How AI Is Changing Cybersecurity

Artificial intelligence (AI) is a subset of computer science that is focused on reducing reliance on human intelligence so that operations can be automated and streamlined. To do so, AI builds intelligent machines capable of performing certain tasks so that humans don’t have to.

Given the advancements in AI technology and the increasing need for intelligent and efficient cybersecurity, it makes sense to leverage cybersecurity with AI. But while AI offers a number of advantages for cybersecurity, it also presents some unique challenges. This article will examine how AI is changing cybersecurity and what you might want to consider when merging one with the other.

The idea behind AI is to collect and analyze data to enable sound decision-making via machine learning, without human interference. As decisions are made, the information gathered is used to refine and improve future decisions and outcomes. This is achieved via the three principles of AI: learning, reasoning, and self-correction.

As AI technology continuously operates on those three principles, it replicates some of the intelligence of a human being. The ability to learn and make outcome-based adjustments is one reason why AI and machine learning hold such promise as a cybersecurity tool.

Supervised vs. Unsupervised Machine Learning

AI or machine learning can be conducted in either a supervised or unsupervised manner. 

Supervised AI Learning

In supervised AI, humans train the machine using information that has already been labeled for a specific output. Supervised learning can be compared to a student learning with the help of a teacher. It can give you an explicit answer based on its prior knowledge.

Supervised training helps the machine learn how to detect patterns, and how input data and output labels relate to each other, so it can learn to yield accurate results. Supervised learning is powerful in that it allows a human being to do the data classification. Where classification is needed (such as for URL filtering, antivirus detection and spam identification), it’s very effective.

The challenge in using supervised AI learning in cybersecurity is that much of the data to be analyzed lacks labels. For supervised learning to really be effective, you’ll need lots of well labeled data. And if a person decides to create a new label based on certain attributes, that’s an extra step you’ll need to consider — potentially over and over again.

Unsupervised AI Learning

In unsupervised AI, unlabeled data is presented to the algorithm, which then analyzes it to discover clusters or patterns without human intervention. Compared to supervised AI, unsupervised AI allows for processing of more complex tasks. 

Unsupervised AI is capable of finding previously unrecognized patterns. Unlike labeled data, the unlabeled data used for unsupervised learning can be obtained from a computer without manual steps.

If you know you want to identify data patterns but don’t really know what you’re looking for, an unsupervised learning approach is especially useful. Since attackers are always changing their methods, unsupervised learning has major potential to be used to look for anything out of the ordinary, rather than just known threats.

Cybersecurity is the term used to describe the collective steps taken to protect networks, servers, computers, devices and their data from unauthorized access by potential bad actors. As online services and internet of things (IoT) continue to grow, cybersecurity will become even more important for the success and survival of business enterprises across industries.

Keeping your business safe from costly downtime or attacks that undermine your reputation is a main objective of cybersecurity, as is protecting users and their private data. Since large amounts of data are collected, handled and stored on a daily basis, cybersecurity is critical to protecting sensitive information like trade secrets, financial records and the private data of users.

Common Cybersecurity Challenges

Despite advancing cybersecurity technology, there are 4 consistent challenges that organizations face in securing their networks and services:

• Having to hunt for threats manually consumes time and money, meaning more attacks have the potential to go undetected.

 

• Geographical spread between IT systems and infrastructure differences across locations make tracking malicious incidents more difficult.

 

• Attackers use hidden or dynamic IP addresses to stay anonymous by using tools like proxy servers, Tor browsers or virtual private networks (VPN).

 

• Since it’s very difficult to foresee attacks before they happen, organizations usually end up taking a reactive approach to cybersecurity, rather than a proactive approach.

AI gives organizations a set of additional tools to help them deal with these challenges, further securing themselves and their users against cyberattacks. While integrating AI in your cybersecurity strategy can’t guarantee there won’t be a breach, it can make stronger security easier to achieve. 

Traditional cybersecurity measures are valuable, especially when they overlap. But while these techniques are effective for threats that are already known, they won’t be able to ward off threats that haven’t been identified yet. This is where AI comes in.

Why Integrate AI in Cybersecurity?

Integrating traditional and AI-based cybersecurity is the best way to detect both known and unknown threats. Here’s why:

1. Proactive Anomaly Detection — Using behavior analysis and on-the-fly evolution of behavior parameters, AI helps to identify anomalies that could indicate an attack before it has a chance to take hold. This is true even if that type of attack hasn’t been seen before. AI can also be used to find “low and slow” attacks that are designed to bypass rules-based systems.

 

2. Real-time Breach Response — AI immediately and autonomously generates a defensive patch any time an attack is identified. So when threats are detected, these patches allow AI and machine learning to spring into action, either alerting your cybersecurity team or responding automatically in real-time without human engagement.

 

3. Prediction of Future Breaches — With AI and machine learning, massive amounts of varied types of data can be processed quickly, enabling threats to be anticipated before they happen.

AI’s capacity for perpetual learning is its most useful feature when it comes to cybersecurity. Deep and machine learning allow AI to comprehend network behaviors and identify patterns, which sets you up for successfully detecting, responding to and predicting cyber threats.

