Evidence that auditors, regulators, and insurers can verify.
AI agents are taking action in production. Panopticore produces the governance evidence that closes the gap between what agents do and what your organization can prove they did.
Can you prove what your agents did?
AI agents move data, call APIs, and change production systems. Auditors and insurance carriers now want proof of how those actions are controlled.
Most enterprises cannot answer three questions about their agents: what did the agent do, which policies were in effect, and were those policies enforced? Observability dashboards record telemetry, but an auditor, regulator, or insurer cannot verify telemetry independently.
Panopticore answers them with signed evidence a third party can check.
What's in an Evidence Binder, and who can check it.
What's inside
- Identity chain: principal, session, signed tokens
- Action inventory: attempted, executed, blocked
- Policy decisions: which policy, why, justification
- Approvals: who approved, when, and scope
- Verification artifacts: hashes, signatures, ledger linkage
Independently verifiable
Evidence Binders are self-contained. Internal auditors, external auditors, and other third parties can verify them without vendor access or trust in the system that produced them.
$ binderverify --binder binder.json \
--evidence-signature binder.evidence.dsse --pdf binder.pdf
merkle_root: 9f8e7d6c5b...
policy_checksum: a1b2c3d4e5...
signing_time: 2026-04-15T14:32:09ZControls and evidence for the insurance conversation.
The Evidence Binder provides the controls and evidence insurers are increasingly requiring as agentic AI systems take production actions.
It gives your risk team verifiable records of agent activity and enforcement to bring to those conversations with carriers.
Frameworks that apply to agent governance.
NIST AI RMF
AI Risk Management Framework 1.0 with Generative AI Profile (AI 600-1). NIST released a concept note for an AI RMF Profile on Trustworthy AI in Critical Infrastructure on April 7, 2026. NIST is revising AI RMF 1.0 under the White House AI Action Plan.
EU AI Act
The EU AI Act's high-risk requirements, including risk management, technical documentation, and automatic logging, apply from December 2, 2027 for Annex III systems and August 2, 2028 for high-risk AI in regulated products.
The audit-driven pilot.
From zero governance evidence to a documented evidence chain in four steps.
On the record with NIST.
In February 2026, founder Dave Medeiros submitted a formal comment to NIST on Security Considerations for Artificial Intelligence Agents (Docket NIST-2025-0035, 91 FR 698).
“The most consequential security gap in deployed AI agent systems today is not prompt injection, model manipulation, or adversarial attacks on the model itself. It is the absence of a verifiable, tamper-evident record of what an agent actually did at runtime.”
“Every decision is recorded in a cryptographically signed audit trail that can be verified offline by any internal auditor or third party.”
Further reading
Our NIST submission in plain language: the evidence gap, action-level risk, bypass-by-architecture, and governance independence.
AI systems cannot reliably govern themselves. Why deterministic, infrastructure-layer governance beats self-reporting by the agent under review.
Close the evidence gap.
Request a briefing and we'll walk through the Evidence Binder with your audit team.