With the amount of data being generated daily across industries, organizations are becoming overwhelmed. For companies, this data holds the key to better performance, less risk, and fewer disruptions. But how can organizations find the information they need from this mountain of data to help teams function better? The answer lies in anomalies.

While traditional anomaly detection — reviewing datasets to identify actions, events, or patterns that fall outside the normal range of activity or behavior — was done manually via statistical and mathematical calculations, the explosion of data in recent years has made it impossible for humans to perform such tasks.

Modern anomaly detection efforts employ algorithms that use machine learning (ML), AI, or both to analyze massive quantities of data and identify complex and subtle anomalies that would be difficult, if not impossible, to find using traditional methods.

What is AI anomaly detection?

AI anomaly detection employs the use of AI/ML algorithms and models to identify unusual behaviors, events, or patterns that diverge from normal or expected baselines. These outlier events often signal potential risks — such as fraud, errors, or cyberattacks — that require deeper investigation.

AI anomaly detection is crucial for maintaining the integrity of critical information and systems, especially for organizations that maintain sensitive customer and organizational data or intellectual property (IP).

Integrating AI-driven anomaly detection into business operations can enhance accuracy, scalability, and adaptability, helping organizations strengthen security, lower risk, and reduce downtime.

Use cases for AI anomaly detection in cybersecurity

In cybersecurity, anomalous events or behaviors can be an early warning sign of a malicious event, such as a data breach, cyberattack, or system failure. By quickly identifying these anomalies, organizations can better contain security risks, thereby minimizing damages and expediting recovery.

Integrating AI-enabled anomaly detection into a comprehensive cybersecurity strategy enhances an organization's ability to protect sensitive data and systems from malicious attacks, proactively address threats, and maintain the integrity of critical information and systems.

Here, we explore two main use cases for AI anomaly detection in cybersecurity:

Threat detection and response

AI-enabled extended detection and response (XDR) collects and analyzes threat data from various security tools as well as endpoints, cloud workloads, network email, and more. AI-powered algorithms can review this combined dataset to identify unusual network, endpoint, or user activity that may indicate malicious activity.

In addition to alerting security teams of the need for investigation, some AI systems may offer resolution recommendations or contextual data to assist with remediation decisions.

Behavioral analytics

AI-enabled behavioral analytics — the study of user behavior within networks or applications — is another core use case for AI anomaly detection in cybersecurity. As with AI-enabled threat detection and response tools, AI-powered behavioral analytics aims to identify unusual or suspicious activity that may signify a security threat.

Organizations often employ behavioral analytics to detect insider threats, credential misuse, and lateral movement. Traditional tools and methods struggle to detect threats like this because they are designed to identify suspicious activity outside the organization as opposed to monitoring and analyzing the behavior of approved users.

Benefits of AI anomaly detection in cybersecurity 

Proactive threat mitigation

AI anomaly detection identifies potential issues early, allowing organizations to address them before they escalate. This helps minimize disruptions, mitigate risks, and prevent damage to operations or reputation.

Scalability and efficiency

AI-enabled anomaly detection platforms can process petabyte-scale datasets in near real time, delivering actionable insights at the speed of business operations. This is especially critical as data volumes continue to grow and manual analysis becomes increasingly difficult.

Adaptability

AI-powered systems continuously learn from new data, enhancing their ability to detect anomalies over time. This adaptability helps ensure organizations can defend against evolving threats in a dynamic environment.

Cost savings

By dynamically performing anomaly detection, AI reduces the need for manual monitoring and analysis, cutting operational costs. Additionally, it helps prevent significant financial losses and operational impact by catching issues before they impact business processes or erode customer trust.

How the Falcon platform can help with AI-powered anomaly detection

AI anomaly detection can help organizations across industries harness the power of their data to reduce risk, improve efficiency, and enhance decision-making.

When it comes to cybersecurity, organizations need to leverage AI-driven insights to identify irregularities in real time. The CrowdStrike Falcon® platform applies advanced ML and behavioral analytics to detect anomalies across endpoints, identities, and workloads, automatically correlating signals to surface threats faster and with greater precision. The platform’s key capabilities include:

• Advanced ML: The Falcon platform leverages ML models that are trained on trillions of daily security events, establishing baselines for normal behavior to effectively detect deviations.
• Behavioral Analysis: The Falcon platform employs AI-powered insights to monitor user and entity behavior, identifying suspicious activity patterns in real time.
• Rapid Threat Response: The Falcon platform provides immediate alerts and remediation options, enabling swift action to mitigate risks and minimize potential damage.
• Adaptability to Emerging Threats: The Falcon platform constantly learns and evolves with new data, ensuring continued protection against evolving cyberattack techniques.
• Comprehensive Data Integration: The Falcon platform integrates endpoint, identity, cloud workload, and telemetry data into a unified AI-native platform for end-to-end anomaly detection and response.

Through these capabilities, CrowdStrike empowers organizations to maintain a strong security posture, ensuring effective anomaly detection and protection in today’s dynamic threat landscape.

For more information about how CrowdStrike leverages advanced AI capabilities to provide the industry’s most complete AI-native defense, please visit our Falcon platform AI-native protection page.