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An Overview of Artificial Intelligence in Cybersecurity

This is a guest post written by Matt Duffin, a Mechanical Engineer and Founder of Rare Connections

With cybercriminals leveraging AI to launch more sophisticated attacks at a faster rate and up to 84% of cybersecurity professionals experiencing burnout in 2022, it’s essential for businesses to begin implementing modern AI in their cybersecurity toolkit. 

In this article, we’ll take a closer look at AI’s role in cybersecurity – a brief history, the necessity for AI-powered cybersecurity, the difficulties faced, and what you can do to implement better cybersecurity in your business or organization.


The Evolution of AI in Cybersecurity

The evolution of AI in cybersecurity can be well described by the generally accepted three-wave progression of AI:

  • First Wave: Rule-Based Systems
  • Second Wave: Supervised Machine Learning
  • Third Wave: Unsupervised Machine Learning


As the AI waves progress they don’t necessarily replace each other but rather add layers of complexity and capability to existing systems. Let’s take a closer look at the three-wave progression, and how it has taken place in cybersecurity.

1. Rule-Based Systems

The first wave of AI in cybersecurity emerged in the 1980s with simple, rules-based systems. By implementing conditional if-then statements – a rudimentary task for any modern coder – systems could automate security tasks. These rules-based tools automated security analysis and response using knowledge curated by human experts. Examples of these systems include signature-based virus detection, automated firewall policies, and network policy monitoring techniques.

The strength of these systems lies in their ability to reliably catch known threats. Once a particular form of malware or a specific attack pattern has been identified, a rule can be created to detect it, and the system will catch it every time. In the earlier days of cybersecurity, when the types of cyber attacks were relatively few and well-defined, rule-based systems were a powerful defense.

However, rule-based systems suffer from significant limitations. The obvious weakness is their inability to detect threats that haven’t been seen previously – known as zero-day attacks. They are also prone to generating false positives from improperly defined or outdated rules. Rule-based systems require constant updates as new threats are identified, and this process can be resource-intensive from both a computational and resourcing perspective.

As networks and attack surfaces continued to expand, rule-based systems became increasingly challenging to sustain, and less effective as a stand-alone security solution. These challenges underscored the necessity for the next evolution in cybersecurity, paving the way for the second wave: machine learning systems.

2. Supervised Machine Learning

The second wave of AI in cybersecurity allowed for significant progress in threat detection and response with the implementation of Machine Learning (ML). By implementing artificial intelligence algorithms that learned from and made decisions from data, machine learning allowed cybersecurity systems to make decisions without explicit programming. Supervised machine learning is still used in a wide range of modern cybersecurity tools.

Machine learning allows smarter rule-based systems to be deployed that can detect irregular behavior, even if it hasn’t seen that exact behavior before. ML systems understand what ‘normal’ behavior looks like based on training data and can identify deviations from the norm. Machine learning can analyze vast quantities of data with ease allowing these systems to scale to meet increasing demands. The implementation of ML systems has also seen a decrease in false positives that plagued rule-based systems in the past.

However, the integration of ML into cybersecurity has introduced its own set of challenges. ML systems require massive amounts of labeled data for training. Care must be taken to ensure the quality of data used for training ML models to prevent data poisoning – a type of adversarial machine learning attack. Another challenge is ensuring the data used for training ML models doesn’t quickly become outdated, in some cases reducing the effectiveness of the model before it is even implemented. Another issue is the “black box” problem, a characteristic of machine learning algorithms. These algorithms don’t readily offer explanations for decisions which makes it difficult to troubleshoot false positives and other decisions made by the system.

With the difficulties faced by supervised machine learning systems, the need for more adaptive and self-reliant systems has become apparent. This has led to the exploration and implementation of unsupervised machine learning.

3. Unsupervised Machine Learning

The third wave of AI is characterized by systems that learn from experience and can understand, reason, and adapt based on context. The focus is to move beyond supervised learning models that need extensive amounts of high-quality labeled data, and instead lean towards systems that can learn, and utilize data in real time. This approach can allow the discovery of patterns in new data without being given explicit instructions and can adapt their strategies based on the feedback they receive.

Unsupervised ML serves to improve many of the drawbacks seen with supervised ML. These smart algorithms have enhanced anomaly detection capabilities, and are less reliant on extensive labeled datasets. They can recognize previously unknown threats by detecting deviations from the norm, much like supervised algorithms, but without needing extensive prior knowledge of what ‘normal’ looks like. This ability to understand and learn from context significantly enhances the system’s ability to detect novel threats.

Despite these advancements, unsupervised ML faces its own unique set of challenges. The outputs from unsupervised learning are often less interpretable than those from supervised learning. This means that while these algorithms may be excellent at detecting anomalies, understanding why they flag certain activities can be challenging. In addition, adversarial machine learning attacks are becoming more commonplace. In these attacks, cybercriminals use sophisticated techniques to fool machine learning models into behaving in their favor and giving false outputs.

Adversarial Use of AI

Just as advances in AI have allowed cybersecurity professionals to enhance threat detection and response, bad actors have also leveraged these tools for nefarious purposes. Naturally, cybercriminals incorporate these tools into their offensive arsenal just as quickly as organizations implement them in their defense.

