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The precogs had the ability to predict when crimes were about to be committed ahead of time, enabling law enforcement to act early. Twenty years later, in a climate of abundant data, almost limitless processing, and at a point in history where law enforcement is frequently discussed, some of these technologies are beginning to look more feasible than ever.
Movie Effects vs. Real World
The movie had a lot of special effects, including a computer with no keyboard and a virtual reality interface that was well ahead of its time. They had multi-touch screens, retina scanners, insect robots, gesture recognition, and jet packs. While we all wait for jet packs, some of the bigger futuristic themes are a current part of everyday life:
If you missed the part where we have already started stopping crime proactively, you cannot be blamed. However, I assure you it is happening, and it is a sign of what is now possible and where we are going.
Stopping Crime Proactively
With OpenAI’s ChatGPT creating both excitement and concern, we are amidst an Artificial Intelligence (AI) and Machine Learning (ML) revolution. The potent combination of data, processing power, mobile, and bandwidth technologies practically assured that we would arrive at this point. Financial institutions are one place where already AI and ML are stopping financial crimes in their tracks. As much as we are fascinated by the potential to prevent crimes in the physical world, this is happening in the cyber world. Cyber-crimes can impact thousands over the course of moments, and security professionals have been using data such as logs, external information, public data, behavioral information, and other indicators of normalcy and compromise to detect and predict malicious activities.
An AI Security Force
By using ML, professionals can replay thousands upon thousands of behavioral scenarios and AI can extract human-relatable value from rapid analysis. By leveraging these technologies, companies have managed to reduce cyber incidents drastically, and mitigate the threats before they happen. For example, credit card companies have been able to reduce and practically eliminate fraud by using real-time anti-fraud measures that leverage data such as shopping patterns, shopper location, transaction data, and average purchase price. Along with multi factor authentication, these technologies have had a real impact on credit card fraud, and thus the bottom line of financial institutions.
Similarly, AI and ML also work to drive predictions tied to weaknesses and indicators of malicious intent in network traffic, aided by the rapid, ubiquitous access to seemingly infinite seas of data. These technologies help to foster a better environment by efficiently and continuously finding the proverbial needle in a haystack.
AI is also becoming useful at uncovering previously unknown threats, also known as “zero-day” vulnerabilities before they become fully executed attacks. By using techniques such as user analytics and data behavior analytics, engineers—like precogs—can now actively perceive unusual events that are likely indicators that something unexpected is going on or about to happen.
2023 is the year of AI
By programmatically establishing a security baseline that represents all devices, servers, applications, lateral movement, and access across an entire environment, AI and ML are the precogs of the cyber community. It is another sign of fiction leaping off the pages and movie screens into real life.
For now, consumers only see AI and ML in action superficially, such as suggesting Netflix favorites or new items to add to your online order or asking the Chat GPT generic questions. The speed of innovation, however, has stepped behind the scenes through backdoor API links to these AI tools. These next-generation technologies and unicorns will become much more apparent in 2023 for all to evaluate the tremendous opportunities but also the societal concerns.
This article was originally published in Forbes, please follow me on LinkedIn.