Avoiding loopholes of machine learning in business
As one of the most hyped buzzwords of our time, machine learning (ML) seems to be the magic bullet for virtually every challenge in every sector, including business. Due to this misconception, many today believe that the integration of this technology into any system spells success.
Unfortunately, the reality has proven to be far different as simply adding the word to a concept does not result in a magical reaction. It is true that ML does hold significant promise to improve various aspects of the business. However, proper deployment is paramount to its success.
One area of interest for business operators when it comes to the implementation of machine learning is managing cybersecurity. What are the loopholes standing in the way of ML adoption for cybersecurity in business and how can we overcome them? Let’s find out.
Poor quality data
In as far as ML is concerned, in cyber warfare and beyond, data is one of the most critical factors. Machine learning models are only able to achieve their full potential when they have access to large and diverse datasets for training.
But it is not all about the quantity of data; quality matters, too. Most businesses have more than enough data at their disposal. But not all such data is complete. Why? A majority of the devices in use today for data collection do not have instrumentation as an integral feature. Therefore, the data provided through them does not always capture critical points.
A good number of organizations also fail to disclose data surrounding cyber-attacks due to legal, privacy and reputational concerns.
It is important to ensure that you use high-quality data without any form of human or technical bias. That would be the only way to achieve the desired results.
Reinventing the wheel
One of the top reasons why businesses fail at implementing ML for cybersecurity is the fact that most try to reinvent the wheel, every single time. Business operators who hire developers to build ML algorithms are perpetrating this mistake.
In much the same way as you would not hire an electrical engineer to bake pastries for you, you do not necessarily need your cybersecurity professionals to create algorithms. Lots of algorithms are already in existence thanks to tech giants such as Google and Microsoft.
Rather than building the ovens for baking your pastries from scratch, get a seasoned chef and use existing appliances. Your team does not have to figure out how neural networks work. They simply need to know how to use them to keep your business safe.
Hardware challenges
A major weakness that cybercriminals exploit is hardware. Note that the modern-day business model consists of vast networks that extend beyond a single building. In fact, many networks spread across borders and could even be global.
Such vast networks pose a major threat as there are higher chances of getting a weak point somewhere in the chain. And we all know that every network is only as strong as its weakest point. Businesses can greatly benefit from enhancing the hardware in such networks as a starting point.
Hardware designs should incorporate security and hardware network architecture can have the capacity to monitor the security state of the network intelligently. Furthermore, for effective adoption of ML, the hardware in use should eliminate compute barriers. This way, it would have what it takes to solve more complex problems.
Software challenges
The typical cybersecurity set is extremely large. Therefore, the networks in use for ML model processing and data delivery must likewise have the capacity to handle large datasets. Unfortunately, though, such networks are extremely scarce, once again hampering the implementation of ML for cybersecurity.
In order to create such networks, it would be necessary to undertake more careful software design. With the use of such software, it would be possible to leverage the full potential of ML-based technologies such as Natural Language Processing (NLP) in securing networks against cyber threats.
For instance, it would become much easier to extract the identity of key actors from past cyber incidents on media reports and elsewhere.
Man vs. Machine
To some business operators, ML is poised to replace human beings in various capacities as it promises greater efficiency. That is yet another mistake since, like any other technology, machine learning has its weaknesses.
Human intuition and knowledge remain a vital component of cybersecurity. It contributes significantly to understanding the depth of issues at hand and charting the way forward on how to react to threats.
Notably, not every sophisticated attack is AI-based. There are numerous human threats as well and these need humans to analyze and plan counteractive measures. Only humans can recognize some behavioral patterns. So leaving them out of the equation could be the worst move a business makes.
Rather, businesses should adopt a hybrid approach that brings together man and machine, highlighting the strengths of each one. The ML-based model can handle the mundane aspects of the task while also tackling surveillance. On the other hand, the human would be in the best position to make decisions and recognize the kind of patterns a machine may fail to notice.
Balancing the act
Business executives should take every effort to ensure they do not get caught up in the hype and fail to optimize the opportunity. While it is true that machine learning holds massive potential for business cybersecurity, the secret to harnessing this potential is proper application.
Take the time to understand the technology and its correct implementation before jumping on the bandwagon. Find ways to balance the act between man and machine, to keep hardware and software in optimal condition for the technology and to put into practice the other outlined suggestions. Actually, that is the hardest part.
The rest of it, solving cybersecurity problems using machine learning should be a walk in the park. But without the right preparatory steps, no amount of technology can put an end to cyber woes.
