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Introduction:
Traditional application logs, endpoint logs, network logs, and identity logs were designed for human-driven workflows, not autonomous AI agents. When an AI agent operates, security teams need four critical data points in a single record: the action attempted (not just the completed result), the policy verdict and which rule applied, the behavioral trajectory leading to that action, and a live risk score updated in real time. Without these, stitching together incidents from multiple sources post-factum isn’t governance—it’s archaeology. Runtime Governance Telemetry (RGT) emerges as a new log category specifically built to govern autonomous agents in real time.
Learning Objectives:
- Identify the gaps in conventional SIEM and EDR logging for AI agent behavior monitoring
- Implement a basic Runtime Governance Telemetry collector to capture action attempts, policy verdicts, trajectories, and risk scores
- Use Linux/Windows commands and open-source tools to generate, forward, and analyze RGT data for real-time policy enforcement
You Should Know:
- What Is Runtime Governance Telemetry and Why Do You Need It?
Runtime Governance Telemetry is a structured log format containing four mandatory fields: `action_attempted` (the raw API call or command before execution), `policy_verdict` (allow/deny + rule ID), `behavioral_trajectory` (sequence of previous actions leading to this moment), and `live_risk_score` (0–100 dynamic score). Unlike traditional logs that record what completed, RGT records what was tried and the decision in real time.
Step-by-step guide to define an RGT schema:
1. Create a JSON schema file `rgt_schema.json`:
{
"type": "object",
"properties": {
"timestamp": {"type": "string", "format": "date-time"},
"agent_id": {"type": "string"},
"action_attempted": {"type": "object"},
"policy_verdict": {"type": "string", "enum": ["ALLOW", "DENY", "CHALLENGE"]},
"policy_rule_id": {"type": "string"},
"behavioral_trajectory": {"type": "array", "items": {"type": "object"}},
"live_risk_score": {"type": "integer", "minimum": 0, "maximum": 100}
},
"required": ["action_attempted", "policy_verdict", "behavioral_trajectory", "live_risk_score"]
}
2. Validate incoming agent telemetry against this schema using `jq` (Linux) or PowerShell (Windows).
3. Forward validated RGT events to a dedicated index in your SIEM (e.g., `index=rgt_events` in Splunk).
- Why Your SIEM and EDR Fail at Agent Governance
Conventional logs lack the four RGT fields. For example, a typical Linux audit.log records a completed `execve` syscall but not the policy verdict or risk trajectory. Windows Security Event 4688 logs process creation but doesn’t include live risk scores. Stitching these from four sources after an incident requires hours of correlation—unsuitable for real-time agent governance.
Linux commands to expose the gap:
Show a completed action without policy verdict
sudo ausearch -m execve -ts recent | head -20
Compare with what's missing: no trajectory, no risk score
To simulate RGT, you'd need to enrich manually:
sudo ausearch -m execve --format json | jq '. + {policy_verdict: "UNKNOWN", live_risk_score: null}'
Windows PowerShell to demonstrate missing fields:
Get last 5 process creation events (Event ID 4688)
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4688} -MaxEvents 5 | ForEach-Object {
$_.Properties | Select-Object -Property Value
}
Observe no behavioral_trajectory or live_risk_score
3. Implementing a Basic Runtime Governance Telemetry Collector
To generate RGT data, intercept agent API calls before execution using a proxy or eBPF hook, evaluate policy, compute risk, and emit the record.
