How AI And ML Are Remodeling Cybersecurity?
Do you struggle to bar your data from security threats?
If yes, AI (Artificial Intelligence) and ML (Machine Learning) are what you need.
AI and ML are powerful catalysts that have already metamorphosed the world. They have automated every other task including cybersecurity, where it does a great job in removing unwanted data without human intervention. It wouldn’t be surprising if we see machines taking up the cybersecurity space shortly.
In this blog, we will be discussing the usage of AI and ML in the cybersecurity space. We will also see how AI and ML have transformed cybersecurity in all these years and what we can expect in the future.
So, let’s begin!
AL and ML- A Great Cybersecurity Coalition
AI and ML allow you to automate the detection of threats and combat security threats without the involvement of humans that helps you stay more secure. Moreover, with the advancements in AI and ML, many organizations have already started using them to protect themselves against cyber attacks.
Here are some chilling facts that will give you a deeper insight into the significance of AI and ML in the cybersecurity landscape:
- In 2021, the global spending on cognitive and AI systems is expected to reach $57.6 billion.
- The market size for ML is expected to grow to USD 9 billion by 2022 at a CAGR of 44%.
- AI market will grow to a $190 billion industry by 2025.
- In 2021, 75% of commercial enterprises are likely to use AI-based apps.
- 80% of the emerging technologies will have a foundation of AI by 2021.
- According to 65% of companies thinking of deploying ML, it helps in better decision making.
- According to 74% of respondents, ML and AI have the potential to transform the job and industry.
The above stats indicate the growing scope of AI and ML shortly as companies are embracing AI, ML, and DL (Deep Learning). All these acronyms are often used in the technical landscape, thus you must know what each of these means before we move ahead.
Introduction to AI
AI or artificial intelligence is the umbrella discipline that covers everything that relates to making smart machines. These smart machines can perform tasks requiring human intelligence without their intervention.
On the other hand, ML is the system of AI that can self-learn based on the algorithms. Though AI, ML, and DL are interchangeably used, but ML is the subset of AI. ML gets smarter with time without any human intervention. It uses a detection learning algorithm that is used to build smarter systems. The best part of ML is that it can learn automatically without needing to specifically program it.
Apart from this, deep learning is machine learning applied to large data sets. It is inspired by the technique of how the human brain filters information. Thus, most AI-related work requires ML as human behavior requires considerable knowledge.
Now let’s talk about what AI means in the cybersecurity world.
What does AI mean for Cybersecurity?
When we talk about AI in cybersecurity, you might have surely heard about various tools that help the experts record, store, and analyze data. It is segregated into four categories namely: Expert Systems, Autonomous System, Augmented Reality, and Neural Networks.
The expert system is a reliable computer-based decision-making system, which is considered as the highest level of human intelligence that uses facts and heuristics to solve typical issues.
An autonomous system in cybersecurity relates to the systems that impart mobility to various platforms. The best example is self-driving cars, sensing systems, and unmanned aerial vehicles.
Augmented reality is one of the powerful inventions that use a combination of AI, advanced analytics, and automation skills to deliver accurate results. The biggest example of AR in cybersecurity is intrusion detection systems that are used to share cyber threat information across different platforms.
A neural network is a series of algorithms that are used to figure out the relationship in a set of data through a process that is like a human brain. You can consider neural networks as a system of neurons that can be organic or artificial.
You would have noticed that we are concerned about data because it is the core for which ML and AI are adopted. In the section below we will be discussing methods of ML and AI that you can use to analyze the large volume of data. So, let’s move ahead:
- AI and ML can be used to correlate varied data sets to figure out a specific pattern that will give an idea of possible threats. This way, AI and ML make room for predictive analysis where you can forecast the next attack.
- With the help of AI and ML, you can resort to data cleansing techniques or continuous auditing of data protection to safeguard the users from possible cyber attacks.
- Using AI and ML cybersecurity professionals can save costs and avoid expenditure on typical data breach recovery, which is wasteful and doesn’t contribute much.
- Cybersecurity professionals can detect various malware and infections beforehand by scanning a huge amount of data to recognize any possible threats.
What are the areas where AI and ML are Crucial?
As we have read above, that companies are already embracing AI and ML to analyze large data, they are following the digital footprints that hackers leave while trying to access the internal systems. It speeds up the process, saves time, and money by automating and optimizing the existing operations. If we talk about the areas of application, then embedded systems are common in companies such as cameras, printers, and IoT devices are particularly vulnerable to cyber attacks that need AI and ML.
Other important areas include:
- Spam recognition is one such area where a spam-filter app such as Gmail is working wonders. It helps protect the inbox from malware and phishing attacks that ensure important messages aren’t drowned.
- Fraud detection is another area where AI and ML are helping in predictive analysis. The best example is MasterCard that uses AI algorithms to analyze customer behavior and track even the slightest difference.
- Botnet detection is also a crucial area where AI and ML help detect botnet attacks. If you don’t know what botnet attacks are, then these are spams driven by master scripts that rely on multiple users where they repeatedly attack a specific website.
If we look at the comprehensive picture then AI and ML are potent of overtaking everything else related to cybersecurity. The biggest reason is the presence of homogeneous data on varied platforms. ML is beginning to shift to the cloud.
Moreover, we can expect AI and ML to access hidden insights from the data without being programmed. Also, there will be new models that will have the potency to deliver faster and more accurate data. AI and ML will be extensively used to monitor security and will be integrated into the firewalls to flag off any anomaly.
Thus, we can say that AI and ML when leveraged properly can provide a great level of sophistication to the industry especially cybersecurity. The thin line of human intelligence and machines will soon be blurred; however, the key lies in speed, accuracy, and automation.