Thinking Like a Criminal: Can AI Stop Crime Before It Starts, or Will It Create the Perfect Criminal?
Cops vs. Code: Will AI Outsmart Criminals or Outpace Our Rights?
Introduction
A recent joint report by the National Security Agency (NSA), the Cybersecurity and Infrastructure Security Agency (CISA), Federal Bureau of Investigation (FBI) and other allied cybersecurity agencies warns of ongoing cyberattacks by pro-Russia hacktivists targeting critical infrastructure in North America and Europe. These attacks focus on operational technology (OT) systems in water, energy, dams, and agriculture sectors. The report details mitigation strategies like stronger passwords, multi-factor authentication, and limiting internet access for OT systems to defend against these attacks. Checkout the factsheet here.
Earlier, the CISA had issued warnings about cyberattacks from China. In the latest report from the UK's Information Commissioner’s Office (ICO), Britain saw a surge in ransomware attacks in 2023, with critical sectors like government and utilities experiencing more attacks than ever before. This follows a trend of increasing ransomware incidents since 2019.
In another report on statista.com, Anna Fleck explains how the financial burden of cybercrime is expected to skyrocket, reaching $13.82 trillion globally by 2028, compared to $9.22 trillion in 2024. This surge is fueled by two key trends:
1. Growing Online Activity: As more people rely on the internet for work and personal needs, they become vulnerable to cyberattacks.
2. Evolving Attack Strategies: Cybercriminals are constantly developing more sophisticated techniques and tools, making it easier for them to exploit weaknesses.
It is evident that the fight against crime has entered a new era. The age-old cat-and-mouse game between law enforcement and criminals has taken a new turn with the advent of artificial intelligence (AI). As technology continues to advance, the possibilities for crime prevention have expanded exponentially. However, this also raises ethical concerns regarding privacy, surveillance, and the potential for AI to be misused or manipulated.
Artificial intelligence (AI) promises to revolutionize crime prevention, but a crucial question emerges: Can AI outsmart criminals, or will it inadvertently empower them?
In this essay, we explore few thoughts and ideas related to:
The potential of AI in crime prevention
The disruptive startup ideas shaping this field
The ethical considerations that must accompany these developments
A future timeline for the transformation of this ecosystem
Predictive Policing
A core strength of AI lies in its ability to analyze vast datasets and identify patterns. This strength allows predictive policing to be one of the most promising applications of AI in crime prevention.
Predictive policing, a burgeoning field, utilizes AI algorithms to analyze past crimes and predict areas or individuals at high risk of future criminal activity [1]. By analyzing vast amounts of data, AI algorithms can identify patterns and predict where crimes are likely to occur. This allows for targeted interventions, such as increased patrols in high-risk areas or social programs for vulnerable individuals.
Startups like PredPol (now Geolitica acquired by SoundThinking) and Palantir have already made significant strides in this area, using AI to help law enforcement allocate resources more efficiently and effectively.
Probable Timeline:
- Present: Predictive policing algorithms are already being used by law enforcement agencies in several countries.
- Near Future (1-3 years): Improved AI algorithms will enhance the accuracy of predictive policing, leading to better resource allocation and crime prevention strategies.
- Mid-term (3-5 years): Integration of real-time data from IoT devices and social media platforms will further refine predictive models, enabling law enforcement to respond proactively to emerging threats.
- Long-term (5+ years): Predictive policing systems will become more autonomous, leveraging advanced AI to adapt to changing crime trends and societal dynamics.
Crime Detection and Recognition
AI-powered surveillance systems can analyze video feeds in real-time to detect and recognize suspicious behavior or individuals. Startups like DeepCam and AnyVision (now Oosto) are developing advanced facial recognition and object detection technologies that can be used to enhance security in public spaces and prevent crimes before they occur.
Probable Timeline:
- Present: Facial recognition and object detection systems are already being deployed in airports, train stations, and other high-security areas.
- Near Future (1-3 years): Continued advancements in AI will lead to more accurate and reliable crime detection and recognition systems, expanding their use in retail, transportation, and urban environments.
- Mid-term (3-5 years): Integration of biometric data and behavioral analytics will enable AI systems to identify potential threats with greater precision, reducing false positives and enhancing overall security.
- Long-term (5+ years): AI-powered surveillance will become ubiquitous, with smart cities leveraging advanced sensor networks and predictive analytics to prevent crime on a large scale.
