You can use artificial intelligence (AI) to automate complex repetitive tasks much faster than a human. AI technology can sort complex, repetitive input logically. That’s why AI is used for facial recognition and self-driving cars. But this ability also paved the way for AI cybersecurity. This is especially helpful in assessing threats in complex organizations. When business structures are continually changing, admins can’t identify weaknesses traditionally.
Additionally, businesses are becoming more complex in network structure. This means cybercriminals have more exploits to use against you. You can see this in highly automated manufacturing 3.0 businesses or integrated companies like the oil and gas industry. To this end, various security companies have developed AI cybersecurity tools to help protect businesses.
In this article, I’ll delve into what AI is and how it applies to cybersecurity. You’ll also learn the benefits and drawbacks of this promising technology. First, let’s take a look at what AI is!
What Is Artificial Intelligence
Artificial intelligence is a rationalization method using a statistically weighted matrix. This matrix is also called a neural net. You can think of this net as a decision matrix with nodes that have a weighted bias for each filtering process. The neural net will receive a database of precompiled data. This data will also contain answers to the underlying question the AI solves. This way, the AI will create a bias.
For example, let’s consider a database containing different images. Let’s say it has images of a person’s face and other images of watermelons. Additionally, each image has a tag to check each item. As the AI ‘learns’ whether it guessed correctly or not, the system increments node weightings. This process continues until the system reaches a predefined error percentage. This is often referred to as deep learning, which refers to the decision layers creating the depth.
Now, let’s take a look at the steps used to process data.
Critical Steps in AI Data Processing
We can condense the overall data workflow into the following process:
- Input sensors receive data.
- Data goes through a CPU and gets redirected to an AI process.
- The data enters the statistically weighted matrix of the AI solution. Each node processes this information. Then, it makes a decision using each respective filter.
- The data reaches the final node of the statistically weighted matrix. This determines the final decision.
However, this process is slightly different with deep learning. The first step would include data from a precompiled database tagged with the correct response. Additionally, deep learning will repeat steps 1 through 4 to reach a predefined error tolerance value.
Let’s take a look at this with an example of how AI data is processed.
AI Data Filtering Example
Let’s say a picture has reached an AI node. The node will filter the data into a usable format like 255 grayscale. Then, it’ll run a script to identify features, for example. If these features match others from a filter, the node can make a decision. For instance, it’ll say whether it found a face or a watermelon.
Then, the data goes to the next node down. This specific node could have a color filter to confirm the first decision. The process continues until the data reaches the last node. At that point, the AI will have made a final decision, ensuring whether it found a face or a watermelon.
Importantly, AI systems will always have a degree of error to them. None are infallible, and they never will be. But sometimes, the error percentages could be acceptable.
Now that you know how AI works let’s take a look at AI cybersecurity solutions.
Artificial Intelligence for Cybersecurity
AI cybersecurity addresses the need to automate the assessment of threats in complex environments. Specifically, here are 2 use-cases for AI in AI cybersecurity:
- Detecting anomalies. AI will often detect anomalies in a network’s daily operation. This helps you see when and where your users are accessing the network. Gateway devices also have AI integration for analytics. In case of unusual behavior, some solutions lock users out. Other solutions only send alerts.
- Classifying data. AI is effectively a classification utility. This speeds up the screening process for malware or bad actors. This is useful in organizations that have a lot of data.
Now you know the two main uses of AI in cybersecurity, let’s take a look at its benefits and drawbacks!
AI Benefits and Drawbacks
As mentioned, AI has a lot of benefits. It runs repetitive tasks to identify anomalies or to classify data in particular in your business. That said, a few large drawbacks may offset its benefits. Here, we’ll look at the drawbacks.
AI Accuracy vs Resource Demand
The first drawback is the AI cybersecurity solution’s accuracy. This accuracy also depends on many factors. This includes the neural net’s size and the decisions defined for filtering. It also depends on the number of iterations used to reach the predefined error percentage.
Imagine you have a decision tree with three layers. And each layer has several nodes for each decision route. Even though this is a fairly simple matrix, it needs a lot of calculations. Your system’s finite resources will compromise your solution’s intelligence.
An AI cybersecurity solution provider may stunt its solution’s intelligence/accuracy to meet the target demographic. But sometimes, the problem isn’t intelligence. Instead, it’s low latency and security vulnerabilities. When searching for an AI cybersecurity solution, consider how secure it is in your network.
Static and Continual Training
Once trained, an AI statistical weighted matrix is often not re-trained in service. You’ll find this is due to the lack of processing resources available in hardware. Sometimes, the system learns something that makes it worse, reducing effectiveness. Conversely, humans learn iteratively. This means they cause a lot of accidents. As a result, solution providers must ensure the software meets specification requirements during use.
Cybersecurity often requires updates to counter new exploits. To this end, it takes a lot of power to train your AI. Additionally, your AI cybersecurity vendor will need to update regularly to address cyber threats.
