Artificial Intelligence in Cybersecurity: Pros and Cons

We can use artificial intelligence to automate complex, repetitive tasks faster than humans can.

Artificial intelligence technology can logically sort complex and repeated inputs. This is why artificial intelligence is used in facial recognition and self-driving cars. But this capability also paves the way for AI cybersecurity. This is particularly useful for assessing threats in complex organizations. When business structures are constantly changing, administrators often fail to identify weaknesses.

In addition, the network structure of enterprises is becoming more and more complex. This means there are more vulnerabilities that cybercriminals can exploit against us. We can see this in highly automated Manufacturing 3.0 enterprises or integrated companies such as the oil and gas industry. To this end, various security companies have developed AI cybersecurity tools to help protect businesses.

This article will take an in-depth look at what artificial intelligence is and how it is applied to cybersecurity. We will also look at the advantages and disadvantages of this promising technology. Next, let’s first take a look at what artificial intelligence is!

What is artificial intelligence?

Artificial intelligence is a rationalization method that uses statistical weighting matrices. This matrix is ​​also called a neural network. You can first think of this network as a decision matrix, where the nodes have weighted biases for each filtering process. The neural network will receive a database of precompiled data. The data will also contain answers to potential questions that AI can solve. In this way, AI can become biased.

For example, a database containing different images. Suppose it has a face image and other watermelon images. Additionally, each image has a tag to check each item. As the AI ​​"learns" whether its guesses are correct, the system increases node weights. This process continues until the system reaches a predefined error rate. This is often called deep learning, which refers to the creation of deep layers of decision-making.

Next, look at the steps used to process the data.

Key steps in artificial intelligence data processing

The entire data workflow can be condensed into the following process:

1. Input the sensor to receive data.

2. The data passes through the CPU and is redirected to the artificial intelligence process.

3. Data enters the statistical weighting matrix of the artificial intelligence solution. Each node processes this information and then makes a decision using each respective filter.

4. The data reaches the last node of the statistical weighting matrix. This determines the final decision.

However, this process is slightly different from deep learning. Step 1 will include data from the precompiled database, tagged with the correct response. Additionally, deep learning will repeat steps 1 to 4 to reach a predefined fault tolerance value.

Let’s look at this through an example of how to process AI data.

AI data filtering example

Suppose a picture reaches an AI node. This node will filter the data into a usable format, such as 255 grayscale. Then, a script is run to identify the characteristics. If these characteristics match other characteristics in the filter, the node can make a decision. For example, it will indicate whether it found a face or a watermelon.

Then, the data goes to the next node. This particular node can have a color filter confirming the first decision. This process continues until the data reaches the last node. At that point, the AI ​​will make the final decision to ensure it finds a face or a watermelon.

The important thing is that artificial intelligence systems will always have a certain degree of error. Nothing is absolutely correct, ever. But sometimes, the error percentage is acceptable.

After understanding how artificial intelligence works, let’s take a look at artificial intelligence’s network security solutions.

Artificial Intelligence in Cybersecurity

Artificial intelligence in cybersecurity addresses the need to automatically assess threats in complex environments. Specifically, here are two AI use cases in cybersecurity:

1. Detect anomalies. Artificial intelligence often detects anomalies in the day-to-day operations of a network. This helps understand when and where users access the network. The gateway device also features AI integration for analytics. Some solutions lock out users if unusual behavior occurs. Other solutions only send alerts.

2. Classified data. Artificial intelligence is actually a classification utility. This speeds up the screening process for malware or bad behavior. This is useful in organizations with large amounts of data.

These are the two main uses of artificial intelligence in network security. Let’s take a look at its advantages and disadvantages!

The advantages and disadvantages of artificial intelligence

As before As mentioned, artificial intelligence has many benefits. It can run repetitive tasks to identify anomalies or classify data. That said, some big drawbacks may outweigh its benefits. So let’s look at the shortcomings.

AI Accuracy vs Resource Requirements

The first disadvantage is the accuracy of AI cybersecurity solutions. This accuracy also depends on many factors. This includes the size of the neural network and the decisions defined for filtering. It also depends on the number of iterations required to reach a predefined error rate.

