Machine learning technology has become invaluable in many facets of the IT sector. A study by Markets and Markets shows that the market for machine learning technology is growing over 44% a year.
One of the biggest factors driving the demand for machine learning technology is a growing need for cybersecurity solutions. Cyberattacks are becoming more common each year. Fortunately, machine learning advances have made it easier to stop them in their tracks.
One of the biggest applications of machine learning in cybersecurity is with stopping digital attack surfaces. In order to appreciate the benefits of machine learning in this application, it is important to understand the nature of these cyberattacks and the best ways to prevent them.
How Can Machine Learning Technology Stop a Digital Attack Surface?
With organizations expanding their digital footprint to reach more customers on more devices across more countries, their exposure (attack surface) to both internal and external threat actors increases. To make matters worse, a number of cybercriminals are using AI technology to conduct more devastating cyberattacks than ever before.
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The good news is that cybersecurity professionals an use machine learning as well. There are a growing number of ways that they are able to fortify their defenses with machine learning. This includes using machine learning to stop digital attack surfaces.
But what are digital attack surfaces and what can machine learning really do to stop them?
Overview of Digital Attack Surfaces
It might seem like an increasing attack surface is simply a recipe for disaster where security breaches are inevitable. Luckily this is not the case. Many organizations join hands with attack surface mapping and monitoring specialists to quantify their risk and introduce remedial steps to protect against breaches.
The term digital attack surface refers to the sum of all the possible attack vectors your organization has exposed to threat actors, that could be utilized to launch a malicious attack against your organization. Simply put, what technologies can threat actors utilize to gain access to your organization?
At first glance, it might seem to be an easy assertion to simply list all networked nodes. As soon as a closer inspection is done though you will soon find many possible vectors that you did not previously consider as vulnerabilities.
The most common kind of attack surface vector is those nodes that we know of. This would include all the organization’s managed technologies. From the workstations and servers to the outward-facing websites and web services hosting public APIs.
The second kind of attack surface vector is all the managed technologies that have fallen outside of the organization’s direct reach of influence. Whether risks have been introduced without the knowledge of the IT team, like shadow IT, for example, or whether there are online resources that have been forgotten about.
And thirdly, if the areas mentioned above are not enough, organizations still need to deal with threat actors who can create resources of their own. From malware and social engineering to resources specifically created to masquerade as your organization to harvest credentials and other sensitive information.
How Can Machine Learning Stop Attack Vectors?
There are a lot of benefits of using machine learning technology to stop cyberattacks. Some of them are listed below:
- Machine learning helps cybersecurity professionals automate certain tasks that would otherwise be very repetitive. This frees their time to focus on more essential threat analysis tasks.
- Machine learning technology can be trained to recognize threats that would otherwise be difficult to detect. For example, it can perform risk scoring analyses on emails that might be used for phishing.
- Machine learning helps identify weak points in the cybersecurity infrastructure, such as outdated firewalls. It can ping the cybersecurity team to make appropriate modifications.
As a result, machine learning is invaluable in stopping attack vectors of all types.
Five common attack vectors that machine learning must be taught to fight
There are a number of different attack vectors that cybercriminals use. Machine learning technology must be trained to address them. The biggest are listed below.
User and cloud credentials
Account restrictions and password policies are among the most neglected security mechanisms and pose a great risk to organizations, globally. Users get into the habit of reusing their organizational credentials on their social media profiles, and unintentionally supplying their credentials during a data leak. The other dimension is where administrators do not apply the principle of least privilege. The combination of these vectors can result in devastating data breaches.
Third-party APIs and web applications
APIs are an attractive target for hackers because they allow attackers to get access to otherwise secure systems and exploit weaknesses. APIs are frequently vulnerable to similar vulnerabilities as web applications, such as failed access controls, injections, and security misconfigurations because of the automated nature of their users. Newer machine learning driven cybersecurity tools are trained to recognize these threats.
Email Security
Email security is too often overlooked. You might be more appreciative of the need to train your machine learning tools to stop phishing attacks if you realize that one out of every 99 emails is a phishing attempt.
Security policy frameworks and similar email authentication measures need to be in place to protect against email spoofing from threat actors. The second major risk introduced by email is malware. Servers that are not configured to scan eliminate high-risk attachments open the door for external threat actors to gain access through social engineering and malicious attachments.
Shadow IT
The use of computer systems, hardware, applications, and resources without express IT department authority is known as shadow IT. With the popularity of cloud-based apps and services in recent years, it has risen at an exponential rate. While shadow IT can potentially boost employee productivity and promote innovation, it can also pose major security concerns to your organization by leaking data and potentially violating regulatory compliance standards. You need to make sure that machine learning tools are trained to recognize the weak points in your shadow IT system.
Unmanaged tech assets
As cloud technologies advance, organizations may still have connections to legacy systems and vice versa. These could have also been approved connections from enterprise applications to decommissioned third-party suppliers. They could also be internal linkages to firm IP addresses or expired storage domains. These unmanaged assets are almost always running outdated software with known vulnerabilities that have never been fixed, making it easy for skilled threat actors to exploit.
Machine Learning is Crucial for Stopping Digital Surface Attacks
To take back control of your digital attack surface, holistic attack surface visibility must be acquired. Machine learning technology makes this task much easier. This will allow you to efficiently identify and manage the risks they pose. Cyber security visibility can be rapidly attained by partnering with an industry security specialist who can provide real-time monitoring tools to remediate risks before breaches occur.
