Artificial Intelligence (AI) and machine learning have been the major buzzwords in the past 2010 decade, and there’s no denying that the advancements of AI will definitely be at the center of our future and will affect various sectors including cybersecurity.
AI, in the world of cybersecurity, is somewhat a double-edged sword. While on the one hand, we can use various AI and machine learning technologies to help protect our devices, systems, and networks, on the other hand, cybercriminals can also use these AI technologies to launch more sophisticated attacks that are much harder to defend against.
With that being said, for Security Forward here we will further examine how AI technologies will affect the cybersecurity landscape in both positive and negative ways.
What Actually Is AI? Terminologies and Misconceptions
With AI being a very popular buzzword, unfortunately, the term is often misused at the moment.
There are, actually, various technological implementations at the moment that claim to be AI while actually, it is only a simple data analytics:
- Data analytics is static: it examines data sets (can be massive data sets like in AI implementations, hence the confusion) to find patterns and draw conclusions from the data sets. However, it is not self-learning nor iterative.
- AI, on the other hand, is both iterative and With machine learning implementations, AI will get smarter the more data it analyzes and can improve itself automatically.
So, AI refers to a program/software that can understand, learn, and act based on the acquired and analyzed data, and at the moment, there are three different types of AI implementations
- Assisted AI: improves what people, software, or organizations are already doing (assisting)
- Augmented AI: enabling people and organizations to do things they couldn’t execute before (augmenting)
- Autonomous AI: AIs that act on their own. Autonomous machines and self-driving vehicles are examples of autonomous AI.
So, what actually is machine learning? Machine learning is actually one of the subsets of AI technology, and at the moment there are three main subsets of AIs we should understand:
- Machine learning: the AI uses statistical techniques to ‘learn’, that is, progressively improving, performance-wise. The AI analyzes data to teach itself rather than being explicitly programmed by human users. At the moment, at least, machine learning is typically utilized in performing highly specific tasks rather than well-rounded operations.
- Deep learning: a subtype of machine learning based on learning data representations using artificial neural network architecture to perform much deeper learning than traditional machine learning. Can perform highly complex decision-making processes like driving autonomous vehicles and perform highly accurate medical diagnoses.
- Expert systems: programs designed to solve problems within a specific domain by mimicking the action patterns of human experts. Based on fuzzy rule-based reasoning to solve problems and make decisions.
AI Implementation In Cybersecurity
Now that we’ve understood that the main benefit of AI is its ability to ‘learn’ and improve itself, machine learning and AI in cybersecurity is typically used to automate threat detection and analyze the attackers’ behavior to always stay one step ahead of the cybercriminals.
AI implementations in cybersecurity are useful in tackling the following issues:
- Massive and still growing attack surface, while protecting digital assets becoming much more challenging.
- IoT implementations where organizations must protect thousands and even hundreds of thousands of devices
- Shortage of skilled cybersecurity professionals
- The growing amount of data that have become increasingly unmanageable
A self-learning AI can instead solve these issues by continuously gathering and analyzing data to find patterns that are relevant to the organization’s attack surface. AI can improve cybersecurity in the following arrears:
1. Improving Threat Detection With Machine Learning
Detection of attacks before the attack itself impacts the target is an extremely valuable asset in cybersecurity: the earlier the detection, the more time the organization will have to mitigate and neutralize the incoming threats. AI and machine learning implementations can significantly help in achieving this purpose.
As discussed, machine learning can analyze a massive amount of data and find relevant patterns. The cybersecurity measures can then adjust and refine algorithms based on this data to improve its performance, enabling the system to detect attract vectors and potential threats faster and more accurately than ever.
Traditional cybersecurity measures rely on previous results (i.e. an antivirus software relies on its database of previously known malware), so it’s simply impossible to detect and mitigate a brand new (zero-day) attack. That is not the case with AI-based solutions that can adapt and predict based on data.
2. Faster Response
Another key benefit of AI implementations in cybersecurity is its (almost) real-time identification and response. It can respond much faster than traditional security solutions, preventing potential attacks from affecting the system or network.
With a fully-automated AI solution, we also minimize the need of having human supervision before the solution can take action, which in turn will minimize human errors. It’s simply impossible for humans to monitor everything at once, which will, in turn, produce delays in decision making.
An AI solution can help tackle this issue.
3. Detection of AI-Powered Attacks
As discussed at the very beginning of this article, cybercriminals can also use AI-based technologies to launch their attacks, which can be much more difficult to detect and mitigate.
For example, hackers can create sophisticated bots with AI technologies so that they can accurately mimic human-like behaviors like non-linear mouse movements, randomized clicking patterns, and others. These bots are very difficult to detect with traditional means, and an AI-powered bot detection solution like DataDome is required to block those bots.
4. Phishing Detection and Prevention
Phishing is a type of cyber-attack where the attacker impersonates someone the victim may know or a legitimate organization to trick the victim into giving up their sensitive information. Or the victim might be tricked into clicking a link that contains malware.
In recent years, various AI and machine learning technologies have been implemented to mitigate phishing attacks, capable of automatic detection of over 10,000 phishing sources. These technologies allow more accurate detection of fake websites, preventing many phishing attacks from impacting various organizations.
Conclusion
AI and machine learning can significantly improve cybersecurity measures to perform better and faster detection and more accurate decision-making. Yet, while as we can see there are many benefits of Ai in cybersecurity, there is still a lot of room for improvement.
For example, while AI can perform predictive-based detection before the attack is executed, it can also potentially lead to false positives. Not to mention, cybercriminals can also use AI to modify their bots and launch more dangerous attacks.
However, while AI technologies in general still have a long way to go, we can’t deny that it’s starting to have a real impact on cybersecurity, and there’s no doubt that it’s going to continue to improve in the near future.
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