Suppose there is a three-level decision tree. Each layer has multiple nodes for each decision path. Even though this is a fairly simple matrix, it requires a lot of calculations. The system's limited resources compromise the intelligence of the solution.

Artificial intelligence cybersecurity solution providers may hinder the intelligence/accuracy of their solutions to satisfy the target population. But sometimes, the problem isn't IQ. Instead, it has low latency and security vulnerabilities. When looking for AI cybersecurity solutions, consider its security within the network.

Static and Continuous Training

Once the artificial intelligence statistical weighting matrix is ​​trained, it is usually not retrained in the service. This was found to be caused by a lack of available processing resources in the hardware. Sometimes the system learns something that makes the situation worse, making it less efficient. In contrast, humans learn iteratively. This means a lot of accidents. Therefore, solution providers must ensure that the software meets specification requirements during use.

Cybersecurity often needs to be updated to respond to new attacks. To do this, a lot of power is needed to train the AI. Additionally, AI cybersecurity vendors will need regular updates to address cyber threats.

That is, the artificial intelligence component of an artificial intelligence cybersecurity solution is used to classify data and evaluate anomalies in baseline data. Therefore, it does not cause problems with malware list updates. This means AI cybersecurity can still be used.

After reading the advantages and disadvantages of artificial intelligence network security, let’s take a look at some uses of this technology!

Where to find AI network security

As before As mentioned above, highly automated enterprise network security is the weakest. Generally speaking, automation environments overlap information technology (IT), operational technology (OT), and the Internet of Things (IoT). This is to increase productivity, reduce the unit cost of the product, and undercut the competition.

But this can also create loopholes. To this end, AI cybersecurity can be helpful in uncovering potential vulnerabilities in these companies. The solution is to either notify the administrator or apply the patch automatically.

However, this may not be enough. Cybercriminals are currently targeting large, highly integrated companies. To do this, they leverage OT without security. This OT is for wired networks to send commands to hardware, such as factory equipment. This means it never constituted a security vulnerability. But today, attackers use OT to gain access to the rest of the network or take factory equipment offline.

OT Risk Management for Manufacturing and Automated Factories

OT risk management tools are becoming more and more popular due to the above reasons. These systems effectively take a live clone of the production environment and then run countless simulations to find vulnerabilities.

Vulnerabilities are often found in the AI ​​part of the system. In this case, the administrator will provide a solution. OT risk management software continuously operates as manufacturing plant schedules change to meet order, project, or supply needs.

In this case, the AI ​​system uses known malware from antivirus lists to try to find an entry path into the system. This task requires the automated, repetitive capabilities of complex systems, which is ideally suited for artificial intelligence.

So, when should you implement AI cybersecurity?

When should you use AI cybersecurity

As mentioned above, businesses using manufacturing and factory equipment should Using artificial intelligence for cybersecurity. In most cases, one also needs to look for an OT risk management solution to reduce the risks associated with OT.

If enterprises use IoT and IT, they can also use artificial intelligence cybersecurity. In this way, the risk of network attacks can be reduced. IoT devices are often sold at a lower price than competitors, so the cost of adding adequate security measures is also eliminated.

Finally, even companies that only use IT can also use AI. Artificial intelligence can help assess irregular traffic to protect gateways. In addition, AI data analysis can also be used. This way, you can tell if someone is using the hardware maliciously.

In summary, that’s all about artificial intelligence network security, a brief summary!

Summary

We may use artificial intelligence wherever repetitive tasks need to be automated intelligent. Artificial intelligence also helps in decision-making on complex tasks. This is why many cybersecurity solution providers use artificial intelligence. In fact, these providers' tools help address the challenges of highly complex and poorly secured systems.

We can always benefit from AI cybersecurity, no matter how integrated the business technology is. AI capabilities are also great for classifying data using intelligent operations. This way, you can speed up your search for malware. AI cybersecurity can also help detect abnormal usage of the network.

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