2020). Deloitte. Retrieved from The Future of Policing : https://www2.deloitte.com/us/en/pages/public-sector/articles/future-of-policing-and-law-enforcement-technology-innovations.html
NIST. (2020). SP 800-207 (Second Draft) Zero Trust Architecture. doi:10.6028/NIST.SP.800-207
Office of Community Oriented Policing Services. (2018). Risk Management in Law Enforcement. Department of Justice , Washington DC . Retrieved from https://cops.usdoj.gov/RIC/Publications/cops-w0865-pub.pdf
Rejiba , Z., Masip-Bruin, X., Jurnet, A., Marin-Tordera , E., & Ren, G.-J. (2018). F2C-Aware: Enabling Discovery in Wi-Fi Powered Fog-to-Cloud (F2C) Systems. 2018 6th IEEE Internation Conference on Mobile CLoud Computing, Services, and Engineering (MobileCloud) (pp. 113-116). Bamberg: IEEE. doi:10.1109/MobileCloud.2018.00025
Service, T. N. (2020, September 25). Congressional Research Service Report: “Facial Recognition Technology & Law Enforcement: Select Constitutional Considerations.’.Retrieved from Targeted News Service : https://advance-lexis-com.proxy.library.georgetown.edu/document/?pdmfid=1516831&crid=1dd17cda-596d-4235-aa9a-67fc8a26e065&pddocfullpath=%2Fshared%2Fdocument%2Fnews%2Furn%3AcontentItem%3A60XN-HD91-JC11-13K6-00000-00&pdcontentcomponentid=299219&pdteaserkey=