Cybersecurity and Fraud Prevention
As more aspects of our lives move online, the threat of cybercrime and fraud has become increasingly prevalent. AI can play a crucial role in detecting and mitigating these threats by analyzing network traffic, identifying anomalous behavior, and predicting future attacks. Firms like Darktrace and Cylance (acquired by Blackberry) are leading the way in AI-driven cybersecurity solutions.
Probable Timeline:
- Present: AI-based cybersecurity tools are already being used by businesses and governments to detect and respond to cyber threats.
- Near Future (1-3 years): Continued advancements in AI will lead to more sophisticated cybersecurity algorithms capable of detecting and mitigating even the most advanced cyber attacks.
- Mid-term (3-5 years): Integration of AI with blockchain technology will enhance the security and transparency of digital transactions, reducing the risk of fraud and identity theft.
- Long-term (5+ years): AI-powered cybersecurity will become increasingly autonomous, with systems capable of learning from past attacks and adapting in real-time to new threats.
The Dark Side of the Coin: Ethical Considerations
While the potential benefits of AI in crime prevention are undeniable, it is essential to consider the ethical implications of these technologies. The proliferation of AI-powered surveillance could raise privacy concerns. Additionally, there are concerns about potential biases in the data used to train these algorithms, which could lead to discriminatory policing practices [2]. As Will Douglas Heaven point outs [3]:
“Yet increasing evidence suggests that human prejudices have been baked into these tools because the machine-learning models are trained on biased police data.Far from avoiding racism, they may simply be better at hiding it. Many critics now view these tools as a form of tech-washing, where a veneer of objectivity covers mechanisms that perpetuate inequities in society.”
Apart from privacy concerns and algorithmic bias, the easy availability of AI tools and data has a huge potential for misuse. Regulatory frameworks and ethical guidelines will be essential to ensure that AI is used responsibly and ethically in the fight against crime.
Utopian Scenario: AI Preventing Crime Before It Starts
In an ideal scenario, AI could be utilized to analyze vast amounts of data to identify patterns and predict potential criminal activity before it occurs. This proactive approach could involve monitoring social media, analyzing behavioral patterns, and identifying individuals who may be at risk of engaging in criminal behavior.
By intervening early through targeted interventions such as counseling, education, or social support, AI could potentially prevent crime before it happens. This approach would aim to address the root causes of criminal behavior, such as poverty, lack of opportunities, or mental health issues, thereby reducing overall crime rates and enhancing public safety.
Dystopian Scenario: AI Creating the Perfect Criminal
On the other hand, there are concerns that AI could be misused or manipulated to facilitate criminal activities. For example, sophisticated AI algorithms could be used to develop highly effective cyberattacks, manipulate financial markets, or create realistic forgeries.
Furthermore, AI-powered surveillance systems could infringe upon privacy rights and enable authoritarian regimes to suppress dissent or target marginalized communities. In this scenario, rather than preventing crime, AI could potentially empower criminals by providing them with new tools and methods to carry out illegal activities more effectively.
The Human Element Remains Key: A Glimpse into the Future
While powerful, AI is not a silver bullet. Ultimately, AI is a tool, and its effectiveness depends on human oversight and ethical implementation. The future will depend on how AI technologies are developed, regulated, and deployed.
Ethical considerations, transparency, accountability, and oversight will be crucial in ensuring that AI is used responsibly and for the benefit of society. Additionally, interdisciplinary collaboration involving experts from fields such as law, ethics, sociology, and computer science will be essential in navigating the complex challenges posed by AI in crime prevention and criminal justice.
Law enforcement agencies and policymakers must work alongside AI developers to ensure responsible use of this powerful technology. Crime prevention will likely see a shift towards a collaborative approach, where AI provides insights, and humans make informed decisions based on those insights.
Looking ahead, here's a possible timeline for the transformation of the crime prevention landscape:
2025-2030: Widespread adoption of AI-powered predictive policing with increased focus on addressing data bias and ethical considerations.
2030-2035: Rise of AI-powered cybercrime prevention systems with advanced threat detection capabilities.
2035-2040: Integration of AI with biometrics and advanced surveillance technologies, necessitating robust legal frameworks to protect privacy rights.
