What Is AI Anomaly Detection?

The goal of anomaly detection is to spot aberrations in data. But as organizations collect more and more information in more and more places, discovering deviations from the norm can be extremely difficult. Enter AI. Now, huge data sets can be quickly scanned to find patterns that fall outside the norm and thus pinpoint anomalies. AI-driven anomaly detection is useful in spotting financial fraud, certain medical conditions, and network intrusions, among many other applications.

What Is AI Anomaly Detection?

AI anomaly detection is a process where an artificial intelligence model reviews a data set and flags records considered to be outliers from a baseline, which represents normal behavior and serves as a reference point for comparison. The expected baseline for a data set is established during the model training process using a combination of historical data, industry expectations, and project objectives.

Anomaly detection can be accomplished with traditional data analysis, but that’s powered by manually established rules. The static and narrow scope of those rules creates limitations that can be overcome by an AI model’s ability to evolve and adapt over time. AI anomaly detection can also be performed without baseline data in cases where the system processes semi-supervised and unsupervised data.

Anomaly detection has both industry-specific and operational applications; examples include analyzing credit card transactions, security logs, and production data. As organizations shift to an IT landscape of multicloud environments and generative AI projects, AI anomaly detection becomes even more useful. For IT departments relying on multicloud, for example, the environment’s native complexities—multiple layers and types of security protocols, different configurations, and customized APIs for interoperability—mean that an AI model can help simplify and improve problem detection.

Key Takeaways

• AI can improve the speed, accuracy, and applicability of anomaly detection.
• While traditional rules-based anomaly detection requires frequent updating, AI-powered anomaly detection can automatically adapt to new patterns and trends.
• When training GenAI on synthetic data, AI anomaly detection can highlight outliers, and those findings can form the foundation for the direction of further synthetically generated data sets.
• AI anomaly detection can also help with the validation of synthetic data sets used for training purposes.

AI Anomaly Detection Explained

Anomaly detection has its origins in statistics, particularly with industrial applications in manufacturing in the early 20th century. Cryptographers used manual anomaly detection to break codes by watching for unusual patterns or deviations from the expected statistical distribution of letters or symbols. As data became more abundant, computer-driven anomaly detection became the norm for areas such as fraud detection, inventory management, and quality control.

AI anomaly detection changes the process from a static set of statistical rules to a more flexible model trained to create a baseline for “normal.” By training on data, the AI model offers a more precise and refined definition of expected data—and the more data it processes, the more accurate it becomes. This allows for a model that better reflects the many facets of each application and shines for complex uses, such as monitoring Internet of Things devices and other systems that generate lots of data and where relationships among data points aren’t always obvious.

Generative AI offers an example of how AI anomaly detection can benefit a range of projects and services. Nearly all GenAI projects are feasible only because of breakthroughs in data collection, storage, and processing. While traditional rules-based anomaly detection can be applied to these projects, the precision and nuance to cleanly process such large volumes of data are often lacking. Thus, AI anomaly detection can be a necessity in data transformation and normalization of training data sources, spotting outliers, catching biases in data, and even assisting in generating synthetic data for algorithm training.

How AI Anomaly Detection Works

The AI anomaly detection process is very similar to the process for any AI model—data sourcing, training, and iteration are all required. The primary difference is in the specific objectives, as anomaly detection focuses on outliers. The general steps for AI anomaly detection are as follows:

1. Data collection and preprocessing: Like other AI projects, AI anomaly detection starts with setting a project direction. First, the big-picture goals of the project require definition: What’s the data’s normal state, and what’s the scope of the data to be considered? From there, teams should identify data sources for training and establish a repository for collecting and categorizing data—which records are anomalous and which aren’t? Organizations can simplify this process of creating training data by building in automated transformations between the source and the training repository.
2. Selection of an algorithm: Rather than just big-picture goals, teams can consider finer objectives and constraints to select features, identify attributes, and pick the best algorithm for a particular situation. For anomaly detection, statistical (regression), clustering (K-means), and neural networks provide an excellent fit. All algorithms used for AI anomaly detection attempt to categorize data records. The most successful often use neural networks that categorize data in complex ways. For instance, financial transactions might be classified as typical for an account or high value. They might then be categorized as originating from expected locations or a new location and might further be classified as typical spending or unusual. When two anomalous states occur in a single transaction, it might be fraudulent. When all three conditions exist, it’s probably fraudulent. Through training, AI can do this sort of analysis across many categories and apply weights to help decide when a transaction should be checked further.
3. Model training and selection: With those pieces in place, teams can begin training a model. Steps include establishing the architecture, choosing an available foundation model, preparing the training data, and running the training data through the model. Next, teams will use another data set to test the trained model to gauge how well it detects anomalies. Training continues until the model works as desired. Depending on the underlying algorithm, the model may continue to learn from live data to continue to improve over time.
4. Real-time versus batch processing detection: Depending on the application, AI anomaly detection may work in real time or process data in large batches. Real-time detection may require that significant compute and processing resources be dedicated to the process. In return, real-time detection produces results quickly, but those results may be less insightful and detailed than those delivered from the controlled environment of batch processing. Thus, real-time detection may trade spotting subtle anomalies for speed.

