Assessing the Benefits and Challenges of AI in Cyber Security

Artificial intelligence (AI) has seen significant growth in the cyber security landscape. As mentioned in our previous guides, AI in cyber security is forecasted to reach $102 billion by 2032, with an increased market size of just under 20% CAGR between 2023 and 2032. Additionally, a report by MarketsandMarkets estimates the AI and cyber security market will grow at a CAGR of 23.3% between 2020 and 2026, rising from $8.8 billion to $38.2 billion. 

With this immense and continuous growth, we believe there’s no better time than now to explore what AI cyber security is, how it can be used and assess the benefits and challenges of AI in cyber security. If you’re considering the prospect of bringing AI-based cyber security, you’ll certainly want to read this guide.  

We will explore the following in this guide:

• What is AI in cyber security?
• How can AI be used in cyber security?
• Assessing the benefits of AI in cyber security
• Assessing the challenges of AI in cyber security

What is AI in cyber security?

AI cyber security is still in its relative infancy and has gained and continues to gain more prominence in the global cyber security landscape. Before the emergence of AI within the sector, specialists would take various, often more reactive, approaches to manage cyber security. These traditional methods typically consisted of rule-based systems, manual analysis, and signature-based detection systems. 

• Rule-based systems were used to determine specific rules and policies for sufficient behaviour on a network or system. If these rules were violated by traffic, an alert would be triggered to notify the cyber security specialist. Although rule-based systems could be effective in certain situations, they were known for lacking the flexibility to adapt to emerging cyber threats.

• The manual analysis approach speaks for itself. Cyber security analysts manually review logs and data to identify suspicious activity. Relying on this particular approach was time-consuming and prone to potential errors, making it challenging to remain updated in the ever-evolving cyber threat landscape.

• Signature-based detection systems would manually compare incoming traffic with a database of known threats. When a match was found, the system would trigger an alert. Despite this approach being somewhat effective in combating known threats, it was mostly inadequate against new and unknown threats, often triggering false positives that cyber security specialists would waste their time trying to solve. 

Although these traditional approaches to cyber security were the only solution available at the time, they were mostly ineffective and a drain on the resources of cyber security specialists. As cyber security threats became and continue to become more sophisticated, there was an immense desire for change.

AI in cyber security would be the required change and solution for tackling cyber threats. The pairing of cyber security and AI can assist professionals within the space and support the more traditional methods mentioned above. AI cyber security can be used to detect and prevent cyber attacks, analyse large amounts of data, identify patterns and anomalies, and automate tasks that previously required manual work. 

These AI-based cyber security solutions can help businesses enhance their security posture and mitigate their risk of being compromised. Let’s further explore how AI is used in cyber security.  

How can AI be used in cyber security?

AI and Machine Learning in cyber security, the latter of which is a component of AI relating to the development of computer systems, can be used in a variety of ways. From detecting and blocking malware, responding to incidents, providing endpoint security and more, here is how AI is predominantly used in cyber security.  

• Detecting and blocking malware: AI-powered solutions can be used to develop more effective malware detection and blocking solutions. For example, AI-based cyber security systems can be used to analyse network traffic and file behaviour to identify suspicious activity that may indicate malware software. If left undetected, this malware could result in hackers gaining unauthorised access to your IT infrastructure. 

• Email filtering: AI in cyber security can stretch to developing email filtering systems that can identify and block phishing emails and other malicious emails that are a common threat to businesses of all sizes and industries.

• Network security: AI can be used in cyber security to detect and prevent intrusions into networks and systems. For example, AI can identify suspicious login attempts, network traffic patterns, and user activity, which could indicate a cyber attack or other online-related fraud.

• Cloud security: With the benefits of cloud security becoming increasingly known to businesses, AI-powered cloud security solutions can also help to address common challenges within the cloud, including the assurance that cloud permissions, access controls, and security settings are correctly configured.

• Endpoint security: AI-based cyber security solutions can monitor user and application behaviour for indicators of compromised accounts or malware on a protected endpoint, such as smartphones, laptops and other devices.

• Responding to incidents: AI in cyber security can automate responses to security incidents, which can help cyber security specialists and businesses mitigate the damage caused by attacks. For example, AI can automatically quarantine infected devices and block malicious IP addresses. 