Probable Solutions, Business Opportunities and Startup Themes
Machine learning is a double-edged sword in cybersecurity. While hackers are using it to find weaknesses, it is also being used to prevent breaches by identifying vulnerabilities in hardware and software design. Machine learning can be trained to detect cyber threats and prevent them from accessing critical data. It can also automate many tasks, saving time and money. A report at semiengineering.com quotes David Maidment, senior director of market development at ARM (developer of energy-efficient processor designs):
“AI/ML is finding many roles protecting and enhancing security for digital devices and services. However, it is also being used as a tool for increasingly sophisticated attacks by threat actors. AI/ML is essentially a tool tuned for very advanced pattern recognition across vast data sets. Examples of how AI/ML can enhance security include network-based monitoring to spot rogue behaviors at scale, code analysis to look for vulnerabilities on new and legacy software, and automating the deployment of software to keep devices up-to-date and secure.”
This essay has already mentioned several disruptive products / platforms / startups that are already pushing the boundaries of crime prevention:
Predictive Policing: PredPol and Palantir
Crime Detection and Recognition: DeepCam and AnyVision
Cybersecurity and Fraud Prevention: Darktrace and Cylance
Next, let us take a deeper look at three main business / product / startup themes based on current trends.
Geospatial Crime Prediction
A company like "CrimePredicted.ai" that could develop AI models that not only predict high-risk areas but also forecast the specific type of crime likely to occur. This would allow for tailored prevention measures, like deploying decoy packages to deter theft or dispatching mental health professionals to areas with a high risk of domestic violence. Here are some key features of next-generation Geospatial Crime Prediction products equipped with AI offer cutting-edge features aimed at forecasting and preventing criminal activities
Data Integration: These products aggregate diverse datasets, including historical crime data, socioeconomic indicators, weather patterns, and urban infrastructure, to provide comprehensive insights into crime trends.
Machine Learning Algorithms: Advanced AI algorithms analyze vast amounts of data to identify patterns and correlations, enabling accurate predictions of when and where crimes are likely to occur.
Geospatial Visualization: Interactive maps and visualizations display crime hotspots, trends, and risk areas, allowing law enforcement agencies to allocate resources effectively.
Real-time Monitoring: These products provide real-time monitoring of crime incidents and dynamically adjust predictions based on new data, enhancing responsiveness to emerging threats. See Hexagon’s Safety & Infrastructure https://www.hexagonsafetyinfrastructure.com/.
Risk Assessment: AI-driven risk assessment tools prioritize areas and times with the highest likelihood of criminal activity, enabling proactive policing strategies and crime prevention efforts.
Predictive Analytics: Utilizing predictive analytics, these products forecast future crime trends, enabling law enforcement agencies to implement preemptive measures and interventions.
Community Engagement: Some platforms facilitate community involvement by allowing residents to report suspicious activities and provide feedback, fostering collaboration between law enforcement and the public.
Scalability and Customization: These solutions offer scalability to analyze data from small neighborhoods to entire cities, with customization options to tailor predictions to specific jurisdictions or types of crime.
Privacy Protection: Robust privacy mechanisms ensure that sensitive data is handled securely and that predictions are generated without compromising individual privacy rights.
Integration with Crime Fighting Tools: Seamless integration with existing crime-fighting tools and systems, such as police dispatch software and crime reporting platforms, streamlines workflows and enhances operational efficiency.
These features represent the state-of-the-art capabilities of next-generation Geospatial Crime Prediction products, empowering law enforcement agencies to proactively combat crime and enhance public safety.
AI-powered Security Cameras
Imagine "Hypervigil.ai" a company that could develop next-generation smart security cameras equipped with AI for facial recognition and anomaly detection. These cameras can identify suspicious behavior and alert authorities, even recognizing individuals on a watchlist in real-time. Here are some common features:
Facial Recognition: AI-powered facial recognition technology allows the camera to identify familiar faces and send customized alerts, enhancing home security.
Object Detection: These cameras can detect and classify objects, such as people, vehicles, animals, or packages, enabling more accurate alerts and notifications.
Activity Zones: Users can define specific areas within the camera's field of view to monitor closely, reducing false alerts triggered by irrelevant motion.
Two-Way Audio: Built-in microphones and speakers allow users to communicate with visitors or potential intruders remotely via the camera's app.
Person Detection: AI algorithms can differentiate between human movement and other types of motion, ensuring more relevant alerts and reducing false alarms.
Smart Integration: These cameras can integrate with other smart home devices and platforms, enabling features such as voice control and automation.
High-Resolution Imaging: Next-gen cameras often feature high-resolution sensors, such as 4K, to capture detailed footage for better identification and evidence.
