As 5G networks rapidly scale across industries, their flexibility becomes a double-edged sword. One of the most innovative features—network slicing—enables operators to segment infrastructure into virtual networks tailored for enterprises, consumers, and billions of IoT devices. But with that agility comes risk: misconfigurations, lateral movement, and hidden anomalies can propagate quietly across slices, bypassing traditional security controls.

Given the rise of AI assistants, can we apply them to the realm of 5G? What if your AI assistant could not just read logs, but understand them? Not just see dashboards, but interpret them?

The Problem: Complexity at Scale

Security analysts face fragmented telemetry: logs from the core (AMF, SMF, UPF), YAML configs for slice definitions, dashboards showing QoS metrics, and endless alerts. Stitching this together is like defusing a bomb in the dark—while the clock ticks across multiple layers of the network.

Using Multimodal AI to perform Threat Analysis

Any multi-modal AI model has the capability to perform reasoning, making sense of logs, screenshots, config files, and natural language queries in one cohesive frame.

Here’s what that looks like in a 5G environment:

• Log Intelligence: LLMs reads structured and unstructured logs to detect policy drift, failed authentications, or insecure slice configurations.

• Config Auditing: It parses YAML/JSON policies to flag QoS mismatches, excessive access, or unintended lateral movement vectors.

• Visual Analysis: A screenshot from your OSS/BSS dashboard can be interpreted based on process trees, red flags KPI spikes, and identifies service degradation—just like a human would.

• Analyst Copilot: Ask in plain English: “Why is slice 3003 overloading?” The LLM model should respond with a root cause, a timeline, and remediation steps, all mapped to MITRE ATT&CK.

Sample Output:

“Slice 3003 shows excessive uplink usage due to a misapplied QoS policy update pushed via SMF. Traffic surge traced to IoT segment Z. Suggest rollback to baseline config v1.14 and apply rate-limiting to affected UE group.”

This AI-enhanced threat detection model can be integrated directly into your SOC stack—with LLM powering a chat-based interface, reading from your SIEM/SOAR, and even interpreting your dashboard screenshots.

If you’re an operator, CSP, or security leader staring down the complexity of 5G multi-modal LLMs can help you unpack, interpret and efficiently analyse and report Threats in your 5G network.

Let’s stop treating 5G security like a fire drill. With the right AI, it becomes a science.