AI-BOM + Session Evidence
Detection & Investigation Engine
Use asset inventory, behavioral baselines, and D01-D06 signals to locate anomalies, then follow the session trace across identity, data, tools, and egress impact.
AI-BOM Asset Graph
Auto-discover models, toolchains, MCP, and Agent Runtime dependencies with version diff and compliance inventory.
4
Models
claude-4-sonnet
gpt-4o
deepseek-r1
qwen-2.5
23
Tools
ReadFile
EditFile
BashExec
WebFetch
+19
3
MCP Servers
github-mcp
jira-mcp
slack-mcp
5
Agent Runtimes
cursor-agent
cli-agent
custom-bot
+2
Initial Detection Rules D01-D06
Behavioral baseline drift detection that catches anomalies before incidents.
New Tool First Seen
Unknown tool call beyond baseline
Depends on: ToolCall
New Model First Seen
Unknown model load beyond baseline
Depends on: ModelLoad
New MCP Endpoint
Previously unseen MCP service connection
Depends on: MCPActivity
Sensitive Path Anomaly
Sensitive file access spike or abnormal combination
Depends on: FileAccess + ToolCall
Permission Oscillation
High-frequency deny→allow switching, suspected bypass
Depends on: PermissionDecision
Call Intensity Spike
Abnormal tool call frequency surge in a single session
Depends on: ToolCall/ToolResult
From Detection Signal to Actionable Investigation
The investigation path separates factual evidence, affected scope, and response options that are actually available.
D04 Sensitive Path Anomaly
Credential-file access combined with a new egress target
Session Trace
Identity -> ReadFile -> BashExec -> unknown.io
Scope Confirmed
1 identity, 2 files, 1 endpoint
Execute by Availability
Audit is available; other controls ship after gates pass
Baseline + Drift
Build call distribution, latency, rejection-rate, and outbound-pattern baselines per tenant/project/agent, then roll them forward after a configurable controlled learning period.
Configurable
Learning Period
Rolling
Update Frequency
tenant/agent/env
Granularity