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KTP-Audit: Flight Recorder Specification

"The invisible success problem: when security works, nothing happens. The Flight Recorder makes the invisible visible."


At a Glance

Property Value
Status Draft
Version 0.1
Dependencies KTP-Core, KTP-Transport
Required By KTP-Governance, KTP-Conformance

The Problem

Traditional logs are often incomplete, mutable, or unstructured. When an incident occurs (or is prevented), it is difficult to reconstruct exactly why a decision was made. Was it a policy error? A sensor failure? A malicious agent?

The Solution: The Flight Recorder

KTP-Audit defines a system for recording Decision Geometry—a multi-dimensional snapshot of the agent, the environment, and the logic used at the exact moment of a decision. These records are cryptographically chained to ensure immutability.

Decision Geometry

{
  "record_id": "dr-uuid-12345",
  "timestamp": "2025-11-25T14:32:07.123Z",
  "chain": {
    "previous_hash": "sha256:abc123...",
    "sequence_number": 1847392
  },
  "agent": {
    "id": "agent:7gen:optimized:a1b2c3d4",
    "e_base": 87,
    "e_trust": 42,
    "tier": "analyst"
  },
  "environment": {
    "context_tensor": { "m": 0.45, "p": 0.78, "h": 0.82 },
    "risk_factor": 0.523
  },
  "decision": {
    "outcome": "DENY",
    "reason": "silent_veto",
    "constraint": "soul_violation"
  }
}

Record Types

The Core Record. Logged every time an agent requests an action. Captures the \(A \leq E\) calculation.

  • Use Case: "Why was Agent X blocked from accessing the database?"

State Transition. Logged when an agent's \(E_{base}\) or \(E_{trust}\) changes significantly.

  • Use Case: "Why did Agent Y drop from Operator to Analyst tier?"

Critical Event. Logged when the Soul constraint triggers a hard stop.

  • Use Case: "Investigate potential rogue agent behavior."

Infrastructure Health. Logged when sensors fail, Oracles partition, or keys rotate.

  • Use Case: "Diagnose the root cause of the zone outage."

Forensic Reconstruction

Because the Flight Recorder captures the full Environmental Snapshot, investigators can "replay" a decision.

  1. Load Snapshot: Restore the exact state of the Context Tensor from the record.
  2. Re-run Logic: Feed the agent's request into the policy engine.
  3. Verify Outcome: Confirm that the decision matches the log.

This capability is critical for proving that the system behaved correctly during a security incident.


Core Components

Cryptographic Chaining

Each record includes the hash of the previous record, creating a tamper-evident blockchain-like structure.

Counterfactual Analysis

Records can include "what-if" data: "If the risk factor had been 0.1 lower, this action would have been allowed."

Compliance Export

Standardized formats for exporting logs to external auditors or SIEM systems.

Liability Attribution

Using the logs to determine if a failure was due to human negligence, agent error, or environmental force majeure.


Related Specifications
  • KTP-Threat Model — The threats that the Flight Recorder helps investigate.
  • KTP-Tensors — The environmental data captured in every record.
  • KTP-Identity — The agent identity tracked across records.
  • KTP-Recovery — How to use logs to reconstruct state after a failure.

Official RFC Document

View Complete RFC Text (ktp-audit.txt)
Kinetic Trust Protocol                                      C. Perkins
Specification Draft                                           NMCITRA
Version: 0.1                                             November 2025


     Kinetic Trust Protocol (KTP) - Flight Recorder Specification

Abstract

   This document specifies the Flight Recorder system for the Kinetic
   Trust Protocol (KTP). The Flight Recorder provides immutable audit
   logging of all authorization decisions, enabling forensic analysis,
   compliance verification, and system learning.

   The specification covers Decision Geometry (the multi-dimensional
   record of each decision), storage requirements, query interfaces,
   retention policies, and integration with external audit systems.