That said, the AI component of an AI cybersecurity solution is for classifying data and assessing anomalies in baseline data. As a result, it doesn’t cause an issue for malware list updates. This means you can still use AI cybersecurity.
Now you know the benefits and drawbacks of AI cybersecurity, let’s take a look at some uses for this technology!
Where You’ll Find AI Cybersecurity
As mentioned, highly automated businesses have the weakest cybersecurity. Generally, automated environments will overlap information technology (IT), operational technology (OT), and the Internet of things (IoT). This is to improve productivity, reduce the unit cost of a product, and undercut the competition.
But this also creates vulnerabilities. To this end, AI cybersecurity is great for finding potential exploits in these companies. Solutions either inform the administrator or automatically apply patches.
However, this may not be enough. Cybercriminals are currently attacking large, highly integrated companies. To do that, they exploit OT, which has no security. This OT was meant for wired networks to send commands to hardware like plant equipment. This means it never posed a security weakness. But today, attackers use OT to access the rest of a network or take plant equipment offline.
OT Risk Management for Manufacturing and Automated Plants
OT risk management tools are becoming popular for the reasons mentioned above. These systems effectively take a real-time clone of the production environment. Then, they run countless simulations to find exploits.
The AI part of the system generally finds exploits. In that case, an administrator provides a solution. OT risk management software continually runs as manufacturing plant arrangements change to meet orders, projects, or supply demands.
In this scenario, AI systems use known malware from antivirus lists to try and find an entry route into the system. The task requires automated repetitive functions of a complex system. And this makes it perfect for AI
So when should you implement AI cybersecurity? Let’s find out.
When You Should Use AI Cybersecurity
As discussed above, businesses that use manufacturing and plant equipment should use AI cybersecurity. In most cases, you’ll also need to look for an OT risk management solution to reduce risks associated with OT.
You also can use AI cybersecurity if your business uses IoT and IT. This way, you can reduce the risk to the network from exploits. IoT devices generally undercut competitors, so you bypass the cost of adding adequate security measures.
Finally, you can use AI even if your company only uses IT. AI helps assess irregular traffic, so it protects your gateways. Additionally, you can leverage AI’s data analytics. This way, you’ll know if someone is using your hardware for malicious purposes.
Now you know all you need to get started with AI cybersecurity, let’s wrap things up!
Final Thoughts
You’ll likely use AI wherever you need automated repetitive tasks. AI also helps make decisions on complex tasks. This is why many cybersecurity solution providers use AI. In fact, these providers’ tools help meet the challenge of highly complex systems that have very poor security.
You can always benefit from AI cybersecurity. It doesn’t matter how integrated your business technology is. AI functionality is also great for classifying data using intelligent operations. This way, you can speed up your search for malware. AI cybersecurity is also beneficial for finding abnormal use of the network.
Do you have more questions about AI cybersecurity? Check out the FAQ and Resources sections below!
FAQ
What is an AI neural net?
An AI neural net is a statical weighted matrix. This matrix helps process input data based on decisions made at nodes with a calibrated bias. To optimize this bias, data gets iteratively passed through the matrix. After that, the success rate is assessed, and each weighting value brings incremental changes. This process is called deep learning.
How intelligent can AI cybersecurity solutions be?
AI intelligence refers to the AI’s error tolerance and decision layers. In theory, you could have as many layers as needed to make an intelligent AI. However, training it with data to reach a high error tolerance could be processor-intensive. This training may also take too long to produce. As a result, the solution becomes ineffective.
How do you decide on your AI error tolerance?
AI is trained using data to meet a predefined error tolerance level. For instance, a self-driving car lasts 1,000,000 miles by design. In this case, the car’s service life determines the AI error tolerance. The AI accuracy must likely be 99.99% correct during decision-making to meet the service life
What is OT risk assessment software?
Operations technology (OT) risk assessment software assesses the security risks of plant equipment. Plants, integrated oil supply chains, and manufacturing 3.0 or above are also prime targets for attacks. AI cybersecurity can help assess threats using a clone of the production system. This helps check routes from OT systems to the rest of the system.
Can AI cybersecurity solutions be used in real time?
Yes, AI cybersecurity works in real-time. This helps detect weaknesses in your network or cyber threats. For example, you can find weaknesses by assessing traffic data through gateways and other hardware. You also can use AI as a centralized OT risk assessment software. This will let you assess the network structure for threats.
Resources
TechGenix: Article on Types of Malware
Learn about the different types of malware your AI cybersecurity solution will have to deal with.
TechGenix: Article on AI Cybersecurity
Find out more about AI cybersecurity.
TechGenix: Article on AI and Deep Learning
Discover more about AI and deep learning.
TechGenix: Article on GRC
Understand how you can protect your organization by following GRC.
TechGenix: Article on OPSEC
Learn how you can make your OPSEC better.
