What’s the role of artificial intelligence in cybersecurity? These days, we can observe a powerful breakthrough in AI-enhanced technologies affecting each and every field of human activity: ML methods revolutionize art, cinema, e-commerce, marketing, trading, entertainment, and many other areas of business. The same technological trend works for the implementation of AI in cybersecurity solutions and overall IT architecture.
Cybercrime has become one of the biggest threats to businesses and organizations worldwide: for example, more than 5000 data breaches and privacy violations occurred between 2009 and 2022 in the American healthcare industry, which led to the disclosure of more than 300 million medical records. In 2022, the total number of cybercrime victims achieved 422.1 million, which increased by almost 41.5 percent (!) from 2021. Moreover, the classification of cyber threats detected in 2022 comes with a surprising note: “Not specified” was the biggest category of cyberattacks, ahead of phishing and ransomware causes. Russia’s war against Ukraine has also caused an increase in cybersecurity risks: numerous institutions and governing bodies across the E.U. and the United States have reported cyber-attacks performed by Russian hackers and botnets.
As we can see, cybercriminals are constantly developing new tactics to exploit vulnerabilities in networks and systems, making it challenging for traditional security measures to keep up. To combat this ever-evolving danger, businesses, and organizations are turning to artificial intelligence (AI) to strengthen their cybersecurity barriers. AI-based techniques offer a range of advantages: real-time threat detection, automatic response, and scalability.
In this article, we will explore the importance of artificial intelligence in cybersecurity, the major AI data-protection methods, the currently available AI cybersecurity solutions, and the future prospects of AI in transforming the IT business landscape.
Applications of Artificial Intelligence, Machine Learning, and Cybersecurity: How Does This Orchestra Cooperate?
AI in cybersecurity is a combination of machine learning methods and other advanced algorithms or intelligent software products: they proactively monitor the network environments and act against cyber threats in a way that imitates the decision-making logic and routines executed by system administrators and cybersecurity specialists.
Meet the “four horsemen” of AI-managed cybersecurity:
- Threat monitoring
- Behavioral analysis
- Vulnerability management
- Fraud detection.
What is a common feature of all of these models? They all combine and leverage the best capabilities of machine learning and cybersecurity. At the same time, the main requirement for computer protection techniques in middle businesses and large-sized companies is the necessity to process extremely large loads of information (for example, a local ISP or an office-based enterprise with dozens or hundreds of workstations.) This sounds like a job match for AI: When it comes to analyzing tons of data, cognitive computing performs definitely better than humans! Now let’s look closer at each of these AI security mechanisms:
Autonomous Threat Monitoring & Detection for Complex IT Environments
AI-based solutions can continuously monitor the computer system environment without human intervention: control traffic graphs and details, keeping track of device parameters, user activities, and event logs to detect threats in real-time or ahead of their actualization. This omnipresent approach enables the electronic brain to apply adequate measures automatically and prevent malicious agents from causing damage far before they can break through the last barrier.
AI systems can stay on duty and act 24/7, so human IT administrators do not need to work overtime or take night shifts. Thanks to correctly configured AI routines, an intelligent machine can lend cybersecurity experts a shoulder and work independently during nighttime and/or weekends, keeping IT systems secure while employees enjoy their rest. Moreover, automated IT system diagnostics, updating, troubleshooting and other IT maintenance & optimization procedures can perfectly be run by AI, with human intervention limited to supervision and verification practices.
Behavioral Analysis: Tracking Suspicious Activities & Agents Online
Cognitive computing solutions are capable of analyzing patterns and trends in vast data amounts to identify suspicious user behaviors, detect undesired patterns among many thousands of active connections/sessions, and either immediately block suspicious activities or generate corresponding alerts for IT administrators to let them take further steps.
Artificial intelligence can use machine learning methods to adapt itself to different user behavior patterns, identify anomalies or malicious scenarios, and flag or manage them as potential threats. Implementation of AI/ML technologies in the cyber threat behavioral analysis allows smart IT engineers & technicians to rely on statistical analysis more than on their experience or security protocols alone. This empowers them to resolve more issues in less time and spot plus eliminate all potential threats or cybercrime risks early.
Thanks to smart machines, it’s possible to neutralize hacking attempts and shut down connections and sessions executed by malicious bots or suspicious users rapidly—this approach is extremely productive in the fields of web server-management technologies, social networks, copyright protection, and applications allowing simultaneous connections of numerous users, like online gaming and entertainment.
Vulnerability Management for Better Sustainability in the Entire IT Sector
Traditionally, predictive vulnerability management in organizations involved hours and hours of rigorous manual data analytics and data science skills, including log reviews, tech record interpretation, identifying drawbacks, and prioritizing them for remediation. With the help of modern AI and ML techniques, cybersecurity engineers don’t have to spend all day long continuously checking, investigating, and correlating data from different sources.
AI-supported vulnerability scanning & management solutions can potentially be set to scan and examine every important data source automatically or semi-automatically:
- Network traffic records
- IT system logs
- Threat intelligence feeds
- Software code sources
- User behaviors
- Bug reports
- Data breach reports, and so on.