Night Vision: Advanced infrared or other low-light imaging technologies enable clear video capture even in complete darkness.
Cloud Storage and Local Backup: Footage can be stored securely either in the cloud or locally, providing options for accessing and preserving video recordings.
Intelligent Alerts: AI algorithms analyze motion patterns and other factors to send relevant alerts, such as suspicious activity or potential security breaches.
These features represent advancements in smart security camera technology, offering users more sophisticated surveillance capabilities and peace of mind against smart criminals.
AI-driven Social Media Monitoring
"SocialPolice.ai" could be a startup that uses AI to analyze social media activity for keywords, threats, or extremist ideology. This allows for early intervention and potential de-radicalization efforts. These Next-generation Social AI products utilize advanced AI algorithms to analyze social media activity for keywords, threats, or extremist ideology, enabling early intervention and prevention of potential harm. Here are some key features:
Natural Language Processing (NLP): Advanced NLP capabilities allow the AI to understand and interpret text, enabling it to detect keywords, sentiment, and linguistic patterns indicative of extremist content or threats. See perspectiveapi.com.
Machine Learning Models: These products employ machine learning models trained on large datasets of known extremist content and threats to recognize similar patterns and behaviors in social media posts. See Cortex Xpanse https://www.paloaltonetworks.com/cortex.
Keyword Detection: AI algorithms scan social media posts for specific keywords or phrases associated with extremist ideologies, violent threats, or illegal activities, triggering alerts for further investigation. See darktrace.com.
Sentiment Analysis: Sentiment analysis algorithms assess the tone and context of social media posts to identify expressions of hate speech, radicalization, or intentions to cause harm.
Network Analysis: These products analyze social network connections and interactions to identify clusters of users sharing extremist content or engaging in radicalization efforts, helping to map out and disrupt online extremist networks.
Multilingual Support: Support for multiple languages enables the AI to monitor and analyze social media activity across diverse linguistic communities, broadening its scope of detection.
Real-time Monitoring: Real-time monitoring capabilities allow these products to continuously scan social media platforms for new content and emerging threats, enabling timely intervention and response. See ZeroFOX Platform https://www.zerofox.com/platform/).
Threat Prioritization: AI-powered algorithms prioritize detected threats based on factors such as severity, credibility, and potential impact, allowing law enforcement agencies or moderators to focus on the most urgent cases. See SafeGuard Cyber https://www.safeguardcyber.com/.
Cross-platform Analysis: These products can analyze social media activity across multiple platforms simultaneously, providing a comprehensive view of online extremism and radicalization efforts. See Dataminr for Corporate Security https://www.dataminr.com/solutions/corporate-security).
Integration with Reporting and Response Systems: Seamless integration with reporting tools and incident response systems facilitates the efficient handling of identified threats and the coordination of intervention efforts. See Splunk Enterprise Security www.splunk.com.
These features represent the capabilities of next-generation Social AI products in detecting and mitigating online extremism, hate speech, and threats on social media platforms, contributing to the promotion of a safer online environment.
Take a look at “48 top Crime Prevention companies and startups in 2024” for more ideas.
Conclusion
To repeat, the use of AI in crime prevention is a double-edged sword. AI has the potential to revolutionize crime prevention, from predictive policing and crime detection to cybersecurity and fraud prevention. Disruptive startups are leading the way in developing innovative AI-powered solutions that have the potential to make our communities safer and more secure.
While these possibilities have the potential to create a safer future, the proliferation of these tools raises ethical concerns. It is crucial to approach these developments with caution and foresight, considering the ethical implications and ensuring that AI is used responsibly to uphold the principles of justice and fairness.
To unlock the true potential of AI, we must prioritize transparency, address bias, and ensure that human judgment remains central to the decision-making process.
References:
"What Happens When Police Use AI to Predict and Prevent Crime?" https://www.gopopai.org/ai-algorithm-predicts-future-crimes-one-week-in-advance-with-90-accuracy/ Daily JStor, daily.jstor.org
"Artificial intelligence in criminal justice: invasion or revolution?" International Bar Association, ibanet.org
https://www.cisa.gov/topics/cyber-threats-and-advisories/nation-state-cyber-actors/china
https://therecord.media/uk-ico-ransomware-cyberattacks-data-2023
https://www.statista.com/chart/28878/expected-cost-of-cybercrime-until-2027/
https://semiengineering.com/using-ai-ml-to-combat-cyberattacks/