The impact of artificial intelligence (AI) on cybersecurity is quickly becoming a major topic as organizations across the world begin the race to adopt AI technology into their products, business models, or security programs. AI is quickly emerging as a field that has the potential to revolutionize the field of cybersecurity.
However, the use of AI in cybersecurity brings on new challenges and risks just as much as it provides new and innovative solutions. As such, it is important to understand both the opportunities and limitations of AI in cybersecurity so that we can use it in a responsible and ethical manner.
What is AI?
If intelligence can be described as the ability to acquire and use knowledge and skills, then artificial intelligence, therefore, can refer to computers, machines, or technology that can replicate human decision-making and problem-solving.
AI typically refers to the field of simulating human intelligence processes through machines, computers, and applications. In order to simulate these processes (and, in some cases, exceed), AI technology uses a combination of complex algorithms, statistical models, language processing, and computational power to not only input the data but also to learn from it and improve its performance over time.
The eventual goal of AI is to create intelligent machines or technology that can perform and automate human tasks at a much higher level, complete them at a much faster rate, and create solutions for complex tasks that may be beyond human capabilities.
Machine Learning vs. AI
The term machine learning is often used interchangeably with AI. However, machine learning is actually just one component of AI. It refers to using and developing computer systems that can adapt and learn. These computer systems draw from statistical models and algorithms.
The way machines learn is through three ways:
Examples of machine learning algorithms include friend-tagging suggestions on social media, speech-recognition software, language translation, and chatbots such as those used for online customer support.
Conversely, AI has a broader scope, referring to the general ability of computers and machines to emulate human intelligence and perform in real-world environments. Machine learning has a more narrow viewpoint and refers directly to the technologies and algorithms that allow systems to achieve processes like pattern recognition and self-adaptation through experience.
How AI Benefits Cybersecurity
When integrated with machine learning algorithms, AI security systems can have significant advantages for cybersecurity. Although the most obvious benefit is the ability to automate processes and save time, AI has far higher and more complex capabilities than most people know.
For example, detecting the many malware variants can be challenging, especially when enormous volumes of innocent code mask it. However, dealing with this cyber threat is easier with AI security solutions that include databases of existing malware and the ability to detect patterns to discover new malicious code.
With ever-larger volumes of data and an increasing number of ways individuals and businesses connect, including emails, chats, and videos, it makes sense that organizations could do with a helping hand to ensure stronger cybersecurity for their organizations.
Perhaps one of the biggest benefits of AI is the ability to use the entirety of its computational power to make cyber predictions based on historical and present data. More specifically, AI technology will be able to identify potential vulnerabilities and risks before they occur and automatically trigger proactive measures to mitigate and prevent cyber attacks, such as ransomware, phishing, or malware attacks from happening.
Predictive analysis is incredibly difficult and complex to implement because it requires analyzing massive datasets to identify consistent patterns and build predictive models using multiple variables, such as user behavior, network traffic, or threat intelligence feeds. Building these models manually can require thousands of hours, and even then, it may not be entirely accurate.
Additionally, one of the biggest threats that plague organizations are zero-day vulnerabilities. These vulnerabilities do not have patches available and can severely cripple an organization if left undetected. AI can utilize its predictive models to detect signs of potential zero-day vulnerabilities.
Faster, Real-Time Threat Detection
Through machine learning, AI cybersecurity systems can learn over time to help protect individuals and organizations from emerging threats. They benefit from machine learning and deep learning and the ability to recognize patterns, meaning these systems can spot deviations from the norm and respond accordingly in the IT ecosystem and even in IoT (internet-of-things) devices.
The most important benefit of real-time threat detection is that AI can filter out false positives once the models have been trained to recognize attack patterns and correlations. Many risk analysis solutions today pull too many false positives that can cost companies valuable time and money because a manual review is needed. With
With this level of watchfulness and the continuous assimilation of data, AI cybersecurity systems can use machine learning capabilities to improve security by detecting and responding to similar patterns much more quickly. Because AI systems can learn continuously, they can also give users an edge over cybercriminals. AI cybersecurity systems can learn more quickly than a human cybersecurity team alone to identify multiple threats simultaneously.
Automation of information security can save organizations money, as preventing data breaches is far preferable to manually containing, remediating, and attempting to repair the damage to systems and reputations.
Vulnerability and Risk Management
Organizations can integrate AI into their vulnerability and risk management programs to fully analyze their threat exposure and understand their security posture. AI can assist with vulnerability management by automating the detection, identification, and remediation of known vulnerabilities.
This means that the AI system can scan hundreds of thousands of connected devices, tens of millions of webpages, large databases, and complete attack surfaces to instantly make informed decisions based on collected data. The self-learning model allows AI to identify the best method for remediation and, if allowed, can perform the entire process from start to finish without requiring human oversight.