The involvement of smart machines can not only help IT professionals manage tedious security research efforts to save a great deal of time for engineers but also discover, predict, and prioritize loopholes and web system vulnerabilities in real-time, allowing organizations to address them far before they are found and exploited by intruders or hackers.
Fraud Detection: Fin-tech, Insurance, E-commerce, Banking, and Governance
Cybersecurity artificial intelligence systems can be deployed in economic and banking sector companies to help financial investigators discover fraudulent activities, such as:
- Credit card fraud, like skimming and more
- Identity theft
- Phishing campaigns
- Insurance fraud
- Money laundering
- Real-estate fraud
- Phone bank fraud
- Financial manipulations
- Dark web dealing.
For this, intelligent fin-tech solutions can be set to monitor and analyze logbooks of financial records, and operational stats in bulk, including financial transactions, payments, and statements created and registered over prolonged periods of time.
To come up with more precise conclusions, financial findings can be correlated with client behaviors, including login sessions, device fingerprints, networking or server events, and much more as identified by local cybersecurity protocols and common cybersecurity hypotheses. Smart machines can efficiently connect the dots across vast amounts of structured data, enabling rapid detection of fraud patterns or other cybercrime attempts. This enables swift responses from law enforcement and regulatory bodies to cope with previous and future misconduct cases and save millions in return.
Examples of AI-based Security Solutions Currently in Use
How do companies use artificial intelligence in cybersecurity? Since AI technologies became cheaper to use and implement, more companies including medium or even smaller enterprises can benefit from creating custom AI-driven cybersecurity solutions.
More and more common businesses, including e-commerce platforms, are implementing artificial intelligence to enhance their processes and reduce fraud or hackery. For example, online shopping services can benefit from AI applications that help them detect and flag atypically large orders or other suspicious shopping occurrences that contradict usual shopper patterns and habits. This approach allows for better detection of some types of account takeovers, credit card fraud, chargeback fraud, and also merchant fraud, which can cause multi-million losses to online businesses.
How to Implement AI-powered Cybersecurity Systems?
Consider the fact that third-party AI cybersecurity solutions can be seamlessly integrated with your company’s software and network-management systems by your IT service provider. If you require skilled specialists capable of integrating AI cybersecurity solutions with your current IT environment, consider Forbytes IT engineering team.
Otherwise, check the options available: The most popular intelligent-computing solutions in cybersecurity include Darktrace, Cylance, and IBM Watson. All of these powerful platforms are designed to help organizations better protect their sensitive information against cyber threats:
Darktrace (based in Cambridge, the U.K.) is a cybersecurity solution that utilizes machine learning algorithms to find and handle cyber threats without delay. It’s like having a watchful eye that observes an organization’s network, devices, and users to learn their regular activities. This system can establish a baseline or “normal” behavior specific to a certain organization’s IT ecosystem. Any deviations from this baseline are then flagged as anomalies, which could indicate a potential cyber attack. When a problem is detected, Darktrace’s machine-learning algorithm kicks into action and can automatically handle the threat.
Cylance (acquired by BlackBerry in 2019) is an AI-based malware-focused cybersecurity solution that leverages ML to detect and prevent malware attacks and other threats. Unlike traditional antivirus software that relies on signature-based detection, Cylance proactively distinguishes and blocks threats before they can cause any harm. To achieve this, the system analyzes the characteristics of known malware and uses this information to identify new, previously unknown threats. This approach, known as “predictive analysis,” enables Cylance to provide a more comprehensive and effective defense against cyber attacks.
IBM Watson for Cybersecurity is a cognitive computing platform maintained by the famous IBM company. Their aim is to revolutionize the way organizations detect and predict cyber threats, and respond to them. The Watson-managed AI system uses a combination of natural language processing, machine learning, and other AI technologies to analyze security data from a wide range of sources. With the help of advanced analytics applied to this data, IBM Watson can identify potential threats that might otherwise go undetected. Moreover, it can provide educated recommendations on responding to different threats and automating certain security and data protection tasks, such as quarantining infected machines/files or blocking malicious traffic and connections. This helps reduce the workload on security engineers, enabling them to focus on more complex tasks and strategic objectives.
The 4 Major Advantages of Using AI in Cybersecurity
The use of AI provides numerous advantages, including real-time threat detection, automatic response, scalability, cost-effectiveness, dark web monitoring, and insider threat monitoring. Remember, that if you want to employ any of these advantages, you can rely on our skilled project management resources that know how to build AI and ML solutions for cybersecurity in business.
#1. Detect Cyber Threats in Real-time
One of the most significant benefits of AI-managed cybersecurity applications is their ability to detect threats instantly. With the ever-evolving frequency and technical sophistication of cyber attacks, it is essential to have a system that can quickly keep up with trends, consider all previous experience, and detect plus respond to threats before they even arise and cause significant damage. AI has the necessary capacities to identify threats quickly, such as malware, phishing attacks, or unauthorized access, and take action to prevent them from causing any damage to your company.
