EPISTEMIC SECURITY FOR AI AGENTS

Know what your agents believe.

Trace how evidence becomes belief before belief becomes an irreversible action.

DETERMINISTIC VERDICTS · SHADOW MODE FIRST · RAW EVIDENCE STAYS INSPECTABLE

SYNTHETIC INCIDENTACTION APPROVED
01Observesources and claims
02Mapbelief and memory
03Testevidence independence
04Reviewdecision impact

ONE PLAUSIBLE CLAIM

A clean run can still be compromised.

No malicious prompt. No stolen credential. One first-party claim becomes four agreeing agents and one unsupported decision.

SYNTHETIC INCIDENTSOURCE OBSERVED

Scout reads a plausible first-party certification claim.

EXHIBIT 01PROVENANCE CONVERGENCE
Monochrome evidence paths converging on one hidden origin
Illustrative evidence graph generated for the Northstar synthetic incident.

THE NEW ATTACK SURFACE

Permissions can be correct while the decision is wrong.

An adversary does not need to control an agent if it can shape the information an autonomous organization trusts.

Inspect the influence model

DETERMINISTIC ANALYSIS

Security for the story behind the action.

Revive models sources, claims, beliefs, memories, and decisions as one inspectable system.

02

Qualification drift

Catch “reported” becoming “verified” as summaries pass between agents.

03

Source laundering

Preserve original ownership after repeated handoffs, citations, and memory writes.

04

Evidence policy

Evaluate whether a consequential action has enough qualifying independent evidence.

05

Counterfactual replay

Remove one source and calculate every belief, memory, and action that changes.

PRODUCT BOUNDARY

Runtime security watches what agents do.

REVIVE

shows why they decided to do it.

Not a truth oracle. Source trust and contradiction signals remain inspectable inputs.

Not a prompt firewall. Plausible content can be dangerous without containing an instruction.

Not generic observability. Every finding resolves to a load-bearing belief or action.

SMALL INTEGRATION SURFACE

One trace format. One evidence graph.

Start in shadow mode. Normalize provenance events and review deterministic findings before enforcing policy.

Read the integration guide
belief.jsonLOCAL OR HOSTED
{
  "agentId": "compliance",
  "claimId": "northstar:soc2",
  "modality": "verified",
  "confidence": 0.92,
  "derivedFrom": [
    "scout-output",
    "analyst-summary"
  ],
  "usedBy": ["approve-vendor"]
}
REVIEW REQUIRED4 agents resolve to 1 challenged origin

RUN THE SYNTHETIC INCIDENT

Expose the influence path.

Watch one claim propagate, inspect every dependency, then replace the sample with your own agent trace.