In our last article, we demonstrated that automation is a great way to tighten up cyber hygiene. Automation is, of course, a growing and multi-layered field so considering the full range of automation tools, how they relate, and why adding intelligent automation to your defense arsenal will help you to achieve sustained security across your technology environment.
That’s why, in this article, we take a deep dive into the different facets of automation by kicking off with examining why automation matters so much for sustainable cybersecurity.
We then outline the different types of automation including Robot Process Automation (RPA) – alongside Artificial Intelligence (AI) and Machine Learning (ML). Finally, we cover intelligent automation by breaking down typical examples of each automation layer – in action.
Sound interesting? Read on!
Automation and cybersecurity sustainability
Cybersecurity efforts can feel like an endless resource-draining battle, but it is a battle that can’t be avoided. Even if your company is spared the brunt of it, one look at last year’s Accenture Cost of Cybercrime Study, and the cause for concern becomes clear.
Combine a growing threat landscape with workforce shortages and the expense of recruiting those skills and, well, it’s hard to see how companies will sustain effective cybersecurity in the long run. At aiden, we are convinced that intelligent automation can help fill the gap and thereby create a more sustainable cybersecurity environment.
Everyone uses automation to a degree. A network firewall is an automated device, after all. But there are different levels of automation sophistication.
Significant use of automation includes, for example, a firewall with AI capabilities that detect brand new threats undetected by published firmware. Or, utilizing automated endpoint management to eliminate the need to manually patch and secure countless corporate endpoints.
Effectively using automation to sustain cybersecurity requires using automation in every shape and form. That requires a good grip on the different layers of automation as it stands today.
What do we mean by intelligent automation?
Automation has come a long way and it’s worth understanding the commonly talked about (and applied) automation layers such as RPA and AI. These automation layers build up comprehensive, end to end automation – what’s commonly referred to as intelligent automation.
Robotic process automation (RPA)
RPA emerged from business process automation. RPA is essentially a “robot” that is built on structured inputs and business logic, with the capacity to mimic human-computer interactions. RPA can handle tasks just like a human, but RPA is faster and cheaper.
In practice, you could deploy an RPA app to click buttons and fill form fields, but RPA can deliver much more. RPA applications link across enterprise and web services, integrating automation capabilities into a larger framework.
Note that RPA is robotic in nature, RPA apps do not learn and simply repeat programmed procedures. One positive of RPA is that it is easy to layer on top of existing applications. If it’s repetitive, and a human can do it, then chances are RPA can do it too.
Why does RPA aid a sustainable, secure tech environment? It’s simple: with RPA, security teams are under less strain because RPA saves valuable time. RPA is also less error-prone and more consistent.
Artificial intelligence (AI) and machine learning (ML)
There are varying definitions of artificial intelligence. But, simply put, AI refers to computing capabilities that mimic intelligent human behavior. Think about learning, reasoning, processing language, and so forth. AI has several branches. The branch of AI that is most critical to cybersecurity is machine learning.
ML enables computer applications to understand, interpret, and execute data to solve a real-world problem. ML-enabled applications learn and build capacity over time and that is why machine learning is key to solving new and emerging cybersecurity threats.
IT teams know that emerging threats are the most difficult to guard against. That is why ML makes cybersecurity regimes more sustainable: ML can detect and mitigate even unknown threats.
Intelligent automation (IA)
Just like AI, intelligent automation has a rather fuzzy definition. See IA as the marriage of RPA and AI. In other words, an application that can automatically act but that does so in an intelligent way – learning over time.
Combining RPA and AI, intelligent automation creates an end-to-end automation journey that not only automates, but also discovers, optimizes, and orchestrates. Yes, RPA mimics human actions and AI mimics human intelligence. But IA replicates human behavior.
In other words, IA applied to cybersecurity can replicate some of what human intuition brings to security best practice. While IA will never replace human security experts, IA does have the ability to make stretched, under-resourced cybersecurity efforts much more sustainable by reducing staff pressure.
Thanks to IA, your team has more time to deal with maintenance and security issues and to think strategically about technology and security concerns. Teams retain full control, but automation removes much of the legwork.
Examples of automation in action
Automation progressively increases in sophistication as you move along the automation curve. But more primitive forms of automation (think RPA) also play a critical role. Each facet of automation is key to delivering sustainable cybersecurity. The following examples illustrate that.
Automating routine and tedious tasks
Cybersecurity requires consistent and repeated workflows that check, verify, and mitigate. Workflows that are left incomplete or unattended undermine cybersecurity in the long run.
RPA automates workflows. Bots automate tedious tasks to make sure these are done – and frees up your cybersecurity staff to focus on high-value work. For example, with RPA, IT help desks spend less time servicing application access requests. RPA also helps to perform security validation across applications.
While RPA does not apply a layer of intelligence to cybersecurity operations, RPA undoubtedly reduces the amount of time spent on routine tasks – reducing the burden on cybersecurity teams.
A proactive cyber defense
We know that cybercriminals use intelligent tools to drive attacks. The best defense is an intelligent countermeasure especially as DDoS attacks are becoming increasingly intelligent.
A machine learning-enabled DDoS solution goes beyond signatures and rate-limiting. ML compares baseline network parameters against measurements taken during an attack and thereby intelligently mitigates an attack even if the attack vector is novel.
Tech giant Microsoft has also deployed machine learning to positive effect. Everyday use of Windows Defender alongside the app’s real-time, cloud machine learning capabilities is proving effective in detecting fresh attack strategies.
In one instance, Defender stopped a much broader attack right in its tracks – showing the power of intelligent ML measures in mitigating these attacks for Windows users across the globe.
ML is hugely effective in creating a sustainable cybersecurity environment. Deploy ML and you significantly reduce the probability that your cybersecurity measures are caught flat-footed by an attacker that uses a novel attack vector.
Saving time and filling in the gaps
AI-enabled firewalls can more effectively mitigate unknown cyberattack strategies, but mitigation can require efforts that go beyond a single task or action such as blocking network traffic.
IA also delivers benefits for routine cybersecurity. Consider endpoint patching and management, for example. Patching is robotic in nature, but effectively patching endpoints to a water-tight degree requires an intelligent approach.
Intelligent automated endpoint management not only saves security teams a significant amount of time, it also ensures tighter endpoint security.
So, IA weaves together the automation of routine processes with the intelligence to comprehensively mitigate novel threats. IA is therefore a critical solution that can take care of complex security tasks – filling in the gaps left by stretched cybersecurity teams.
The above examples demonstrate how automation in its many shapes and forms will aid your cybersecurity efforts. It provides evidence that automation is not just another cybersecurity buzzword. RPA reduces effort, ML adds intelligence and IA delivers end to end automated solutions.
In the context of a high-stakes cybersecurity environment where resources are scarce and information is invaluable, automation delivers a welcome boost which allows you to put your cybersecurity measures on a sustainable track.
Endpoint management is a typical example. Few security teams manage endpoints consistently. In practice, teams lose a lot of time trying to manage endpoints and still end up with security gaps. Some teams get it right most of the time, but just one missed patch can be the gateway to a damaging attack.
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