One need not understand the finer details of the Dark Web in order to respect the risk these black markets pose in an increasingly data-centric world. In nearly every aspect of our modern lives, cybersecurity is becoming a necessary part of the conversation. 

Even novice “hackers” can rent cloud-based botnets and orchestrate DDoS attacks for around $25 per hour according to data released by Kaspersky Labs. Fortunately, the classic adage remains true, even on the Dark Web - you get what you pay for.

Low-priced DDoS attacks are easily thwarted by modern network security systems designed to recognize incoming threats and divert resources to squash attacks. 

Thanks to advancements in Artificial Intelligence (AI) and Machine Learning (ML) in the area of cybersecurity, small businesses and enterprise-level companies can stay focused and maintain a high level of digital trust from their customers while keeping overhead costs in check.

Machine Learning and Cybersecurity

So-called “script kiddies” won’t pose much of a threat for robust systems, but complex attacks are also being mitigated by systems driven largely by AI and machine learning. As a result, cybersecurity teams can be more proactive, and use the organization’s resources more strategically. 

In the context of cybersecurity, machine learning systems quickly and efficiently recognize patterns, and through algorithms manipulate these patterns to take the best course of action. Meanwhile, the end user is unaffected and, in most cases, completely unaware of the attack.

Saying that AI is making cybersecurity easier wouldn’t be entirely accurate but it’s hard to argue against higher rates of productivity, better protection and lower costs.

The Challenges AI Present for Everyone 

As mentioned above, machine learning and AI both rely on accurate, non-corrupted data in order to function. This can require a staggering amount of computing power, and of course data sets with current malware markers, anomalies and non-malicious code.  

Hackers also attempt to leverage AI and machine learning in order to test and improve their own malware. 

Remesh Ramachandran, CISO and security researcher summarized that AI can be a bane to cybersecurity by augmenting today’s threats, by using existing AI data to develop new threats and/or developing a variation of the nature of current threats.

He goes on in a recent Entrepreneur article to explain:

“Another significant issue is that, just like we can deploy AI and machine learning to complement the shortage in human resources and to save cost in cybersecurity, our adversaries can also use it for the same. The finances and resources needed for launching and coordinating such attacks will go down massively – more threat to cybersecurity at relatively lower investment for the cyber attacker.”

Still, despite the potential downsides, understanding the limitations may be the key to avoiding pitfalls. A holistic strategy for cybersecurity within a small business or an enterprise company should contain a combination of traditional techniques working in concert with tools powered by AI and machine learning.

Modern AI and ML in Action

In tightly regulated industries like finance and healthcare, AI and ML has been part of the security conversation for a while. Leveraging machine learning tools to help make better decisions ensure that businesses continue to run smoothly, customer information stays secured and the entity can remain profitable.

Tasks that formerly required hundreds of hours of research or chasing down a hunch are now executed much faster, and more accurately. Let’s explore some examples.

Healthcare

Organizations dealing in the collection, storage and analysis of medical data, face harsh penalties if they suffer a data breach. In the United States, HIPAA compliant data security isn’t a “nice to have,” it’s the only way to stay in business. The positive effects of keeping this data secure are being felt throughout the medical ecosystem.

Thanks to ML certain medical technology can now differentiate between tumors and healthy tissue through 3D radiological images. Machine learning is also being used to predict illness which helps physicians provide more proactive care and payers prepare a cost-effective treatment schedule. 

Banking & Finance

In an industry built on risk assessment, AI and ML are helping reduce the ambiguity of decision making and speed up traditionally slow processes like credit risk testing. Financial institutions can more quickly determine investment opportunities based on credit history and minimize the chances of default or fraud.

Similarly, portfolio managers leverage AI to make better financial decisions for their clients. Market data can be analyzed more a more granular level with exponentially more data points going into the decision matrix at higher speeds.  

Cybersecurity 

The modern scarlet letter is a company with a weak cybersecurity strategy. Ultimately, these organizations will become the victim of a data breach. It’s a question of when, not if.

A shockingly high amount of data breaches is the result of poor password hygiene. AI and ML tools are helping to curb data breaches through biometric logins and other multi-factor authentication tools to determine network privileges for users. 

Because of the continuous scanning for anomalies or known threat patterns, AI also reduces the amount of time and the cost it takes to spot threats and respond accordingly. Dwell time (the time that bad actors remain undetected within a system) on average is cut by 11 percent by leveraging machine learning tools in cybersecurity.

The Future of AI, ML and Us

So far, we’ve identified the benefits of AI and ML in the context of security for businesses and the risks associated with this type of tech. 

Advancements in these areas present more protection and faster threat mitigation but also potentially more risk as additional modalities of cybercrime come into focus. Still, the truth is that AI and ML allow cybersecurity professionals to do much more with less resources.

For this reason, AI and ML are largely a boon for business technology across the entire spectrum. 

Most security teams will focus on five high-impact areas – fraud detection, malware detection, intrusion detection, scoring risk within a network and user/machine behavior analysis. Before AI and ML these modalities each required much more rigorous and time-consuming analysis.

Conclusion

The full extent of how AI and machine learning will change the cybersecurity landscape cannot fully be known at this point. But professionals within the space should have optimism about the future of AI and machine learning-assisted threat mitigation (and job security). 

Responsibilities of security teams working in information technology (IT), operational technology (OT) and the Internet of Things (IoT) no longer need to operate in silos. Some believe we may see the extinction of the traditional “password” within a generation or two.

Neglecting to embrace AI and machine learning within a cybersecurity strategy will come at a higher cost for businesses with weaker digital immune systems. Customers, too, are becoming more judicious about aligning themselves with companies that collect and store sensitive information. 

Protect that digital trust.