One of the most common and damaging uses of AI by cybercriminals is the creation of AI-assisted phishing attacks. The accessibility of natural language processing tools like ChatGPT has made phishing attacks much harder to differentiate from genuine communications, and has crushed any language barriers that may have existed before. Spear phishing attacks are also much easier to personalize now with AI helping to automate the scraping of victims’ public information, and sentiment analysis making it much easier to build a profile.

Hackers are also harnessing the power of AI for brute force password attacks. Traditional brute force attacks can be time-consuming and are often unsuccessful against moderately complex passwords. However, by using AI to exploit patterns in how people generally create passwords, the hit rate of brute force attacks can be significantly increased.

AI algorithms can also help malware to remain undetected by mimicking normal network traffic, altering its code to avoid signature-based detection, or even proactively searching for vulnerabilities within a network’s security system.

Lastly, the realm of deepfakes, synthetic media where a person’s likeness is replaced with someone else’s, has been facilitated by AI. Cybercriminals can use these deepfakes to impersonate trusted individuals and trick victims into revealing sensitive information or bypassing security protocols. Voice cloning is a common way deep fake technology is being used to steal millions of dollars.

AI’s adoption in cybercrime is presenting new challenges for cybersecurity. The very technology being used to strengthen security is also being harnessed for malicious intent, thus maintaining the ongoing battle between digital offense and defense.

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AI in Cybersecurity: Difficulties and Challenges

Incorporating AI into cybersecurity poses significant challenges, amongst which adversarial machine learning, data manipulation/poisoning, membership inference attacks, and prompt injection rank high.

Adversarial machine learning refers to the practice of fooling AI models by leveraging advanced data manipulation at either the training stage or when the model is in service. Hackers create inputs designed to confuse and comprise AI systems. Slight alterations in input data undetectable by humans can lead an AI model to misclassify information and give erroneous outputs. Cybercriminals can misuse this vulnerability to bypass AI-based cybersecurity measures, thereby rendering them ineffective.

Prompt injection is a novel type of attack that targets language-based AI models. Attackers inject malicious instructions or prompts into the model’s input, which could manipulate the AI into generating harmful or inappropriate outputs. An example of this is a Google search containing the words “jailbreak chatGPT” away.

Data manipulation or poisoning attacks are another major concern. Cybercriminals introduce malicious data into the system to corrupt the learning process, causing the AI models to make incorrect predictions or decisions. For instance, an attacker may inject misleading data into a network traffic analysis system, tricking the AI into treating malicious network traffic as normal.

Membership inference attacks pose another serious threat. In these attacks, adversaries use machine learning models to determine whether specific data was used in the training set. By inferring this, they can extract sensitive data or reveal private user information that was not intended to be shared.

Beyond these, the ‘black box’ nature of many AI models further complicates cybersecurity efforts. Lack of transparency and interpretability can make it challenging to understand how AI systems make decisions, which in turn makes it harder to diagnose, correct errors, and effectively respond to threats.

The enormous computational resources and high-quality training data required by AI models present their own difficulties. While AI continues to offer promising advancements in cybersecurity, these challenges underscore the need for ongoing research, proper data management, robust ethical frameworks, and comprehensive defensive strategies. It’s crucial that as we continue to leverage AI for cybersecurity, we also remain mindful of these issues to ensure effective deployment.

Implementation Strategies

A range of AI-powered cyber tools are available for companies looking to strengthen their security efforts. Automated threat detection, malware analysis, and User and Entity Behavior Analytics (UEBA) are a few of the readily available types of tools.

For enterprises looking to implement AI in their cybersecurity efforts, consider the following:

  • Identify Your Needs: AI implementation in cybersecurity is not a one-size-fits-all solution – threat detection, malware analysis, fraud prevention, etc. Initially, it is often easier to focus on a targeted AI application compared to a broad implementation.
  • Develop a Strategy: Create a roadmap for where, how, and why AI can be effectively implemented.
  • Asses Your Data: AI relies on large volumes of high-quality, organized data. Assess your organization’s data management practices considering collection, storage, and labeling.
  • Choose the Best Tools: Look for tools that cater to your specific needs, leverage cybersecurity vendors with relevant experience, and avoid starting from scratch.
  • Hire an Expert: Expert experience will help you avoid common pitfalls and ensure a successful implementation.
  • Test and Iterate: AI models often require constant feedback to improve accuracy over time. Monitor performance and retrain as needed.


The key is to have clear goals for AI and iterate based on continuous learning and improvement. With the right approach, AI can significantly improve enterprise cybersecurity.

The Future of AI in Cybersecurity

The application of AI in cybersecurity is under constant change, which presents both advantages and challenges. The progress from rule-based systems to unsupervised learning has greatly enhanced the capacity for threat detection and response. However, as cyber threats continue to evolve in sophistication, so too must the AI systems designed to counter them. 

Ensuring the successful integration of AI into cybersecurity strategies will require continuous research and development, improved data management, and robust ethical frameworks.

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