Myth #5: Explainable AI Makes Cybersecurity Less Accurate

𝘙𝘦𝘢𝘭𝘪𝘵𝘺: 𝘌𝘹𝘱𝘭𝘢𝘪𝘯𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘣𝘶𝘪𝘭𝘥𝘴 𝘵𝘳𝘶𝘴𝘵 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘴𝘢𝘤𝘳𝘪𝘧𝘪𝘤𝘪𝘯𝘨 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦.

A common misconception is that if an AI system explains why it generated an alert, it must be compromising speed or detection accuracy. In reality, explainability makes AI more useful - not less.

Cybersecurity is about more than detecting threats. Security analysts need to understand why an incident was flagged, what evidence supports the recommendation, and how it may impact critical systems before taking action.

Within the AIAGENT4CYBER project, we are exploring how Knowledge Graphs, Multi-Agent AI, and Large Language Models (LLMs) can provide transparent, evidence-based cybersecurity decision support. Instead of returning only a risk score, AI can explain:

- Which vulnerability triggered the alert 

- Which assets are affected 

- Whether the vulnerability is actively exploited 

- The potential attack path through the network 

- Why the incident has been prioritized 

This contextual reasoning helps analysts validate AI recommendations, reduce false positives, and make faster, more confident decisions. Explainability is also becoming increasingly important for trustworthy AI, supporting transparency, accountability, regulatory compliance, and human oversight in high-impact cybersecurity environments. The future of cybersecurity is not about choosing between accuracy and explainability. It is about combining both to create AI systems that security professionals can understand, trust, and confidently use.