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AI and machine learning have become game changers in so many different areas of business. From logistics to finance to marketing, the possible uses of AI are endless. Recently, AI has earned a spotlight in cybersecurity as well. Keep reading to learn the main applications of AI and machine learning in cybersecurity and some of its advantages and disadvantages.
Contents:
What is AI?
Artificial intelligence (AI) is a computer program that aims to mimic human intelligence while performing various tasks. These tasks include problem-solving, learning, and pattern recognition. AI systems leverage algorithms and vast datasets to continuously improve their decision-making abilities. In cybersecurity, AI is used to automate threat detection, enhance security protocols, and respond to incidents more efficiently than traditional methods.
What is Machine Learning (ML)?
Essentially, machine learning (ML) is a subset of AI that focuses on enabling systems to learn from data and improve over time without being explicitly programmed. ML algorithms analyze vast volumes of data to identify patterns and help you make an informed decision based on that data. In cybersecurity, ML is leveraged to help systems detect new threats by learning from previous attacks and constantly adapting to new types of malicious activity.
How is AI leveraged in cybersecurity
AI is quickly taking cybersecurity to the next level by introducing more sophisticated methods of detection and prevention in terms of threats. Here are a few examples of how AI can be applied in cybersecurity.
Improved threat detection: While traditional systems normally use signature-based detection, AI systems leverage ML in the detection of known and unknown threats. By enacting sophisticated pattern analysis across large datasets, AI-based security solutions can identify anomalies in activity that may indicate an emerging threat, thus enabling businesses to take action sooner.
Anomaly detection: AI is expected to be great at recognizing deviation from standard behavior within a network. What the traditional systems can easily miss—often very subtle anomalies—AI detects with incredible effectiveness due to its ability to process and analyze large volumes of data in real-time.
Reduce false positives: One significant issue with traditional cybersecurity systems is the generation of false positives, which can overwhelm security teams. AI-based systems have a better understanding of activity context, reducing false positives, and ensuring that security teams can focus on actual threats.
Volume of data processing: Enterprises generate immense amounts of data, making it challenging for traditional systems to process and analyze this data efficiently. AI’s high processing power enables it to analyze large datasets quickly, identifying potential security threats in real-time and making it ideal for large-scale operations.
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Advantages of Intelligence in Cybersecurity
The ability of AI to work independently and improve over time has proven to be immensely beneficial to businesses of all industries. The advantages can be especially seen in the cybersecurity industry. Unfortunately, cyber-attacks have become increasingly advanced in recent years. Missing any crucial bit of cybersecurity-related information can be detrimental to a business.
Conventional cybersecurity measures based on regular non-AI algorithms aren’t always enough to deter cyber attacks. Here are the main benefits of AI in cybersecurity:
Detecting New Threats
Hackers are rapidly thinking of new ways to breach systems and cause damage. Plenty of great cybersecurity tools aim to identify these threats and eliminate them immediately. However, it’s nearly impossible to keep up with every new threat without help from AI.
Artificial intelligence doesn’t rely on a fixed database of known cybersecurity threats like traditional software often does. By analyzing past malware, scanning the web for news on emerging threats, and thoroughly analyzing all activity on a given system, it can identify even previously unknown threats and inform the user of the system immediately.
Better vulnerability management
The larger the organization and the more devices and users connected to a given database, the more difficult it is to determine the most pressing vulnerabilities. Artificial intelligence can continuously analyze all devices, authorizations, user habits, and software. It can then combine this information with its knowledge on the most urgent cybersecurity threats.
In doing so, AI can determine which systems, devices, or users are most susceptible to a cyber attack. Advanced AI solutions may even be able to indicate the potential monetary losses associated with a given vulnerability.
Improving endpoint security
An endpoint refers to any device that is connected to a given network. For example, businesses often have hundreds of devices connected to their network. Every one of these devices has the potential to be hacked. Many businesses have implemented various cybersecurity solutions, such as antivirus software and firewalls.
Most conventional cybersecurity software relies on signatures to detect threats. These signatures continue to be updated as new threats are detected and registered within the system. However, users failing to update software or developers failing to spot new threats means that many devices continue to rely on outdated cybersecurity software.
Endpoint protection based on AI and machine learning rather than signatures is significantly more secure. AI-based software can spot suspicious behavior based on patterns and subtle signs. As mentioned previously, it can discover even completely new threats and vulnerabilities by using its neural network. This ability makes it much more autonomous and less dependent on developers and the willingness of users to install updates.
