In what feels like 10 minutes, cybersecurity AI and machine learning (ML) have gone from a concept pioneered by a handful of companies, including SenseOn, to a technology that is seemingly everywhere. 

In a recent SenseOn survey, over 80% of IT teams told us they think that tools that use AI would be the most impactful investment their security operations centre (SOC) could make. 

However, although demand for AI is soaring, misconceptions around AI cybersecurity use cases and terminologies are still very common. In one vendor survey, 65% of security executives admitted they lack education about the latest AI technologies.

We try to clarify some of the misconceptions surrounding real-world cybersecurity AI use cases. In this blog post, we explore some of the conceptual and practical differences between machine learning (ML) and artificial intelligence (AI) as used in cybersecurity applications. 

One core takeaway is that you could describe the same application as using machine learning (ML) and artificial intelligence (AI) technology and not be wrong. 

Machine Learning vs Cybersecurity AI

• Machine learning (ML) describes an AI algorithm that learns from data.

• Cybersecurity artificial intelligence (AI) is a broad term that covers different types of algorithms, including machine learning algorithms.

The graph below explains the semantic relationship between these two terms.

In other words, cybersecurity AI describes a range of approaches to security that could include machine learning and other types of AI.

If you work in a SOC that uses a machine learning threat detection solution, you are using cybersecurity AI. However, it's important to note that, on a technical level at least, there is no such thing as “cybersecurity AI.” There is also no cybersecurity-specific AI machine learning technology.

All AI models, including machine learning models and applications, rely on algorithms (sequences of maths formulae and instructions that tell a model how to react to data). These algorithms are found in a vast range of use cases, from search engines like Google to self-driving cars.

The core difference between one use case and another is the data input into the algorithm.

Cybersecurity AI is when an AI model of some kind interacts with cybersecurity data like files, logs, packets, etc.  

Machine Learning Learns from Data, Cybersecurity AI Doesn't Have To

Machine learning is the name given to algorithms that learn from data. 

A machine learning model uses a set of data it knows to learn how to build a model it can use to understand data it does not know anything about, e.g. if an ML model is told that x is y, then it can eventually figure out that x+1 is likely to be y+1.

• Machine learning is a set of methods for predicting, detecting and grouping data based on learnings from other data.

Not all AI models have to learn from data like this. Some types of AI, such as heuristics, use a premade set of patterns instead, i.e., comparing the patterns in their database to an application's behaviour.

The advantage of machine learning methods is that they can understand new information based on information they already know. This is how a driverless car knows how to go around a roundabout it's never seen before - because it has successfully navigated similar roundabouts.

Machine Learning Is a Powerful Cybersecurity AI Toolset

Machine learning can find malicious activities even when not specifically trained to find those exact activities. 

In any modern IT environment, there are so many variants of user, device and network behaviours that compiling an accurate predefined rule set of “normal” events and applications is almost impossible. 

Cybercriminals have also learned how to take advantage of predictable detection methods by obfuscating malware and developing exploits that abuse legitimate, trusted device processes.

Using security tools that leverage machine learning is one of the best ways for organisations to respond. 

Here's an example of how a machine learning model might be trained to recognise network behaviours indicative of compromised devices even when it has never seen those behaviours before. 

1. You feed a security tool with machine learning libraries.

2. Then, you feed the tool with examples of traffic from compromised devices.

3. The tool uses machine learning to develop a predictive model to understand when a device has been compromised based on how it interacts with a network.

4. You feed the tool unlabeled information about recent network traffic.

5. The tool can then use the predictive model it develops to spot whether a device is likely to be compromised or not.

The above is a very high-level view of how machine learning can fit into a cybersecurity AI use case by taking known data (e.g., this is what malicious traffic looks like) and using it to figure out how to detect similar patterns in never-before-seen data. 

3 Cybersecurity AI and Machine Learning Use Cases

There are a lot of cybersecurity AI machine learning use cases. You can find variations of machine learning in every security solution type today. 

Here are three examples: 

1. Anomaly detection in network traffic

Unsupervised machine learning models can analyse network traffic without labelled data to identify unusual patterns or anomalies that deviate from normal behaviour.

2. Malware detection and classification

Classification algorithms can be trained on labelled datasets of malicious and benign software. 

Deep learning (a type of machine learning) models like convolutional neural networks (CNNs) can then analyse file structures and code to detect and classify malware, including zero-day threats that signature-based solutions would miss.

3. User behaviour analytics (UBA)

Anomaly detection algorithms monitor user activities to establish a baseline of normal behaviour. 

Techniques like Hidden Markov models (HMMs) and recurrent neural networks (RNNs) analyse sequences of actions over time to detect deviations indicative of insider threats or account compromises.

The Best Way to Get Cybersecurity AI and Machine Learning Benefits

… Is to deploy a security tool that uses a combination of various cybersecurity AI methodologies. 

SenseOn is a multi-model AI-driven cybersecurity platform that was recognised by the World Economic Forum in 2021 as a technology pioneer due to our advanced security AI-powered cybersecurity.

Our AI-powered platform uses a “Universal Sensor” - a single piece of software deployed across devices, servers, databases and cloud environments - to capture user, device, process and network telemetry all the way down to deep packet inspection. 

We use a combination of supervised and unsupervised machine learning models in a proprietary AI model to deliver detections that traditionally would have taken hours in less than 10 minutes.

SenseOn’s detections are mapped to the MITRE ATT&CK framework and presented in the platform as “Cases,” while false positives are automatically filtered out. 

Our technology can also supercharge your existing SIEM solution types by reducing cost and improving the mean time to respond (MTTR). Using SenseOn’s AI technology, organisations can get benefits like reducing SIEM data costs by 57%, saving £10k per month. 

You can deploy SenseOn in minutes, manage it yourself, or outsource the management to us as a managed detection and response (MDR) partner.

Learn more about how an integrated AI-powered platform can deliver results with a free report.