As you can see, AI is a powerful tool that is supporting modern cyber security in many ways. With this comes many benefits and challenges that we’ll address in the two following sections. Let’s start by assessing the benefits of AI in cyber security.

Assessing the benefits of AI in cyber security

We’ve mentioned how AI in cyber security can bring strategic solutions to bolster your security posture and support talent working within the space. Now, it’s time to highlight the pros and advantages that accompany AI and cyber security. From its ability to automate threat detection and enhance real-time responses to offering accurate predictive analysis and reducing false positives and false negatives, let’s assess the benefits of AI in cyber security.

1. Automated threat detection and enhanced real-time responses

One of the most significant benefits of AI in cyber security is its ability to provide automated threat detection and enhance real-time responses. This particular benefit can help reduce the possibility of a cyber attack causing detrimental harm to your business, be it a data breach, financial crime, or reputational damage.

AI and machine learning in cyber security work in tandem to process data from various sources and decipher any anomalies, patterns or indicators that could signify a present or imminent threat. By offering this analysis in real-time, your security team gains valuable insight into current and upcoming attacks, allowing them to take immediate action to combat the threats or develop a plan of action to counter forecasted attacks before they cause any major problems.  

Additionally, machine learning and AI algorithms can be trained to recognise suspicious activity, picking up common behaviours and patterns through the analysis of historical data. When new threats come to light, AI and machine learning can evolve with the threat landscape and modify their detection models to ensure that even the latest tactical attacks can be mitigated in real-time. 

In scenarios where an attack is detected, AI-based cyber security can produce automated alerts to notify your security specialists. These automated alerts include details about the type of attack, the damage it could cause and recommendations on how to reduce its impact. Here, AI and cyber security can give your security professionals the information they require to make informed decisions regarding effectively responding to a threat before it causes issues for your organisation. 

Further, regarding automation, AI and machine learning in cyber security can also automate processes such as initiating incident response workflows, isolating systems affected by the threat and blocking malicious activity. 

Ultimately, this benefit of AI in cyber security supports the day-to-day tasks of your security team’s defensive capabilities. It speeds up their detection and incident response times, allowing them to eradicate a threat before the attacker can fulfil their intentions and before the threat has a chance to spread and cause further harm.

2. Cyber security and AI offer accurate predictive analysis 

Another one of the key benefits of AI in cyber security is its ability to offer accurate predictive analysis regarding various threats and attacks. As mentioned, AI and machine learning in cyber security can analyse historical and present data. This feature enables AI and machine learning to accurately predict the types of cyber threats your business is most vulnerable to, including when they're likely to happen and the impact they could have on your company.

AI-based cyber security can implement automated processes to notify your security team about the attack, be it malware, ransomware, phishing, or a significant data breach. This predictive analysis gives your team the time to react and mitigate the threat before it becomes a major concern. To support your team further, AI in cyber security can put automated measures in place to combat the forecasted attacks that have been identified via predictive analysis.   

Predictive analysis can be conducted manually by a team of cyber security experts. However, doing so is immensely complex as it involves reviewing hefty amounts of data to spot consistent patterns and behaviours from sources such as network traffic, user interactions and threat intelligence. Manually building predictive analysis is possible but can take thousands of hours to complete and has the potential to result in a portion of the analysis being inaccurate due to human error. 

Therefore, using AI-based cyber security solutions to assist your security workforce with predictive analysis makes sense. It will reduce the level of human resources needed for a task, freeing up your security specialists to work on other projects and result in more accurate predictive analysis. Our next point offers more insight into the accuracy of AI and machine learning in cyber security.   

Additionally, the accurate predictive analysis of AI in cyber security can support your business in overcoming zero-day vulnerabilities. For context, a zero-day vulnerability is a security flaw in an aspect of an organisation's IT infrastructure that is unknown to the company, vendor or anyone else capable of mitigating it. Therefore, this means no patch or workaround is available to protect systems from being exploited. 

AI-powered predictive analysis can be used to identify the signs of a potential zero-day vulnerability, giving cyber security specialists the time to engineer solutions to eliminate these previously unknown threats. 

