There’s a never-ending battle going on between cyber defenders and attackers, and this plays out with security products too: As soon as a security vendor develops a way to mitigate the latest threat, attackers are busy finding a way around it or a new threat to take its place.
To try to gain an edge in their efforts to protect businesses and individuals from scammers, malware, and data theft, many cybersecurity companies have turned to artificial intelligence (AI) and machine learning (ML) as a potentially useful weapon in their arsenal.
There are some benefits to employing AI in a cybersecurity context. It can make defensive measures stronger and response times faster, but it’s not a perfect solution. AI is not a replacement for human intelligence—especially when it comes to identifying and mitigating threats—but in the right contexts, with the right team, it can be helpful.
How is AI Currently Used in Cybersecurity?
Whether it’s SIEM solutions attempting to enhance their predictive capabilities or threat intelligence software trying to automate the threat detection process, businesses the world over are looking to AI as a critical part of their cybersecurity futures.
AI in general is in vogue right now, but its use in cybersecurity is expected to explode in coming years. A Statista report expects the “AI in Cyber Security” market to grow from $10.5 billion in 2020 to $46.3 billion by 2026, in the process taking an ever-bigger slice of a cybersecurity products market that’s approaching $200 billion.
Here are some examples of what companies think AI and machine learning can do to give cybersecurity firms an edge over their cybercriminal competition:
- Evaluating Threats More Quickly: As digital transformation takes over the business world, security teams are tasked with processing and protecting unprecedented amounts of data, and the volume of alerts is overwhelming. Artificial intelligence makes it possible to sift through this data and identify potential threats more efficiently while avoiding alert fatigue. This is how IBM’s QRadar SIEM solution leverages machine learning in its threat detection duties.
- Automating Defense Measures: Security orchestration, automation, and response (SOAR) tools have gained popularity among cybersecurity strategies since they were first introduced in 2017. This is due in part to SOAR’s potential ability to reduce human intervention needed to act on security threats, freeing up human experts’ time to deal with issues that require more creative thinking than an AI can offer.
- Lower Chance of Human Error: One of the critical weak points of any cybersecurity system is the human element. No matter how well you plan, no matter how effective your technology, all it takes is one or two people making a mistake for your network to be left defenseless against ransomware and other cybersecurity threats. By automating as much of their solutions as is reasonable, cybersecurity companies hope to reduce the likelihood of human error leading to cyber catastrophe.
- Phishing Detection: Phishing is one of the most effective techniques hackers have to infiltrate your network. AI integrated with email security tools can analyze the context and content of emails to detect signs of suspicious behavior like email spoofing and block potentially malicious emails before they hit users’ inboxes. This can be useful both for simple phishing scams and more advanced techniques like spear phishing.
- Biometric Authentication: In lieu of or alongside traditional password protection, some companies have turned to biometric and facial recognition scanners powered by AI to potentially block hackers’ access to user accounts.
- Behavioral Analytics: Many companies have begun experimenting with UEBA and other behavioral analytics solutions to help better identify anomalous user behavior and stop potential threats before they can do too much damage. This form of analytics can come in a variety of forms, such as an AI tracking unusual user login behaviors, such as someone logging in from a public library instead of their office.
Want to Know More About AI’s Role in Cybersecurity? Check Out AI & ML Cybersecurity: The Latest Battleground for Attackers & Defenders
How Effective is AI in Cybersecurity?
A common refrain when talking about AI and automation is that it ultimately can’t replicate the creative and strategic thinking that human intelligence provides. Based on how AI has been implemented and developed thus far, this is accurate.
The tasks AI and machine learning have proven to be good at are tasks with simple, predictable patterns and tasks that require the processing of large data sets. This is how AI can potentially speed up incident response times, as humans wouldn’t be able to process network traffic as quickly as automation can.
On the flip side, in use cases where the AI has to deal with a number of unusual or unpredictable behaviors, it struggles. This is why behavioral analysis can be a mixed bag as a solution. A 2018 paper published by IEEE goes into more detail about it, explaining, “Machine learning has limitations dealing with privileged users, developers, and knowledgeable insiders. Those users represent a unique situation because their job functions often require irregular behaviours. This cause[s] difficulties for statistical analysis to create a baseline [for] the algorithms.”
Additionally, if an AI system is poorly implemented, it can be weaponized against a company in an attack. This could happen at the data level, where malicious actors manipulate the data sets that AI algorithms use to learn their behaviors. Vulnerabilities could also come from biases or gaps in the data. Hackers sometimes use a technique called neural fuzzing to determine where weaknesses lie in software that processes input data.
To prevent your AI from working against you, it’s important to create safeguards. You should regularly evaluate the configurations of your devices and applications and monitor other areas of your cybersecurity infrastructure that aren’t directly-related to artificial intelligence tools. This is not only beneficial for your AI, but also for your security posture overall.
AI’s increased prominence in cybersecurity also goes both ways. As more cybersecurity enterprises leverage AI to boost their security, hackers are able to do much the same, through methods like AI-generated phishing emails or constantly-changing malware signatures.
Thankfully, well functioning AI is difficult to build, even for companies with the resources and expertise to do so. As such, your average cyber criminal probably isn’t going to be using AI for their next social engineering scheme. However, state-backed hackers from countries like Russia might have access to sophisticated AI hacking capabilities.
Bottom Line: AI in Cybersecurity
AI’s efficacy in cybersecurity is the same as in any field it’s deployed in. When focused on the things AI has been proven to do effectively and consistently, it’s useful, but when focused anywhere else, it struggles, often mightily so.
Knowledge is key when implementing AI into a cybersecurity strategy, both knowledge in the form of the data you feed your AI to train it and knowledge in the form of understanding what AI is good at and how to best leverage that for your business.
Ultimately, AI, like firewalls or IDPS, is a tool, and no one tool is going to be the cure for all your cybersecurity woes. Although artificial intelligence can be a benefit to your organization’s cybersecurity strategy, you still need people working to support it. Otherwise, you’ll be putting your weight on an unstable foundation.
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