Benefits of AI Anomaly Detection

AI anomaly detection represents a significant leap forward compared with traditional rule-based anomaly detection. AI-based systems can require a more robust compute infrastructure, but they’ll typically perform much better. The following are some of the most common benefits of AI anomaly detection:

• Improved accuracy and precision: Properly trained neural networks develop thousands of categories or clusters and come to understand how membership in those clusters can indicate interrelated data. The result is an ability to spot data aberrations that would never be found with human-written rules. These models find more subtle anomalies and have the proper context to make judgments. All of this combines for a more accurate detection process.
• Scalability: Not only can AI handle large data sets—rules-based systems can do that too—but they can handle more complex data sets. In current algorithms for very large, very complex data sets, vector representations are created for each complex record. Then, similarity searches are used to find the nearest neighbors. If the vector’s nearest neighbors are in a cluster known to contain fraudulent records, it’s a safe bet the record under consideration is, too. This approach can scale to data sets with billions of records, each containing complex data.
• Real-time monitoring capabilities: The same approach that lets detection systems work at scale will also let them work at pace. Similarity searches can be performed very quickly, even on complicated data.
• Enhanced adaptability to changing patterns: Algorithms that create clusters of records can spot potential new anomalies by finding new record clusters. These clusters may represent new anomalies, but they may also simply represent a shift in what’s being recorded. For instance, say a product a store carries goes viral on social media; a new cluster will occur within the data. In this example, the anomaly is a happy one. Analysis of how it occurred might help a store or producer make it happen again. If the craze fades, the cluster may not grow. Again, that’s data to mine.
• Reduced false positives: A drift in what records show might represent a good thing for the business, as in the previous example. Or, static rules might categorize those new viral sales records as fraud. This sort of misclassification is less likely to happen with AI-based systems, which are typically looking at data relationships in a more nuanced way.

Key Techniques in AI Anomaly Detection

While AI anomaly detection models follow many of the same general steps and rules as other AI development projects, the process leans on specific methods that are well suited to the purpose. The following are key techniques used in AI anomaly detection:

1. Supervised versus unsupervised learning: Teams need to choose between supervised learning and unsupervised learning for AI anomaly detection. Both work. However, labeled anomalies in data sets tend to be rarer, and labeling data can be a slow and costly process. In most circumstances, unsupervised learning is preferred because it’s faster and less costly. The result will be an ability to cluster records and spot anomalies, but further training or programming will be needed to determine if the anomalies found are benign or represent some threat that must be managed.
2. Clustering-based methods: Clustering is a common AI technique that’s often used for anomaly detection because of the way models group data points based on overlapping traits and other notable similarities. By clustering data points, the process catches outliers to flag anomalies. Common clustering algorithms include K-means, Isolation Forest, and Gaussian mixed models. Vector representations of records are increasingly used for complex or high-dimensional records. Multimodal databases that support vector creation and processing can facilitate anomaly detection for complex data.
3. Neural networks: Neural networks can identify complex patterns and analyze nonlinear relationships, which help find outliers and anomalies. Some neural network architectures for AI anomaly detection are autoencoders, which can detect anomalies during the reconstruction phase, and generative adversarial networks, or GAN, which can use the generator/discriminator paradigm to identify outliers through the discriminator.
4. Time series anomaly detection techniques: For data recorded over time, the above techniques are useful, but the ideal selection depends on resources, objectives, data points, and other factors. For example, clustering algorithms can assess credit card transactions for fraud by grouping specific dimensions, such as geography, purchase category, and cost. In another example, a neural network could observe the relationships between output accuracy and usage cycles on complicated production machinery. By assessing relationships between various monitors on the machine, the neural network might identify that a combination of data points flags a need to perform earlier-than-usual maintenance.

