Newer AI algorithms are extremely good at analyzing data traffic, access, and transfer, as well as detecting outliers or anomalies in data trends. Below are some of the ways AI can prevent and mitigate the damage caused by cybercrime.
The world faces an unprecedented threat in modern cybercrime. Sophisticated, globally-dispersed actors who are increasingly hard to trace find themselves with a bonanza on their hands as more of our economic and personal lives continue to migrate online.
The pandemic was an especially fruitful time for cybercriminals, who exploited the fear, confusion, and the huge increases in online activity to hack and steal everywhere they could.
SEE: Staggering growth of cybercrime and how data science helps improve online security
When you factor in the increasing sophistication of Artificial intelligence (AI), cybercrime represents one of the gravest threats to human civilization. Luckily, those same technologies can be leveraged to, if not stop entirely, at least slow down the bad guys. Below are some of the ways AI can prevent and mitigate the damage caused by cybercrime.
Combatting Attacks
Newer AI algorithms are extremely good at analyzing data traffic, access, and transfer, as well as detecting outliers or anomalies in data trends. If something strange is discovered, the AI programs can delve deeper into the data to see if the system has one or more security flaws. Creators of AI are also able to make better use of AI model management to stay on top of evolving threats and produce more up-to-date responses.
A process known as supervised learning is another way AI is employed in the prevention of cyber assaults. The algorithm is given a set of inputs and outputs, and it “learns” to detect risks over time by making decisions based on the data it sees or expects to see. For instance, supervised learning might be used to detect complex malware masquerading as a benign piece of code.
Over time and many (hundreds or thousands) of different instances, the AI that we employ to detect and root out malware becomes increasingly advanced and is better able to recognize and preempt potentially devastating attacks. The AI will have to evolve in tandem with the AI that is currently being used to create and stage cyberattacks, however.
Streamlining Cybersecurity Operations
AI is being used all around the world to streamline processes and relieve pressure on company cybersecurity teams, in addition to being used to prevent cybercrime. The increasing number and complexity of cyber-attacks have IT security personnel exhausted. Because machine learning is a highly scalable technology, it is frequently used to assist IT security personnel’s efforts to monitor, detect, and eliminate risks.
Artificial intelligence, when supplemented with human efforts, can be used to fill in the gaps in a company’s cybersecurity workload. This is important as cybercrime becomes more advanced and requires more sophisticated and even a greater number of IT and cybersecurity professionals to deal with. It is made all the more pressing by the current lack of cybersecurity talent out there, which puts already strained teams at even more of a disadvantage against sophisticated criminals.
Simulation
AI is also being used to help simulate attacks on networks so that cybersecurity teams have a better idea of where their major vulnerabilities are and how to respond when these attacks happen. AI has become adept at depicting how threat actors might travel laterally through a network seeking weak points.
Enterprise network defenders and researchers do this by creating various nodes on the network and identifying which services are running, which vulnerabilities are present, and what type of security measures are in place when creating the attack simulation.
In the attack scenario, automated agents simulating threat actors are deployed to do random behaviors while attempting to take over the nodes. These simulations are particularly important for critical sector protection like infrastructure and transportation.
By exploiting these planted vulnerabilities, the simulated attacker hopes to gain control of a segment of the network. While the simulated attacker moves over the network, a defender agent watches network traffic to detect the attacker’s presence and contain the attack. Simulation and gamified learning and testing are some of the best weapons we have against increasingly sophisticated and ingenious cyber criminals out there, who are harnessing and modifying AI for their purposes.
Conclusion
The next several decades will be defined in large part, at least in technological terms, by advances in artificial intelligence. Like any new tech, it will be harnessed by malicious actors looking to make stealing, extorting, and causing damage easier, harder to trace, and more difficult to combat.
AI will also, however, evolve in tandem and help cybersecurity professionals, and even the off-the-shelf programs normal people use in their everyday cybersecurity stand a better chance of thwarting the criminals.
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