Once the AI has automatically scanned an IT ecosystem, it can assess and analyze existing security measures to ensure they are sufficient, help prioritize vulnerability remediation, and ensure the network is well-equipped to defend against potential vulnerability exploitation.
One of the main struggles organizations face is the constant cycle of vulnerability and risk remediation, which they often have trouble managing without dedicated software or services. With AI, organizations can scan systems for vulnerabilities against vulnerability databases and even monitor user and network activity for suspicious behaviors to identify potential zero-day vulnerabilities.
Network Security and Management
Not only can AI systems identify and respond to threats, but they can also use advanced logging and tracking that can help with the analysis and mitigation of new threats. Hackers constantly change their tactics, so using a system that can adapt to rapidly evolving cyber threats can significantly improve effectiveness and speed.
AI can also be more effective than human security experts in monitoring traffic. For enterprise-level organizations, internal and external traffic on the network can be extensive. It can be time-consuming and labor-consuming for cybersecurity personnel to monitor all this data manually, which can be automated and tracked at a much larger scale with AI security systems.
Moreover, since many security breaches involve human error, the future will likely have more organizations implementing AI systems to monitor networks for all internal and external threats. An AI network monitoring tool can learn the behavior of users and respond accordingly when it detects an anomaly.
With an ever-increasing number of connected devices, this is challenging for cybersecurity professionals that may already have massive workloads. AI can handle endpoint lifecycle management, tracking certificates, detecting credential misuse and theft, performing audits, and more to ensure the continued security of a network and its confidential information.
AI Cybersecurity Challenges and Risks
While there are many significant benefits to using AI to counter cyber attacks, organizations must also understand the drawbacks and challenges that come with AI technology.
Because AI in cybersecurity is relatively new, this means that the design and implementation costs may not be worth the investment for smaller companies, at least during this early period of AI use. For medium to enterprise businesses, such cybersecurity systems would need to be built and maintained, typically necessitating increased short-term expenditure and resource consumption. Additionally, the demand for AI professionals may exceed the supply in the short term.
Training AI models with data sets is another investment cost when implementing an AI cybersecurity system. With the massive volume of data and events, this can be a time-consuming process to gather data, review results, and test the model before it is fully functional. Training AI is essential since inaccurate or unreliable source data will lead to an unreliable cybersecurity system.
In theory, AI should become fully self-sufficient once it is operational; however, updates and maintenance will need to be performed to ensure the system is consistent with business goals and stays effective and accurate.
Cybercriminals Can Also Use AI
Unfortunately, just as organizations and cybersecurity professionals can train AI systems to spot emerging and evolving cyber threats to adapt their threat-hunting capabilities, cybercriminals can also use AI to devise and launch increasingly complex cyber attacks.
In the case of adversarial AI, cybercriminals could maliciously feed machine learning models with false or misleading data to devise newer ways to fight against increasing cybersecurity. The AI system can be trained using inputs from the hacker and learn how to get around AI-based cyber defenses. Advanced AI can also technically compromise facial recognition technology to further commit fraud or theft.
To ensure AI-based cybersecurity stays ahead of AI-based attacks, it will need to be regularly updated to learn new attack methods. This can add to the considerable investment required to implement AI effectively in cybersecurity.
AI Technology Is Not Perfect
Though AI technology has potentially limitless uses, because we are in the early stages of understanding its full capabilities, there may be a tendency for organizations in the early adoption stage to over-rely on its functionalities. AI systems must still be monitored closely with ideally an AI-trained specialist. The early stages of AI adoption can be finicky and slow, with plenty of errors and bias throughout while attempting to mold it into a self-sufficient state.
AI could potentially unlock a whole new field of study, but it will take some time to fully grasp its scope. Organizations will need to not only closely monitor and study AI implementation, but they will also need to ensure that AI is used for ethical purposes that do not invade user privacy or circumvent traditional security measures.
AI technology can provide a significant advantage in the fight against cybercrime. Through automation and machine learning, AI cybersecurity systems can offer advanced real-time threat detection for known and unknown threats, save countless hours in cyber defense, and develop innovative solutions beyond current human capabilities.
The disadvantage of high investment costs for early adopters is something that will improve over time, and the efficiency of AI cybersecurity saves money and lowers the risk of costly data breaches. These problems can be addressed by cybersecurity professionals who are not superseded by AI systems but work with them to optimize their effectiveness, improve accuracy, and provide early benefits.
As for malicious use of AI, it will soon become a race between organizations and cybercriminals to see who can adopt AI as quickly and effectively as possible. It is just one of the factors in the evolving cyber threat landscape that must be considered strongly before it is wrongly abused.