Use Cases for AI Anomaly Detection

AI anomaly detection is helpful in a wide range of applications and industries. Factors to consider include whether your use case requires internal or external data or both and whether real-time detection is the goal.

Popular applications of anomaly detection include:

• Cybersecurity: With constant monitoring across all facets of an organization’s IT setup, anomaly detection can identify indicators that may signal an attack. In the case of a multicloud configuration, AI anomaly detection can provide significant help to IT teams that may be struggling with a range of overlapping systems and configurations. For multicloud setups, AI can automatically monitor the many details of operations that require expertise in each individual cloud’s operation.
• Finance: Fraud detection is one of the longest-running use cases for anomaly detection. Powering it with AI makes sense as financial data volumes grow. With AI anomaly detection, institutions get more accurate models that benefit from those large data sets. At the same time, financial scammers are constantly discovering new techniques, and AI allows for the model to evolve and become better at detecting new patterns by applying broader context.
• Healthcare: AI can analyze hospital data to help identify anomalies that may indicate inefficiencies, underutilized facilities, or fraud.
• IT operations: As corporate IT landscapes become increasingly complex and the scope of network monitoring increases, AI anomaly detection can supplement human experts and minimize “alert fatigue.” For example, AI might analyze application logs to identify anomalies that may indicate degraded performance, such as slow response times or errors.
• Manufacturing: AI anomaly detection systems for manufacturing can now evaluate significantly more data points in the ongoing effort to find and help address potential problems before they cause costly downtime, defects, or safety hazards. For example, abnormal temperature fluctuations in equipment could indicate overheating, while AI-powered cameras might detect defects in products before they’re shipped to customers.
• Municipal management and construction: Anomaly detection offers benefits for anyone who needs to monitor infrastructure, especially when leveraging drones. For municipalities, AI can analyze captured imagery of bridges, roads, and power lines to identify anomalies and enable proactive maintenance. Likewise, drones equipped with AI-powered anomaly detection can monitor project progress against models; identify safety hazards, such as missing equipment or incorrect material placement; and detect structural defects in buildings under construction.

Explore Anomaly Detection Using Oracle Cloud AI Service

AI services on Oracle Cloud Infrastructure (OCI) provide a suite of prebuilt tools, models, and features to integrate AI into applications and workflows, including features specific for anomaly detection in various configurations. OCI brings AI-powered learning and adaptability to monitoring, maintenance, and surveillance across industries and use cases.

The outlook for AI anomaly detection is exceptionally promising, thanks to the increasing volume and complexity of data across industries, plus the need for proactive identification of fraud and other issues. Expect to see more sophisticated AI models, including deep learning and unsupervised techniques, becoming more central to anomaly detection, thanks to their ability to detect subtle anomalies without access to extensive labeled data. Companies are also seeking improved real-time analysis, better integration with existing systems, and deployment of anomaly detection at the edge.

See OCI Anomaly Detection Service in Action

AI Anomaly Detection FAQs

What distinguishes AI anomaly detection from traditional methods?

Traditional anomaly detection is based on teams establishing rules and data thresholds. While this can produce results, it comes with significant limitations, such as failing to adapt to evolving industry changes and overlooking nonlinear relationships. AI anomaly detection is more adaptive to changing data and can consume more types of data.

How can businesses determine if they need AI anomaly detection?

AI anomaly detection is commonly more powerful, more accurate, and faster than traditional anomaly detection, making it a useful business tool for many organizations. However, there are circumstances where traditional methods will suffice. To determine the best fit, organizations should examine the complexity of their data, the strictness of their regulatory needs, and security risk factors. In addition, the scope of available resources needs consideration. For example, can the company support purchasing and refining a pretrained model, and does it have the compute and data resources to support the effort? Is the cloud the best option? In many cases, that’s the most cost-effective path.

What are the basic steps to start using AI anomaly detection?

The most basic steps to use AI anomaly detection include

• Determining the objective, data sources, and limitations
• Selecting the appropriate AI algorithm to fulfill objectives
• Training, preparing, and tuning the model, whether it’s pretrained or internally developed
• Launching the model with live data and continuously monitoring its output
• Evaluating results to see if it has started to uncover surprising trends