#2. Automatic Response to Cyber Risks
Another advantage of AI in cybersecurity is its ability to respond automatically to threats, both known and yet unknown (predicted or hypothetical). By using AI to automate security processes, organizations can respond quickly and efficiently to various cyber threats coming from different directions. Thanks to flexibility and scalability, AI-enhanced cybersecurity solutions can be an ideal choice for organizations with large networks, numerous computers, and diversified data sets. In this context, it’s important to mention that AI allows companies to:
- Minimize the impact of cyber-attacks and reaction time
- Rebalance the workload on their security teams
- Efficiently mine large amounts of security data and correlate information to generate reasonable cyber threat responses
- Align the response with multiple factors: data coming from technical logs, globally collected insights, network traffic patterns, and other important observations.
#3. Cost-efficient AI-based Cybersecurity Approach
AI can reduce the cost of cybersecurity by automating many of the tasks traditionally performed by human analysts and IT security consultants or engineers. This means that organizations can save the working hours of computer experts by using AI solutions, and benefit from the effect that is equal to hiring large teams of cybersecurity experts. Of course, the implementation of artificial intelligence is not a complete replacement for a skilled cybersecurity administrator, however, it allows humans for achieving far beyond their normal capabilities. A sophisticated AI/ML tool can provide even a single IT administrator with potential productivity and capacity levels similar to the same of an entire cyber-security department, or even exceeding them.
#4. Dark Web Monitoring Strategy
The Dark Web is a hidden (underground) network of websites and services that are not accessible through traditional search engines or web browsers and is often used by cybercriminals to buy and sell stolen data, such as credit card information, login credentials, and other sensitive information. AI-supported monitoring of the Dark Web can be one of the critical components of cybersecurity that involves checking the dark web locations for mentions of an organization’s name, employees, or data.
- AI platforms can deeply delve into Dark Web and apply natural language processing to analyze content in regard to specific organizations or their employees.
- If suspicious or sensitive content is detected, AI-powered software can immediately alert security teams to take appropriate action.
By monitoring the dark web, organizations can detect potential threats and data breaches before they become publicly disclosed or exploited by malicious agents. This allows cybersecurity administrators to take swift action to mitigate the impact of these threats and breaches, such as resetting passwords or notifying law enforcement bodies and/or affected customers.
The 5 Limitations and Potential Risks of AI in Cybersecurity
Everything comes with the reverse side, and AI cybersecurity is not an exclusion. There exist serious limitations and potential risks of AI applications in cybersecurity, including technical and legal concerns. Let’s check them to support your decision-making on whether to use AI/ML cybersecurity platforms in your company:
Bias in AI Security Systems
Bias is not only a human issue: Artificial intelligence systems are only as unbiased as the data they are trained on. If the data is somehow biased, then the AI system will generate biased results as well. This can lead to discriminatory outcomes in cybersecurity decision-making. Basically, it’s safe to say that popular AI platforms such as IBM Watson and Cylance invest in continuous and reasoned ML training to minimize the bias factor in their systems.
Even the best AI systems out there are still prone to so-called AI hallucinations. That’s why they can misinterpret certain information and make decisions based on false or incomplete data they were trained on. This can lead to incorrect threat assessments and potentially allow threats to go undetected, or otherwise increase the number of false positives, like blocking legit operations and kicking off authorized users.
While chasing after the lower costs of IT services, businesses and organizations may become too reliant on autonomous AI systems for cybersecurity, leading to complacency and a lack of human support or qualified oversight. These “robotic mode” conditions can cause the potential accumulation and impactful buildup of AI errors in cybersecurity systems. This creates new vulnerabilities, making the organization more susceptible to exotic cyber-attacks exploiting the loopholes found in AI-managed barriers (yep, cognitive computing still requires people nearby to provide efficient outcomes.)
Cybersecurity Skills Gap
There is a considerable shortage of skilled IT specialists, project managers, and other cybersecurity professionals who can effectively deploy and manage AI systems. This can lead to poor implementation, misconfiguration, and inadequate approach to protection against cyber threats. This is a problem that Forbytes can successfully help you with, just contact our AI engineers for a free cybersecurity consultation!
Privacy and Legal Complications
The utilization of AI systems in cybersecurity can raise essential privacy concerns as it may include the collection, processing, and analysis of personally identifiable data in large volumes. Applications of artificial intelligence in cybersecurity must undergo legal examination before being deployed, as they may not fully comply with privacy regulations. The use of cognitive computing may violate data protection laws or not be fully regulated by many local jurisdictions: for example, some countries have banned the use of ChatGPT, which can also make the use of the rest of AI solutions problematic in those locations.
In Conclusion: The Future Prospects of AI in Cybersecurity
One set of versions assumes that the future of AI in cybersecurity is bright and promising, while some radical theories warn us about the threats imposed by AI, including IT specialists losing their jobs in thousands and the total reliance on cognitive computing with its outstanding potential in messing things up big time. Forbytes engineers believe that we can avoid apocalyptic occurrences if we follow precautions like the correct development, implementation, and configuration of AI cybersecurity systems. We expect that the advantages and disadvantages of AI and ML data-protection systems will achieve a fruitful balance in the upcoming years, thanks to open-source AI systems, smarter legislation, and the efforts of engineers. If you want to join the future of artificial intelligence in cybersecurity, contact us for help!