Python collector example (Linux):
import json, time, uuid
from datetime import datetime
class RGTCollector:
def <strong>init</strong>(self):
self.trajectory = [] stores last N actions
self.risk_score = 50 baseline
def evaluate_policy(self, action):
Simple rule: block file deletion outside /tmp
if "unlink" in action.get("syscall", "") and not action["path"].startswith("/tmp"):
return "DENY", "RULE_FILE_DELETE_BLOCK"
return "ALLOW", "RULE_DEFAULT"
def compute_risk(self, action):
Increase risk if trajectory shows rapid syscalls
if len(self.trajectory) > 5 and time.time() - self.trajectory[-1]["timestamp"] < 0.1:
self.risk_score = min(100, self.risk_score + 10)
else:
self.risk_score = max(0, self.risk_score - 2)
return self.risk_score
def capture(self, action):
verdict, rule = self.evaluate_policy(action)
risk = self.compute_risk(action)
record = {
"timestamp": datetime.utcnow().isoformat(),
"agent_id": str(uuid.uuid4()),
"action_attempted": action,
"policy_verdict": verdict,
"policy_rule_id": rule,
"behavioral_trajectory": self.trajectory[-5:], last 5 actions
"live_risk_score": risk
}
self.trajectory.append({"timestamp": time.time(), action})
return record
Usage: intercept agent syscalls (e.g., via LD_PRELOAD or eBPF) and call capture()
collector = RGTCollector()
sample_action = {"syscall": "unlink", "path": "/etc/passwd"}
print(json.dumps(collector.capture(sample_action), indent=2))
- Linux Commands to Monitor AI Agent Activity in Real Time
Use `strace` to trace agent process syscalls and pipe into the RGT collector.
Step-by-step:
1. Identify the agent’s PID: `pgrep -f “ai_agent”`
2. Trace syscalls and output JSON:
sudo strace -f -e trace=file,process -p <PID> -o /dev/stdout | while read line; do
Parse syscall into JSON and feed to collector.py
echo "$line" | python3 -c "import sys, json; print(json.dumps({'raw': sys.stdin.read()}))" | python3 collector.py
done
3. Forward RGT records to a remote syslog server:
nc -u your-siem-host 514 < rgt_events.log
4. For eBPF-based interception (lower overhead), use `bpftrace`:
sudo bpftrace -e 'tracepoint:syscalls:sys_enter_openat { printf("{\"action_attempted\":\"openat\",\"path\":\"%s\"}\n", str(args->filename)); }' | python3 collector.py
5. Windows Commands and PowerShell for Agent Telemetry
On Windows, use Event Tracing for Windows (ETW) and PowerShell to capture agent actions before completion.
Step-by-step:
1. Enable ETW for process and file operations:
logman create trace AgentTrace -p "Microsoft-Windows-Kernel-Process" -p "Microsoft-Windows-Kernel-File" -o .\agent.etl -ets logman start AgentTrace -ets
2. Consume ETW events in real time and emit RGT:
Use PowerShell to listen for process creation (Event ID 1 from Microsoft-Windows-Sysmon)
Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; ID=1} -MaxEvents 1 -Oldest | ForEach-Object {
$action = @{
command = $<em>.Properties[bash].Value
pid = $</em>.Properties[bash].Value
}
Call Python RGT collector
$actionJson = $action | ConvertTo-Json
& python3 collector.py $actionJson
}
3. Integrate with Windows Defender ATP APIs to retrieve live risk score:
$risk = Invoke-RestMethod -Uri "https://api.security.microsoft.com/api/machines/<MachineId>/risk" -Headers @{Authorization="Bearer $token"}
Inject into RGT record
- Integrating Runtime Governance Telemetry with SIEM and SOAR
Forward RGT events to Splunk, ELK, or Sentinel for real-time dashboards and automated playbooks.
Splunk HTTP Event Collector (HEC) configuration:
curl -k "https://splunk-host:8088/services/collector" -H "Authorization: Splunk <token>" -d '{
"sourcetype": "rgt:telemetry",
"event": {
"action_attempted": "rm /data/db",
"policy_verdict": "DENY",
"policy_rule_id": "RULE_DB_DELETE_PROTECT",
"behavioral_trajectory": [{"cmd": "ls", "ts": "2025-04-01T10:00:01Z"}],
"live_risk_score": 78
}
}'
Elasticsearch ingest pipeline to enrich RGT:
PUT _ingest/pipeline/rgt_enrich
{
"processors": [
{"set": {"field": "risk_level", "value": "{{live_risk_score}}", "override": true}},
{"script": {"source": "if(ctx.live_risk_score > 70) ctx.policy_verdict = 'DENY';"}}
]
}
SOAR playbook trigger (e.g., TheHive):
- When `policy_verdict = “DENY”` and
live_risk_score > 85, automatically isolate the agent’s container:docker stop <agent_container_id> && docker rm <agent_container_id>
- Real-Time Risk Scoring and Policy Enforcement Using Open Policy Agent (OPA)
Deploy OPA as a sidecar to evaluate RGT telemetry and enforce micro-decisions.
