How IT Professionals Risk Leaking Confidential Data When Using ChatGPT and Other LLMs
Artificial intelligence has fundamentally changed how IT professionals work. Network engineers, security specialists, system administrators, DevOps engineers and SOC analysts now routinely rely on ChatGPT, Claude and Gemini to parse configuration files, locate errors, explain unexpected behavior and draft scripts.
The time savings are substantial, yet a critical question is often overlooked: exactly what data is being sent to these cloud-based services?
The human factor behind data exposure
The issue is not limited to junior staff. Both experienced engineers and newcomers face the same pressure to resolve problems quickly. In almost every security incident, the root cause is the desire to bypass repetitive manual work rather than any sophisticated attacker technique.
When an engineer pastes a multi-thousand-line log into an LLM with the request to “find anomalies and successful logins,” internal IP addresses, server names, domain information, employee email addresses and authentication tokens travel to the provider’s infrastructure.
Network configuration files present even greater risk
The same pattern occurs with device configurations. Engineers commonly upload running-config files from Cisco, FortiGate and Palo Alto devices to accelerate troubleshooting of ACLs or routing issues. Although passwords may be hashed, the files still disclose:
- Internal and external IP addressing schemes
- VLAN and DMZ topology
- VPN peer addresses and encryption parameters
- Hostnames, interface descriptions and branch names
- SNMP community strings and LDAP server locations
For government agencies, banks and healthcare providers, this information constitutes a direct reconnaissance vector.
Storage and access guarantees remain uncertain
Many users treat conversations with large language models as private notes. In reality, no provider offers absolute assurances regarding long-term storage, internal access by employees or subsequent use of the data. On 28 July 2026 the Malwarebytes research team published an analysis of real incidents triggered by the platform’s “Share” feature, confirming that sensitive corporate data had left organizational boundaries.
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