Overall, this benefit of AI and cyber security allows you to predict, forecast and stay one step ahead of the hackers and the potential threats they pose against your brand.

3. AI in cyber security reduces false positives and false negatives

Our final point on assessing the benefits of AI in cyber security regards its ability to minimise false positives and negatives. In terms of their definitions, false positives and false negatives speak for themselves. For context, we’ve provided a simple outline of what each one is:

• False positive: A false positive is when a security system detects a cyber threat that is not actually present or has the potential to cause harm. 

• False negative: A false negative is when a security system fails to detect a present cyber threat that can potentially cause severe damage.

False positives and false negatives typically occur when the previously highlighted traditional cyber security methods are used. These approaches to cyber security, which often rely solely on manual work to sift through large amounts of data, run the risk of human error and inaccurately identifying security issues, such as malware and data breaches. As a result, significant threats can be left undetected and unresolved, potentially leading to irreversible damage that puts your business in grave danger. 

Thankfully, AI-based cyber security can be the difference maker in reducing false positives and false negatives. It can analyse vast amounts of data quicker, more efficiently and accurately than humans. When cyber security and AI are combined, AI can train its rules and configurations to filter out false positives, reduce the risk of false outcomes and focus on helping security teams combat real threats. 

Assessing the challenges of AI in cyber security 

Although AI and cyber security go hand-in-hand to bring many benefits, some challenges still need to be addressed. We wanted to assess the challenges of AI in cyber security so you have complete transparency over the ins and outs of this security tool. In this section, we will outline how AI-based cyber security can fall into the wrong hands, how it can create bias and how it still has its vulnerabilities.

1. AI-based cyber security can fall into the wrong hands 

As much as AI-based cyber security is a tool that can greatly benefit your business, it's crucial to remember that AI and machine learning are technologies with dual uses. What do we mean by this? We mean that it can be used for both good and bad. When used for bad, it poses one of the biggest challenges of AI in cyber security.

With cyber hackers becoming increasingly more sophisticated and advanced in their tactics to develop new types of attacks, they often lean on AI to help them conjure up their latest threat methodology. Whilst your business ramps up its efforts utilising AI to counter cyber attacks, it's a safe bet that cybercriminals will also be exploiting AI to devise new ways to breach your defences and gain unauthorised access to your data and IT infrastructure.  

Here's an overview of the common ways AI can be exploited:

• AI-powered phishing attacks: These attacks use AI to generate personalised phishing emails that are more likely to fool victims into clicking on malicious attachments or links.

• AI-powered malware: Malware that uses AI can be more challenging to detect and remove than traditional malware, as AI can create malware that is continuously evolving and adapting to new security measures.

• AI-powered denial-of-service (DoS) attacks: DoS attacks are designed to overwhelm a system with traffic so that it becomes unavailable to legitimate users. 

• AI-powered deepfake attacks: Deepfake attacks use AI to create fake videos or audio recordings of people doing or saying things that they never actually did or said. Cybercriminals use deepfakes to spread misinformation, damage reputations and commit fraud.

• AI-powered adversarial attacks: Adversarial attacks are designed to exploit an AI model's vulnerabilities into making mistakes. For example, an attacker could create an adversarial image that is designed to fool an image recognition model into classifying it as a different object.

Ultimately, just as AI in cyber security can be trained to follow specific rules and configurations to help protect your business from threats, it can also be trained to be unaffected by AI-based cyber defences.

Therefore, it's crucial to ensure you not only invest in the best AI cyber security solutions but also the top talent within this space. With AI by their side, cyber security specialists can focus their attention on staying up-to-date with the latest cyber security trends and discovering ways to counter emerging attack methods and threat methodology.

2. Cyber security and AI can create bias

Another one of the most notable challenges of AI in cyber security is that it can create bias. In this instance, bias is when an AI algorithm is poorly trained, lacks objectivity and makes decisions or predictions favouring one group over another. Whether intentional or unintentional, biases can result in discrimination against specific demographics and false positives and false negatives. This bias can have negative implications for a company, from financial to reputational damage.

As mentioned, these biases can occur if the AI cyber security algorithm is trained poorly. For example, if the AI algorithm is trained to analyse data retrieved from males, it could disregard any data stemming from females - resulting in a biased and misrepresented outcome. 