OPA policy example (rego):
package agent.rgt
default allow = false
allow {
input.live_risk_score < 50
input.policy_verdict == "ALLOW"
count(input.behavioral_trajectory) < 10 not too many steps
}
deny_reason = "high_risk" {
input.live_risk_score >= 70
}
Step-by-step:
- Run OPA server: `opa run –server –addr localhost:8181`
- Load policy: `opa eval –data policy.rego “data.agent.rgt.allow” –input rgt_event.json`
- From Python collector, query OPA before final verdict:
import requests response = requests.post("http://localhost:8181/v1/data/agent/rgt/allow", json={"input": record}) if not response.json()["result"]: record["policy_verdict"] = "DENY"
What Undercode Say:
- Real-time governance requires a new log paradigm – stitching after incidents is archaeology, not governance. Autonomous agents demand action attempts, policy verdicts, behavioral trajectories, and live risk scores in a single record.
- Existing SIEM/EDR tools are blind to agent intent – they log outcomes, not the decision process. Adding RGT as a dedicated log source transforms security from forensic to proactive.
- Open-source and built-in OS tools can implement RGT today – using eBPF, ETW, strace, and PowerShell, teams can build lightweight collectors without waiting for vendors. OPA enables dynamic policy evaluation.
- Live risk scoring must be context-aware – behavioral trajectory length, action frequency, and deviation from baseline directly influence risk. A simple moving average or anomaly detection (e.g., using
scipy.stats.zscore) can be embedded. - Cloud hardening for agent APIs – for LLM agents calling external APIs, RGT should include request/response hashes and token usage. Use AWS CloudTrail or Azure Monitor to capture attempted invocations before execution.
- vulnerability mitigation via dynamic policy – if an agent attempts a known vulnerable command (e.g.,
curl | bash), RGT can instant deny and escalate to SOAR. Pair with CVE feeds using `jq` to check `action_attempted` against exploit database. - Adversarial testing of your RGT pipeline – simulate a malicious agent that rapidly escalates privileges; ensure your collector captures all action attempts even under load. Use `stress` (Linux) or `burpsuite` (API fuzzing) to validate.
- Training courses for security teams – existing SOC training on log analysis must evolve to include RGT. Recommend modules: “Building an RGT Collector in Python,” “eBPF for Agent Telemetry,” and “OPA Policies for AI Agents.”
- Compliance implications – regulations like EU AI Act will likely mandate runtime governance for high-risk AI systems. RGT provides immutable audit trails of agent decisions, supporting explainability and accountability.
- The future is real-time autonomous governance – just as EDR revolutionized endpoint security, Runtime Governance Telemetry will become the cornerstone of AI agent security, shifting left to the moment of attempted action.
Prediction:
Within 18 months, major cloud providers will introduce native Runtime Governance Telemetry pipelines for their AI agent services (e.g., AWS Bedrock Agents, Azure AI Agent Service). SIEM vendors will add dedicated RGT parsers and dashboards, while open standards like OpenTelemetry will extend their logs specification to include the four mandatory fields. Security teams that adopt RGT early will reduce mean time to detect (MTTD) for rogue agent behavior from hours to milliseconds, turning agent governance from “archaeology” into “real-time radar.” Regulatory bodies will reference RGT in upcoming AI security frameworks, making it a compliance requirement for any autonomous system interacting with production data or critical infrastructure.
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Reported By: David A – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