However, these aren’t the only benefits of AI on cybersecurity. Its ability to learn, reason, and self-correct has other benefits that illustrate how AI is changing cybersecurity.

Streamlined Tasks

Using AI, it becomes much easier to sustain constant security procedures which used to require a long list of tedious tasks performed by humans. AI can help eliminate those tasks, freeing up your IT team to focus their attention elsewhere.

Streamlined Data Handling

At the enterprise level, massive amounts of data are processed and transferred every day. Traditionally, people have had to manually secure and evaluate it. With AI, these processes can be automated, including looking at data for possible threats. This makes detection and response far more efficient.

Lower IT Costs

As less human effort is required for threat detection and response, cybersecurity becomes less costly. According to Capgemini, the average reduction in cost is 12%. Some organizations are able to reduce their costs by over 15%.

“Smarter” Overall Security

The more data is collected and analyzed over time, the more AI will learn from the patterns and decisions of the past. As its ability to detect and respond to malicious activities improves, so does your overall security.

Aside from rapid threat prediction, detection, and response, AI and machine technologies can be successfully applied to many different cybersecurity functions, including but not limited to:

• Fraud detection — reducing financial risk for users and enterprises by predicting unusual behaviors

 

• Malware discovery — predicting future infections by using patterns learned from previous malware

 

• Network risk assessment — ranking the risks of different network segments by analyzing the data of previous threats to identify which parts are the most likely targets

 

• Vulnerability remediation — learning to distinguish between typical and aberrant user behaviors that could signify an attack, proactively illuminating vulnerabilities so they can be addressed

 

• Spam filtering — using algorithms to detect whether incoming emails are legitimate or not

 

• Botnet detection — identifying a potential botnet invasion based on legitimate versus illegitimate user behavior and traffic patterns

 

• User authentication — ensure the legitimacy of users by looking for odd behaviors and enhancing biometric identification

 

• Endpoint protection — combining geolocation and time zones with expected behavior patterns to identify suspicious activities on company assets across the globe

While it’s clear by now that there are a host of advantages to making AI part of your overall cybersecurity posture, there are also some potential pitfalls to be aware of.

Adding AI and machine learning could be the best thing you could do for your cybersecurity profile. But there are some potential stumbling blocks that need to be addressed before deciding when, where and how to integrate AI into your overall security plan.

Potential Bias

One of the innate qualities of being human is the formation of biases, and they are not always easy to see. Unfortunately, AI is also prone to bias, but it’s much more difficult to identify in technology than in people. 

In cybersecurity, human information, context and knowledge is imparted to AI to help manage risks and blind spots that we can see. So while AI is an excellent cybersecurity tool for reasons already discussed, it can also reflect any human biases intentionally or unintentionally imparted to it, including ideologies, gender, race, age, disabilities and more.

As AI gains more ground in cybersecurity, it will become even more important to be able to trust that AI-based outcomes are free of bias. Left unchecked, AI bias could create major problems for diversity and cybersecurity. Should you choose to implement AI into your cybersecurity posture, your team will need to be keenly aware of the potential risks, including:

• Biased Business Rules — Algorithms are created to align with the rules of business logic, written by human beings with their own (often unrealized) biases. Because of this, AI can mirror the unconscious security risk assumptions of those who set it up.

 

• Narrow Training Data — AI can only make decisions based on the training data it receives, which is inherently neutral until it passes through the filter of humans and their biases. By the time algorithms are built, human bias has had its effect in the form of sampling decisions, data classifiers, and how training data is weighed.

 

• Non-diverse Collaborators — When the people who contribute to the training of AI are too similar, it becomes very difficult to ensure the diversity of algorithms, which is needed to provide balance and fairness when they are applied.

Building diverse teams who understand the importance of diversity and the risks of AI bias can go a long way to ensuring fair algorithms and balanced training data.

Data Sets

AI cybersecurity models are trained using learning data sets. This means your team will need to obtain many different sets of accurate data for malware and malicious codes, and other abnormalities. For many organizations, getting their hands on all that data is a big challenge.

Expense

Adopting AI and machine learning is an expensive, time-consuming undertaking. Extra computing power, data and expertise are needed to build and maintain an AI system.

AI in the Wrong Hands

Cybercriminals are always testing their capabilities to make their tools resistant to AI security. Attackers continually learn from existing AI tools to help them build more complex attacks for both traditional and AI-integrated cybersecurity systems.

This includes the potential for neural fuzzing. Fuzzing is when large amounts of random data are software-tested to identify any weaknesses. With neural fuzzing, AI is leveraged to do so very quickly. From a hacker’s perspective, neural fuzzing makes it easier to target a system by identifying its weaknesses.

The role of artificial intelligence in cybersecurity will continue to expand, helping enterprises enhance their security profiles. For example, PingIntelligence for APIs leverages AI to monitor your API traffic, gain insights into abnormal API behaviors, and detect and guard against potential threats.

In the wrong hands, AI can make cybercrimes easier and more automated, which makes a comprehensive cybersecurity solution even more critical. Since no cybersecurity solution is 100% iron-clad, your best bet is to combine traditional and AI-based cybersecurity technologies. Ping Identity can help you design the best strategy for you.