Use centralized LLM log data to power real-time threat detection and rapid incident response. Identify risks like PII leakage, jailbreaks, and policy violations across all AI usage.
With normalized logs from all major LLMs and AI providers, you can write a single detection rule for threats like PII exposure, and it will apply across your entire AI ecosystem.
FireTail monitors logs continuously and flags threats such as prompt injection, jailbreaking, misuse of AI tools, and abnormal activity, instantly alerting security teams.
Security teams can investigate LLM-related incidents using rich, structured logs with full context; who prompted, what was sent, and what the model responded with.
Based on detection rules and defined guardrails, FireTail can trigger automated responses like blocking future use, revoking access, or flagging users for follow-up action.
Security Architect @ Global Enterprise
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Organizations using multiple LLMs often lack unified detection and alerting. Each provider’s logs are in different formats, with different data elements, making it hard to detect threats like PII exposure or jailbreaks quickly and consistently.
FireTail normalizes logs from all AI providers into a single schema and log stream, enabling consistent threat detection logic. Whether it’s prompt manipulation, data leaks, or rogue behavior, one rule can find it all.
Centralizing detection and response reduces risk, speeds time-to-containment, and gives security teams confidence that no AI usage is falling through the cracks.
