AI Anomaly Detection in Cybersecurity

How anomaly detection works

It starts with a baseline of normal behaviour. During a learning period, the system watches things like:

• when users usually sign in
• which servers talk to each other
• how much data a service usually sends
• which processes run on a client

It then scores each new event by how far it strays from that pattern.

Common methods are:

• Statistical models: They spot changes from averages and seasonal patterns, for example an odd number of failed logins at the weekend.
• Unsupervised learning: Methods such as clustering, isolation forests or autoencoders find data points that do not fit any known group.
• Peer group analysis: An account is compared with similar accounts. One example is an accounts clerk who suddenly opens development servers.
• Sequence analysis: It spots odd chains of events, such as a sign-in, then higher rights, then mass access to file shares, all within a few minutes.

You find these methods in user and entity behaviour analytics (UEBA), network detection and response (NDR), EDR/XDR products and modern SIEM platforms.

Typical use cases

• Spotting account takeover: sign-ins from a new country, impossible travel, new devices, odd mailbox rules in Microsoft 365.
• Lateral movement: a client that suddenly connects to many other systems over SMB or RDP.
• Data theft: unusual upload volumes to cloud storage, or DNS tunnelling.
• Early signs of ransomware: many files being renamed or encrypted at once.
• Misuse of AI services: spikes in tokens and costs on AI APIs, or odd query patterns that point to model extraction or stolen API keys.

Limits and risks

Anomaly detection does not run on its own. Not every anomaly is an attack, and not every attack is an anomaly:

• False positives: Year-end closing, waves of remote work or new software cause normal changes. Without tuning, the team drowns in alerts.
• Concept drift: Normal behaviour keeps changing. Models need retraining, and you must keep an eye on their quality.
• Poisoned baseline: If an attacker is already active during the learning phase, their behaviour counts as normal. An attacker who moves slowly, step by step, can also stay below the detection threshold.
• Explainability: A risk score without a reason does not help analysts much. Make sure the tool explains which features set off an alert.
• Data protection: Analysing staff behaviour means handling personal data. Purpose limitation, data minimisation and retention periods apply. You usually also need a data protection impact assessment under Art. 35 GDPR. In German companies with a works council, technical monitoring systems generally need its co-determination.

Rolling it out in practice

1. Define data sources: Identities (Entra ID, Active Directory), endpoints, firewall or proxy, cloud and AI services. The quality and completeness of logs matter more than their volume.
2. Pick the key use cases: Start with a few clearly described cases, such as account takeover and data theft.
3. Guide the learning phase: Build the baseline with care. Write down known exceptions and leave them out.
4. Combine with rules: Anomaly detection adds to signature- and rule-based detection. It does not replace it.
5. Define the response: For each type of alert, decide who looks into it and what happens next. At best, this runs around the clock, for example through a managed SOC.
6. Measure what it achieves: Track the share of confirmed alerts, the time to detection and the results of regular attack simulations.

For organisations covered by NIS2, good attack detection also helps meet the short reporting deadlines. You can only report what you have detected.

Conclusion

AI-based anomaly detection helps you find unknown attacks and misused accounts sooner. But the data, the tuning and the response process must be right. SecTepe runs anomaly detection as part of its managed SOC and helps you choose the right data sources.

Frequently asked questions

How long does the learning phase take?

That depends on the product and the setting. A few weeks is common, so that repeating patterns such as month-end closing are captured. After that, the model keeps learning all the time.

Can anomaly detection also monitor AI systems themselves?

Yes. The same methods can spot odd usage patterns in AI apps, such as sudden cost spikes or large numbers of similar requests. For this, usage has to be logged.