environments is massive, and it’s continuing to grow rapidly. This means that analyzing and improving an organization’s cybersecurity posture needs more than mere human intervention.
AI and machine learning are now becoming essential to information security, as these technologies are capable of swiftly analyzing millions of data sets and tracking down a wide variety of cyber threats — from malware menaces to shady behavior that might result in a phishing attack.
These technologies continually learn and improve, drawing data from past experiences and present to pinpoint new varieties of attacks that can occur today or tomorrow.
In this post, we’ll review the use of AI in cybersecurity (both good and bad), along with what the experts and executives have to say about this matter.
Advantages of AI in Cybersecurity
AI presents many advantages and applications in a variety of areas, cybersecurity being one of them. With fast-evolving cyberattacks and rapid multiplication of devices happening today, AI and machine learning can help to keep abreast with cybercriminals, automate threat detection, and respond more effectively than conventional software-driven or manual techniques.
Here are a few advantages and applications of using AI in cybersecurity:
Detecting New Threats
AI can be used to spot cyber threats and possibly malicious activities. Traditional software systems simply cannot keep pace with the sheer number of new malware created every week, so this is an area AI can really help with.
By using sophisticated algorithms, AI systems are being trained to detect malware, run pattern recognition, and detect even the minutest behaviors of malware or ransomware attacks before it enters the system.
AI allows for superior predictive intelligence with natural language processing which curates data on its own by scraping through articles, news, and studies on cyber threats.
This can give intelligence of new anomalies, cyberattacks, and prevention strategies. After all, cybercriminals follow trends too so what’s popular with them changes constantly.
AI-based cybersecurity systems can provide the latest knowledge of global as well as industry-specific dangers to better formulate vital prioritization decisions based not merely on what could be used to attack your systems but based on what is most likely to be used to attack your systems.
Battling Bots
Bots make up a huge chunk of internet traffic today, and they can be dangerous. From account takeovers with stolen credentials to bogus account creation and data fraud, bots can be a real menace.
You can’t tackle automated threats with manual responses alone. AI and machine learning help build a thorough understanding of website traffic and distinguish between good bots (like search engine crawlers), bad bots, and humans.
AI enables us to analyze a vast amount of data and allows cybersecurity teams to adapt their strategy to a continually altering landscape.
“By looking at behavioral patterns, businesses will get answers to the questions ‘what does an average user journey look like’ and ‘what does a risky unusual journey look like’. From here, we can unpick the intent of their website traffic, getting and staying ahead of the bad bots,” explains Mark Greenwood, Chief Technical Architect & Head of Data Science at Netacea.
Breach Risk Prediction
AI systems help determine the IT asset inventory which is an accurate and detailed record of all devices, users, and applications with different levels of access to various systems.
Now, considering the asset inventory and threat exposure (as discussed above), AI-based systems can predict how and where you are most likely to be compromised so that you can plan and allocate resources towards areas of most vulnerabilities.
Prescriptive insights from AI-based analysis enables you to configure and improve controls and processes to reinforce your cyber resilience.
The number of devices used for working remotely is fast increasing, and AI has a crucial role to play in securing all those endpoints.
Sure, antivirus solutions and VPNs can help against remote malware and ransomware attacks, but they often work based on signatures. This means that in order to stay protected against the latest threats, it becomes necessary to keep up with signature definitions.
This can be a concern if virus definitions lag behind, either because of a failure to update the antivirus solution or a lack of awareness from the software vendor. So if a new type of malware attack occurs, signature protection may not be able to protect against it.
“AI-driven endpoint protection takes a different tack, by establishing a baseline of behavior for the endpoint through a repeated training process. If something out of the ordinary occurs, AI can flag it and take action — whether that’s sending a notification to a technician or even reverting to a safe state after a ransomware attack. This provides proactive protection against threats, rather than waiting for signature updates,” explains Tim Brown, VP of Security Architecture at SolarWinds.
What Cybersecurity Executives Think About AI
Capgemini Research Institute analyzed the role of AI in cybersecurity and their report titled Reinventing Cybersecurity with Artificial Intelligence strongly suggests strengthening cybersecurity defenses with AI is urgent for modern enterprises.
The survey’s respondents (850 executives from cybersecurity, IT information security, and IT operations across 10 countries) believe that AI-enabled response is necessary because cyberpunks are already leveraging AI tech to execute cyberattacks.
Some of the report’s key takeaways include:
Three out of four surveyed executives say that AI allows their organization to respond faster to breaches.
69% of organizations think AI is necessary to respond to cyberattacks.
Three in five firms say that using AI improves the accuracy and efficiency of cyber analysts.
As networks become larger and data becomes more complex, AI provides better solutions to an organization’s cybersecurity needs. Simply put, humans aren’t capable of handling the growing complexities on their own, and sooner or later, the use of AI becomes inevitable.
Downsides of AI in Cybersecurity
The advantages discussed above are just a small chunk of the potential of AI in improving cybersecurity.
However, as with anything, there are also some downsides to using AI in this field. In order to build and maintain an AI system, organizations would need substantially more resources and financial investments.
Furthermore, as AI systems are trained using data sets, you must acquire many distinct sets of malware codes, non-malicious codes, and anomalies. Acquiring all of these data sets is time-intensive and requires investments that most organizations cannot afford.
Without huge volumes of data and events, AI systems can render incorrect results and/or false positives. And getting inaccurate data from unreliable sources can even backfire.
Another major downside is that cybercriminals can also use AI to analyze their malware and launch more advanced attacks, which brings us to the next point…
Use of AI by Adversaries
AI can be used by cybersecurity professionals to reinforce cybersecurity best practices and minimize the attack surface rather than continually being on the lookout for malicious activity.
On the flipside, cybercriminals can take advantage of those same AI systems for malicious purposes. Adversarial AI “causes machine learning models to misinterpret inputs into the system and behave in a way that’s favorable to the attacker,” according to Accenture.
For example, an iPhone’s “FaceID” access feature uses neural networks to recognize faces, making it susceptible to adversarial AI attacks. Hackers could construct adversarial images to bypass the Face ID security features and easily continue their attack without drawing attention.
Conclusion
AI is fast emerging as a must-have technology for enhancing the performance of IT security teams. Humans can no longer scale to sufficiently secure an enterprise-level attack surface, and AI gives the much-needed analysis and threat identification that can be used by security professionals to minimize breach risk and enhance security posture.
Moreover, AI can help discover and prioritize risks, direct incident response, and identify malware attacks before they come into the picture.
So, even with the potential downsides, AI will serve to drive cybersecurity forward and help organizations create a more robust security posture.
About the Author:
Gaurav Belani is a senior SEO and content marketing analyst at Growfusely, a content marketing agency that specializes in data-driven SEO. He has more than seven years of experience in digital marketing and loves to read and write about education technology, AI, machine learning, data science, and other emerging technologies. In his spare time, he enjoys watching movies and listening to music. Connect with him on Twitter at @belanigaurav.
