Whitepaper: AI Governance

Continuous Compliance for Autonomous Agents

A framework for real-time telemetry ingestion and drift detection in non-deterministic AI systems using Splunk Observability.

1. The Non-Deterministic Challenge

Traditional software is deterministic: Input A always yields Output B. Autonomous AI agents, however, are probabilistic. They "drift." As we deploy LLM-based agents into critical infrastructure (see aiops-substrate), ensuring they remain within safety guardrails is not just a nice-to-have—it's a requirement for sovereign operation.

[CRITICAL_WARNING]: Feature-vector drift can lead to unauthorized API execution. Static policies are insufficient.

We solve this by treating Agent "Thoughts" as loggable events. By binding a Splunk Technology Add-on (TA) directly to the agent's runtime environment (TA-asset-identity-framework), we create a "Sidecar Observability" pattern.

2. Architecture: The Telemetry Pipeline

Fig 2.1: The Sidecar TA pattern for real-time agent telemetry ingestion.

The architecture consists of three core components:

Agent Nodes: The execution layer where the LLM performs tasks.
Universal Forwarder (Sidecar): Captures stdin/stdout and internal trace logs. It sanitizes sensitive data before transmission.
Splunk Core: The aggregation layer where our TA-suhlabs-eMASS app correlates agent actions against defined compliance controls.

3. Methodology: Vector Space Drift Detection

How do we know if an agent is "thinking" dangerously? We extract feature vectors from its prompt chain and compare them against a "Safety Manifold"—a pre-computed cluster of approved operational parameters.

Splunk SPL Implementation Strategy

We use Splunk's Machine Learning Toolkit (MLTK/DLTK) to monitor cosine similarity in near real-time.

          index=ai_logs
          sourcetype="suhlabs:agent:thought" |
          eval embedding =
          ml_generate_embedding(thought_content) |
          fit DBSCAN embedding into
          agent_safety_model |
          apply agent_safety_model |
          search cluster_id = -1 |
          alert level=critical
          action=kill_agent_process
        

4. Interactive Safeguard Simulation

Simulate the Drift Detection engine. Enter a command below to see if it triggers the safety kill-switch based on heuristic analysis.

Agent Command Buffer:

> WAITING_FOR_TELEMETRY...