Observe-only
Audits observe behavior without intervening in the audited system. No changes are made to configuration, operation, prompts, guardrails, or runtime conditions during evidence collection.
2026 · Public reference layer
Observe-only AI behavior audit reference layer.
This site documents an observe-only audit methodology and the SENTINEL execution tool that generates structured audit evidence across deployments. It defines boundaries, terminology, and an evidence model for reproducible observation of AI system behavior without intervention.
No certification, no verdicts, and no compliance claims are issued here.
Core principles
Immutable boundaries for observation and evidence handling.
Evidence-only
Outputs are structured technical evidence bundles. Evidence is reproducible, versioned, and traceable to scenarios, tools, and methodology versions, without interpretive summaries as decisions.
No authority
This reference layer does not issue certifications, approvals, or conformity statements. Evidence is not a decision and does not substitute legal, regulatory, or compliance judgment.
SENTINEL
Audit execution tool description (development in progress).
What it is
- Executes deterministic audit rules against observable AI outputs.
- Runs predefined bilingual scenarios in a stateless, single-turn pattern.
- Collects raw outputs and associated metadata as evidence artifacts.
- Packages artifacts into versioned evidence bundles for reproducibility.
- Supports adapters for local runtimes and external HTTP APIs.
What it is not
- Not a certification system or approval authority.
- Not a compliance evaluator or conformity assessor.
- Not a scoring engine that assigns ratings or risk scores.
- Not a recommendation system that prescribes mitigations or actions.
- Not an agent that controls, modifies, or optimizes audited systems.
- Not a guarantee of safety, reliability, or trustworthiness.
- Not a legal, regulatory, or policy decision mechanism.
Prototype status: SENTINEL has been exercised across heterogeneous deployments as a
neutral measurement instrument.
Prototype demonstration (Phase 1–2)
Functional execution of the SENTINEL audit prototype.
SENTINEL has been implemented as a functional audit execution prototype and exercised in controlled technical environments. Two demonstration phases were conducted to verify that the tool can execute observe-only audit scenarios and generate reproducible, machine-readable audit evidence.
The purpose of these demonstrations was to validate SENTINEL as a neutral measurement instrument, not to evaluate, certify, or judge the audited AI systems.
- Phase 1: Local open-weights LLM runtime.
- Phase 2: Stateless external API-based AI system.
- Output: structured audit evidence bundles (no interpretation).
This demonstration does not constitute certification, compliance assessment, or regulatory
evaluation.
Methodology summary
High-level description of observation design and evidence production.
The methodology defines scenario execution and evidence capture for reproducible observation across deployments. It applies deterministic, string-based rules to recorded outputs and binds evidence to tool and methodology versions. Outcomes are recorded as evidence artifacts, not interpreted as compliance or non-compliance.
- Scenario specification with stable identifiers and parameterized inputs.
- Stateless execution: one prompt, one response, no retained conversational context.
- Repeated runs under identical inputs to observe stability and variance.
- Rule application external to the target, using deterministic indicators only.
Evidence model
Structure of an evidence bundle produced by observe-only execution.
An evidence bundle is a structured container linking a scenario run to captured artifacts, versions, and integrity references. Its purpose is reproducible observation: the same scenario and conditions should yield a comparable evidence footprint, without adding interpretive conclusions.
- Bundle metadata (timestamps, identifiers, environment notes).
- Scenario reference (scenario ID, parameters, language).
- Observed artifacts (outputs, transcripts, logs, attachments).
- Version links (methodology version, tool version, configuration hash).
- Integrity references (hashes, checksums, optional signatures).
{
"evidence_bundle_id": "eb_2026_02_03_0001",
"methodology_version": "v1",
"tool": { "name": "SENTINEL", "version": "v0.x" },
"scenario": { "id": "scn_observation_01", "params": { "seed": 42 } },
"artifacts": [{ "name": "output.txt", "hash": "sha256:..." }],
"note": "synthetic example; no interpretation"
}
Governance
Stability, versioning, and disciplined change control.
This reference layer is versioned. Evidence bundles record methodology and tool versions to preserve reproducibility and enable comparison across time. Documentation updates may refine clarity or formats while preserving the declared principles and boundaries.
Terminology used in this reference layer aligns with a public descriptive ontology for AI core concepts (AI Core v1.0).
Changes that alter principles, scope of authority, or meaning of evidence require a new baseline version. Baseline changes are explicit, dated, and documented as a new version rather than silent edits.
Stewardship
Maintenance and publication of the reference layer.
This site is maintained under stewardship to provide a stable public reference for observe-only audit methodology and evidence structure. Stewardship focuses on clarity, versioning, and preservation of boundaries between evidence production and any external decision layers.
This reference layer is informational and technical; it is not legal or regulatory advice.