Navigating the digital deluge : Preparing for the future of evidence
The plethora of digital evidence available to law enforcement is bringing actionable insights to investigations and strengthening cases for prosecution , but it must be managed with care
By Chief Philip Lukens , Ret .
Reprinted with permission from Police1 . com
The New Jersey Police Chief Magazine | June 2024
The explosion of digital evidence from multiple sources Law enforcement agencies are facing a staffing crisis due to a decline in applications and an increase in resignations and retirements . One of the ways to cope with this challenge is to leverage technology , such as automated license plate recognition ( ALPR ) systems , body cameras , drones and community sources , to augment capabilities of sworn officers . These technologies can generate a vast amount of digital evidence that can help law enforcement agencies to locate and apprehend suspects , prevent or solve crimes and recover stolen property . ALPR systems , for instance , are computer-controlled camera systems that can capture and analyze license plate images from vehicles and compare them to databases of vehicles of interest . ALPR systems can also access archived data from real time crime center ( RTCC ) databases to provide insights for investigators on cold cases .
However , digital evidence is not only useful for law enforcement , but also for prosecution . By providing accurate and reliable evidence of the location , movement and association of vehicles and suspects , as well as video , audio and biometric data , digital evidence can help prosecutors to build strong cases , corroborate witness statements and refute defense claims . Digital evidence can also help prosecutors to identify patterns of criminal activity , link multiple cases and establish criminal intent and motive . Furthermore , digital evidence can help prosecutors to streamline the discovery process , reduce the need for plea bargains and increase the conviction rate .
The challenges of managing and analyzing unstructured data While digital evidence can be a powerful tool for law enforcement and prosecution , it can also pose some challenges in terms of data management and analysis . Digital evidence is voluminous , complex and dynamic , requiring a lot of storage space , processing power and analysis skills . Moreover , digital evidence is often unstructured , meaning that it does not have a predefined format or schema , such as text , images , audio or video . Unstructured data is difficult to organize , search , and interpret , as it may contain noise , ambiguity or inconsistency . Furthermore , unstructured data is subject to various legal and ethical standards , such as privacy , transparency and accountability , which require proper policies , procedures and safeguards to ensure compliance .
Therefore , law enforcement agencies need a system that not only collects digital evidence , but also processes it into actionable insights , which is crucial for effective law enforcement and case resolution . Such a system should be able to filter , organize and analyze the unstructured data , as well as generate reports , alerts and recommendations . Moreover , such a system should be able to protect the data from unauthorized access , modification and deletion , as well as encrypt , backup and audit the data . Furthermore , such a system should be able to integrate the data with other sources , such as video , audio and biometric data , to provide a comprehensive picture of the situation .
Best practices for preparing law enforcement and prosecutors for digital evidence One of the best practices for preparing law enforcement and prosecutors for digital evidence is to provide them with adequate training and education on the technical , legal and ethical aspects of regulations for accessing , sharing and disclosing digital evidence . Moreover , they need to understand the ethical and social implications of using digital evidence , such as the impact on privacy , civil rights and public trust .
Another best practice for preparing law enforcement and prosecutors for digital evidence is to provide them with adequate resources and support for managing and analyzing digital evidence . Law enforcement and prosecutors need to have access to sufficient storage space , processing power and analysis tools for handling digital evidence . Furthermore , they need to have access to reliable and secure networks , systems and platforms for communicating and collaborating with digital evidence .
The importance of AI-enabled image and data analysis in surfacing key evidence One of the ways to enhance the processing and analysis of digital evidence is to employ artificial intelligence ( AI ) techniques , such as machine learning , computer vision and natural language processing , to enhance the image and data analysis capabilities of the system . AI can help to automate the verification and classification of the unstructured data , as well as to
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