Detection and response for the AI you've put in production. Behavior monitoring on agents, prompt-injection signals, exfiltration patterns through model APIs. Agentic triage that augments your SOC — or your sister firm Baseline IT's, if that's where your traditional SOC lives.
Traditional SOC tooling watches network ports, EDR processes, and authentication events. An LLM agent is invisible to that stack: its actions are tool calls, its inputs are prompts, its decisions live inside model context windows. Without AI-aware detection, the abuse patterns are silent — prompt injection succeeds, tool authority gets escalated, training data exfiltrates through clever queries, and the SIEM shows nothing. AI defense closes that gap: telemetry on the agent and model layer, signals tuned to AI-specific abuse, and triage built for the volume.
Not a vendor feature list — the capabilities you actually need so an AI system in production isn't a blind spot in your monitoring.
// AI defense coverage — every scope
// six-of-six is the baseline. below that, your AI runtime is unwatched.
From inventory to instrumentation to handoff. We build the AI-defense layer that plugs into your existing SOC — yours or your sister firm's.
We catalog the AI surface: agents, models, RAG pipelines, grounding sources, tool inventories, exposure paths. Threat model each system on its actual attack surface, not a generic risk template. Output: a prioritized monitoring target list and a detection plan.
We build the detection layer — behavior signals, prompt-injection classifiers, tool-call anomaly detection, exfiltration patterns — and wire it into your existing SIEM/EDR/SOAR. Read-only at the start; alerting once the false-positive rate is under control. Every signal is documented and tunable.
We can co-monitor with your team for the first 90 days, then hand the runbooks over — or stay as an embedded AI-defense desk feeding into your SOC. Quarterly reviews: what the signals got right, what they missed, what changed. No black-box monitoring.
Free initial scoping — 30 minutes to look at your AI surface, your current monitoring stack, and where the highest-risk blind spots are.