We touched on how AI algorithms can be intentionally or unintentionally biased. For example, an algorithm deliberately designed to identify criminals might be biased to favour a particular race or gender if the people who designed and trained the algorithm are themselves biased. An unintentional bias could occur through a lack of knowledge, consideration and market awareness.

Cyber security experts should be encouraged to train AI algorithms with transparency, fairness and equity, regardless of whether intentional or unintentional. When using AI in cyber security, specialists should ensure that the data sets on which they train the algorithm are diverse and well-represented.

Bias in AI cyber security can be mitigated by bringing the right cyber security experts into your business who take an unbiased approach to their work. These specialists can also help educate your wider business on how to develop trustworthy AI algorithms that generate reliable results and represent your business in the best and most transparent light. 

3. AI in cyber security still has its vulnerabilities

Our closing point on our assessment of the challenges of AI in cyber security is that, although highly valuable, it still has its vulnerabilities. As discussed, AI is still vulnerable to the exploitations of hackers, who use it to serve their criminal activities. Despite AI-based solutions being an excellent tool for defending against cyber threats, there is still the chance of these defences being compromised by hackers. 

AI and machine learning in cyber security must continue developing and staying ahead of the industry's darker side to counter this challenge. Additionally, tighter regulations and laws against cybercrime and the exploitation of AI will need to be introduced to mitigate the wrongful use of AI.

As mentioned, AI in cyber security can be used to minimise false positives and false negatives. Although it typically provides more accurate results than solely relying on human analysis, AI cannot guarantee to provide perfect outcomes every time. More advancements will be required to overcome this vulnerability.

When looking at these challenges of AI in cyber security, remember this technology is relatively new and is always progressing. These challenges should not put you off utilising AI to support your cyber security procedures; in fact, we encourage it. However, these challenges are an indicator that we are far from solely relying on AI and machine learning to take care of cyber security, and talent is still very much at the forefront.  

Therefore, when assessing the vulnerabilities of AI, it's crucial to consider AI and machine learning as a tool to assist your cyber security specialists rather than entirely replacing them.

The final word on AI in cyber security

Our assessment of AI in cyber security reveals it as a powerful and transformative tool reshaping the security landscape. AI has come a long way from the traditional methods of rule-based systems, manual analysis, and signature-based detection, which have proven inefficient in dealing with the ever-evolving cyber threat landscape compared to AI.

The benefits of AI in cyber security are substantial. It offers automated threat detection and enhanced real-time responses, allowing organisations to respond swiftly to potential threats and minimise damage. Accurate predictive analysis empowers businesses to proactively defend against known and even emerging threats, reducing the element of surprise. Additionally, AI significantly reduces false positives and negatives, enhancing the overall effectiveness of security systems.

However, AI in cyber security is not without its challenges. It can fall into the wrong hands, with cybercriminals leveraging AI to create more sophisticated attacks. Bias in AI algorithms also remains a concern, requiring careful training and diverse data representation to prevent discrimination. Furthermore, AI-based solutions are not immune to vulnerabilities, and ongoing advancements and regulatory measures are needed to address these issues.

Ultimately, AI serves as a powerful ally to cyber security specialists and businesses needing to bolster the defences of their IT infrastructure. Again, it’s important to emphasise that whilst AI can augment your defence capabilities and provide invaluable support, it should be viewed as a complementary tool rather than a replacement to your cyber security team. 

As we move forward, a holistic approach that combines human expertise with AI-driven solutions will be essential to stay ahead in the ever-changing cyber security landscape.

Get in touch with a cyber security recruiter today!

Now that you've gained a clear understanding of the benefits and challenges of AI in cyber security, you may be considering your next move: hiring skilled cyber security professionals to utilise AI and machine learning effectively to strengthen your IT infrastructure and protect your brand's resilience. Fortunately, you don't have to navigate this path alone; we're here to help you.

Our experienced team of cyber security recruitment specialists are ready to connect you with top-tier professionals capable of seamlessly integrating the benefits of AI and cyber security into your business. Get in touch with one of our consultants today, and let's explore how we can help your organisation harness the benefits of AI in cyber security.