Myth #7: AI Can Understand Cybersecurity Without Context

Myth: AI Can Understand Cybersecurity Without Context

Reality: AI is only as good as the context it receives.

A common misconception is that Artificial Intelligence can automatically understand cyber threats simply by processing large volumes of security data. In reality, more data does not necessarily mean better cybersecurity intelligence. Without sufficient context, even sophisticated AI systems may struggle to distinguish routine activity from genuine threats.

Modern cybersecurity environments generate enormous volumes of heterogeneous and fragmented information. Security-relevant data may originate from SIEM platforms, IDS/IPS, endpoints, cloud services, IoT and IoMT devices, identity systems, vulnerability scanners, asset inventories, and cyber threat intelligence platforms. These systems often operate independently, leaving important relationships hidden across multiple data sources.

Consider a security alert indicating unusual communication from a device. By itself, the event may appear relatively insignificant. But what if the AI also knows that:

• The device is a critical IoMT patient-monitoring system

• It contains a known vulnerability (CVE)

• The vulnerability has a high EPSS exploitation probability

• Threat intelligence indicates that the vulnerability is actively exploited

• The device communicates with a critical clinical server

• The observed behavior corresponds to a MITRE ATT&CK technique

• The compromised device creates a potential lateral-movement path toward sensitive healthcare systems

Suddenly, an isolated event becomes part of a much more significant cybersecurity incident. Within the AIAGENT4CYBER project, we are exploring how Knowledge Graphs, Multi-Agent AI, Graph Neural Networks (GNNs), and Large Language Models (LLMs) can bring this fragmented information together and provide AI systems with the context required for more meaningful cybersecurity reasoning.

A Cybersecurity Knowledge Graph can connect entities such as:

• Devices and digital assets

• Users and identities

• Vulnerabilities and CVEs

• CVSS and EPSS information

• Software and dependencies

• Network communications

• Threat actors and indicators

• MITRE ATT&CK tactics and techniques

• Security events and historical incidents

• Attack paths

• Business and asset criticality

  
  

Instead of asking an AI system to interpret each alert independently, the Knowledge Graph provides a connected representation of the cybersecurity environment.

This enables AI agents to ask more meaningful questions:

What happened?

Which security events indicate suspicious activity?

Where did it happen?

Which devices, users, applications, or services are affected?

Why does it matter?

Are the affected assets critical to business or healthcare operations?

How are the events connected?

Do apparently unrelated alerts belong to the same attack sequence?

What could happen next?

Is there a feasible attack path toward another critical asset?

What should the analyst investigate first?

Which incident represents the greatest contextual risk?

This is particularly important for Multi-Agent AI. Specialized agents may independently analyze vulnerabilities, network traffic, IoMT behavior, threat intelligence, identities, and attack patterns. A shared Knowledge Graph can provide a common contextual layer through which these agents exchange evidence and coordinate their reasoning.

The result is a shift from:

Data → Alert → Analyst

toward:

Data → Context → Relationships → AI Reasoning → Explainable Cyber Intelligence → Human Decision

Context can also improve explainability. Instead of simply reporting:

“Anomalous activity detected — Risk: High.”

a context-aware system could explain:

“High-risk activity was identified because an Internet-connected IoMT device with a known exploitable vulnerability initiated anomalous communication. Threat intelligence indicates active exploitation, and Knowledge Graph analysis identifies a potential attack path toward a critical clinical system. Immediate investigation is recommended.”

This gives cybersecurity professionals something much more useful than a prediction: evidence, relationships, risk context, and an understandable basis for action.

The future of AI-powered cybersecurity is therefore not simply about processing more logs or deploying larger AI models. It is about giving AI the right context to understand how vulnerabilities, assets, users, threats, and security events are connected.

Context transforms isolated security data into connected knowledge—and connected knowledge into actionable cyber intelligence.