AI in Law Enforcement: 6 Top Challenges and How AI Can Help

Law enforcement investigators and analysts encounter significant challenges that are compounded by emerging technology, advanced criminal networks, tradecraft changes, borderless crimes, manual processes, and access to data and solutions.

Gaining an understanding of AI in law enforcement will help agencies leverage the latest technologies while upholding public safety expectations and maximizing limited resources. To conduct investigations that provide accurate information, law enforcement needs access to cutting-edge AI technology.

Law enforcement challenges

First, let’s explore some of the challenges facing law enforcement agencies across state, local, and federal jurisdictions.  

Criminals are more technically sophisticated

Criminals’ mastery of technology is exceeding that of law enforcement, often putting the good guys at a disadvantage. Despite the adage, crime really does pay — giving bad actors significant financial incentives to keep honing their techniques. State and local law enforcement agencies are on the front lines of dealing with many of these technology-driven crimes but may lack the specialized expertise needed to investigate them.

There’s too much data

While the internet offers a vast amount of potential evidence and leads, the sheer volume of data makes it difficult to find what’s needed. Without sophisticated search tools and data analysis capabilities, important details never come to light, connections between data points are missed, and valuable insights are buried beneath irrelevant details.  

Manual processes hinder investigations

As budgets are cut, agencies face staffing shortages — causing increased workloads that lead to a backlog of non-violent crimes, missed investigation outcomes, and delays in closing cases. Experts must often fill in the gaps, preventing them from applying their specialized skills and detailed knowledge to investigative searches.  

Data must be verified

Together with too much data is the need to ensure the veracity of the data. Improper citations or missing data provenance can be a dead end for investigators. Uncited information could be unreliable, fabricated, taken out of context, or simply inaccurate. The lack of verifiability and context can weaken a case and hinder efforts to hold criminals accountable.  

Social networks are extensive

Criminals often take advantage of social media communication, using anonymity and coded language to hide their actions. The anonymous nature of social media makes it even more challenging to map identities and relationships across social networks and back to real people.  

Technology-powered crime can include:

• Ransomware attacks — Criminals encrypt a victim’s data and demand payment in cryptocurrency to restore access.
• Phishing and deepfake scams — AI-generated deepfake voices and videos are used to impersonate executives or family members to steal money or data.
• Credential stuffing — Automated bots use stolen username-password combinations to gain access to online accounts.
• AI-powered malware — Smart malware adapts in real time to evade detection by cybersecurity tools.
• Cryptocurrency laundering — Criminals use blockchain technology and crypto mixers to hide illicit transactions.
• AI-generated documents — AI can be used to create fake IDs, passports, or bank statements for fraud.
• Spyware and stalkerware — Malware is used to secretly monitor individuals’ phones and computers.
• Hacked security cameras and IoT devices — Criminals hijack smart home devices to spy on or extort victims.
• 3D-printed weapons — Criminals manufacture untraceable firearms using 3D printers.
• AI-powered voice spoofing — Criminals use AI to clone voices and execute scams.
• Autonomous drone smuggling — Drones transport drugs, weapons, or contraband across borders and into prisons.
• Facial recognition spoofing — High-quality masks, AI-generated faces, or deepfake videos are used to bypass security systems.
• Synthetic identity fraud — AI is used to generate fake identities that look real enough to pass financial checks.

How to defeat crime with AI

What role can AI play in law enforcement for detecting, investigating, and reducing crime?

Match names against watchlists

A criminal will use an alias to hide his record, evade law enforcement, and conduct identity theft. AI-powered fuzzy name matching compares and matches names that have slight differences, variations, or errors. It parses data sources with different spellings, abbreviations, missing information, or typographical errors. Unlike exact or rules-based matching, fuzzy matching has the power and flexibility to cross-reference similar names across languages.

Predict crime

Predictive policing employs data analysis and machine learning algorithms to identify areas, times, and patterns where crimes are most likely to occur. It has two main goals: proactively prevent crime and allocate police resources more efficiently. Predictive policing uses machine learning to rapidly analyze large data sets. After analyzing the data, the AI algorithms identify patterns that predict where and when crime will occur. These patterns, which are not always apparent to humans, can then be used to direct law enforcement resources where they’re needed most.  

Map social media networks

Criminals routinely use social media to buy and sell illicit items, plan crimes, and communicate with accomplices. Specialized AI tools can map relationships within social networks to better understand known connections as well as uncover previously hidden ones. Patented algorithms break through the boundaries of influence measurement to precisely score and prioritize accounts that are most important within a social relationship network. This use of AI in law enforcement provides immediate visibility into who’s “pulling the strings” in a criminal network.

Intelligent search augmentation and sentiment analysis

Law enforcement organizations can use an open-source intelligence (OSINT) solution to search publicly available data — including social media — from billions of top-level domains. But it can be daunting to search that much data and know which terms will yield the desired results. A search bar that incorporates AI and data enrichment, including keyword synonyms and translations, can expand every query invisibly to discover more pertinent results. When thousands of search results are returned, AI sentiment analysis and violent intent detection can help narrow them down. These intuitive search refinement tools enable law enforcement to quickly find what matters and prioritize results.

The ability to access cutting-edge AI technologies can significantly improve the speed of obtaining knowledge, analyzing data, and achieving successful outcomes in law enforcement investigations.