Artificial intelligence (AI) has become an integral part of IT security. It recognizes anomalies, assesses risks and reacts automatically to threats. However, it often remains unclear why a decision was made. This is precisely where the concept of Explainable AI (XAI) comes in: It makes AI decisions comprehensible, verifiable and trustworthy – a decisive factor for use in safety-critical areas.
Why is explainability so important?
- Create trust
Security managers need to understand why a system triggers a certain action – such as blocking access or blocking a data packet.
- Compliance & traceability
Regulatory requirements such as GDPR or NIS2 demand that security-related decisions are documented and traceable.
- Recognize and correct errors
If an AI reacts incorrectly, it must be clear where the error lies – this is the only way to improve systems and minimize risks.
- Acceptance within the company
Employees are more likely to accept AI-supported systems if they understand how decisions are made.
How does Explainable AI work?
Explainable AI uses various methods to make decisions understandable:
- Feature analysis: Which features contributed to the decision?
- Visualizations: Graphical representations of decision paths or risk assessments.
- Comparison with known patterns: Shows how similar an incident is to previous attacks.
- Natural language explanations: AI provides an understandable explanation in text form.
Practical examples in IT security
Anomaly detection: Instead of just reporting “suspicious”, the AI explains why a behavior is unusual.
Access management: AI justifies why access was denied or approved – e.g. based on location, time or role profile.
SIEM systems: Modern security platforms integrate XAI to better classify and prioritize incidents.
Conclusion: transparency is security
Explainable AI is not a “nice-to-have”, but a central component of modern IT security. Only those who understand how AI makes decisions can trust it – and use it in a targeted manner.
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