Learning over time
Traditional cybersecurity software requires updates to function properly. In a way, such software becomes less advanced with time if the user fails to install updates. AI-based software is quite the opposite: with time, as it is exposed to more data, it becomes smarter. It does so through machine learning – AI can identify patterns by analyzing new data and use this information to continuously build its neural network, making it increasingly advanced.
Enhancing user experience
AI can be extremely helpful in improving user experience. For example, here at NordPass, we’ve integrated machine learning into our autofill option, making the new and improved autofill far more accurate and faster than any signature-based option.
Downsides of Artificial Intelligence in Cybersecurity
There’s no denying the advantages of AI in cybersecurity. However, here are some cybersecurity challenges that must also be considered:
It can be used by malicious actors as well
Hackers can also use AI. More specifically, they can manipulate it. When creating new malware and writing malicious code, hackers can analyze how AI reacts to it. They can then tweak the malware so that the AI will no longer be able to detect it properly. Instead, the AI will act in favor of the hacker and even aid in the attack.
It requires significant resources
AI is complicated, and it requires a tremendous amount of resources. Such resources include money, talent, and as much data on cybersecurity and malware as possible. (The more data that AI is based on, the more accurately it works). AI that lacks crucial data and is not implemented at an expert level will likely fail to detect threats. Ultimately, poorly executed AI will cause more harm than good.
Of course, not every cybersecurity business has the means to implement AI into its practices. There’s no saying that businesses that lack AI are inherently bad and unreliable. However, when choosing any AI-based cybersecurity solution for business, make sure that the company knows what it’s doing and that the tool will allow you to address the aforementioned cybersecurity challenges.
Will AI take over cybersecurity?
The idea of AI taking over cybersecurity might sound like a sci-fi story; however, the reality is far more complex and collaborative. Indeed, AI is revolutionizing the field, automating routine tasks, identifying threats at speeds and scales impossible for humans, and delivering predictive insights that help head off attacks before they happen. However AI still has limitations, such as the inability to fully comprehend context along the subtleties of human behavior.
Cybersecurity is not just about technology; it’s also about strategic thinking and understanding the broader implications of security policies—areas where human expertise is quintessential. The creativity, intuition, and deep knowledge of human experts regarding how business works are things that cannot be replaced by AI alone. They are essential in making judgment calls, adapting to new and unforeseen situations, and applying a nuanced understanding of the context that AI lacks.
In practice, the future of cybersecurity is not AI versus humans, but AI and humans working together. Let AI do the heavy lifting: processing volumes of data, recognizing patterns, and responding autonomously to certain kinds of threats. Meanwhile, human experts can attend to complex threat analysis, strategy development, and key decisions that machines simply cannot make. This partnership between AI and human intelligence is key to building a robust and resilient cybersecurity framework in the future. The future of cybersecurity will likely see AI continuing to evolve, with humans leveraging these tools to enhance their capabilities rather than replace them entirely.
Examples of AI in cybersecurity
Below are five key examples of how AI is being leveraged in this field, combining insights from both existing knowledge and recent industry use cases:
Threat detection and prevention: AI enhances threat detection by analyzing large datasets from various sources to identify patterns indicative of potential cyberattacks. Tools like AWS GuardDuty are particularly effective in monitoring and detecting unusual activities across cloud environments, safeguarding sensitive data from unauthorized access.
User behavior analytics: Through continuous monitoring of user activities, AI can build a baseline of normal behavior and identify deviations that could indicate a security breach. AI-powered behavioral analytics, such as those used by AWS Macie, help in identifying unauthorized access attempts by analyzing data usage patterns and alerting security teams to potential threats.
Advanced threat response and mitigation: An AI-driven platform can automatically isolate infected systems, block malicious traffic, and initiate incident response protocols, significantly reducing the time it takes to contain and neutralize threats. This automation is evident in the practices of companies like Wells Fargo, where AI plays a key role in real-time threat management.
Vulnerability assessment and management: AI tools are increasingly used to scan networks for vulnerabilities, prioritize them based on potential impact, and recommend remediation actions. Splunk, for example, employs AI to continuously assess and manage vulnerabilities, allowing security teams to focus on high-priority issues only.
Security operations and automation: AI significantly enhances the efficiency of security operations by automating routine tasks such as log analysis, threat detection, and system monitoring. AI-driven automation platforms, like those used by IBM, can process billions of data points daily, enabling faster and more accurate threat detection and response.
Bottom line
New cyber threats keep emerging every day, and companies are more vulnerable to hackers than ever. AI and machine learning may be the best solution to help keep companies in the loop at all times. With enhanced threat prediction, better endpoint security, and several other benefits, AI-based cybersecurity solutions are certainly the way to go.