Status of This Memo

   This document is a draft specification developed by the New Mexico
   Cyber Intelligence & Threat Response Alliance (NMCITRA). It has not
   been submitted to the IETF and does not represent an Internet
   Standard or consensus of any standards body.

   Feedback and contributions are welcome at:
   https://github.com/nmcitra/ktp-rfc

Copyright Notice

   Copyright (c) 2025 Chris Perkins / NMCITRA. This work is licensed
   under the Apache License, Version 2.0.

Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . .   2
       1.1.  The Invisible Success Problem  . . . . . . . . . . . .   2
       1.2.  Flight Recorder Philosophy . . . . . . . . . . . . . .   3
       1.3.  Requirements Language  . . . . . . . . . . . . . . . .   4
   2.  Terminology  . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Decision Geometry  . . . . . . . . . . . . . . . . . . . . .   5
       3.1.  Core Components  . . . . . . . . . . . . . . . . . . .   6
       3.2.  Environmental Snapshot . . . . . . . . . . . . . . . .   8
       3.3.  Agent Context  . . . . . . . . . . . . . . . . . . . .   9
       3.4.  Decision Outcome . . . . . . . . . . . . . . . . . . .  10
       3.5.  Counterfactual Analysis  . . . . . . . . . . . . . . .  11
   4.  Record Types . . . . . . . . . . . . . . . . . . . . . . . .  11
       4.1.  Authorization Decision . . . . . . . . . . . . . . . .  11
       4.2.  Trust Score Change . . . . . . . . . . . . . . . . . .  13
       4.3.  Tier Transition  . . . . . . . . . . . . . . . . . . .  14
       4.4.  Soul Veto  . . . . . . . . . . . . . . . . . . . . . .  15
       4.5.  Attestation  . . . . . . . . . . . . . . . . . . . . .  16
   5.  Immutability Requirements  . . . . . . . . . . . . . . . . .  17
       5.1.  Append-Only Storage  . . . . . . . . . . . . . . . . .  17
       5.2.  Cryptographic Chaining . . . . . . . . . . . . . . . .  18
       5.3.  Tamper Detection . . . . . . . . . . . . . . . . . . .  19
   6.  Storage Architecture . . . . . . . . . . . . . . . . . . . .  20
       6.1.  Hot Storage  . . . . . . . . . . . . . . . . . . . . .  20
       6.2.  Warm Storage . . . . . . . . . . . . . . . . . . . . .  21
       6.3.  Cold Storage . . . . . . . . . . . . . . . . . . . . .  21
       6.4.  Retention Policies . . . . . . . . . . . . . . . . . .  22
   7.  Query Interface  . . . . . . . . . . . . . . . . . . . . . .  23
       7.1.  Query Language . . . . . . . . . . . . . . . . . . . .  23
       7.2.  Common Queries . . . . . . . . . . . . . . . . . . . .  25
       7.3.  Forensic Reconstruction  . . . . . . . . . . . . . . .  27
   8.  Accountability Analysis  . . . . . . . . . . . . . . . . . .  28
       8.1.  Human Negligence vs. Environmental Force . . . . . . .  28
       8.2.  Attribution Framework  . . . . . . . . . . . . . . . .  30
       8.3.  Liability Boundaries . . . . . . . . . . . . . . . . .  31
   9.  External Integration . . . . . . . . . . . . . . . . . . . .  32
       9.1.  SIEM Integration . . . . . . . . . . . . . . . . . . .  32
       9.2.  Compliance Export  . . . . . . . . . . . . . . . . . .  33
       9.3.  Machine Learning Pipeline  . . . . . . . . . . . . . .  34
   10. Security Considerations  . . . . . . . . . . . . . . . . . .  35
   11. References . . . . . . . . . . . . . . . . . . . . . . . . .  36
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . .  37

1.  Introduction

   The Flight Recorder is the memory of a KTP system. Every decision,
   every state change, every veto is recorded with full context,
   creating an immutable history that can be queried, analyzed, and used
   for both forensic investigation and system improvement.

1.1.  The Invisible Success Problem

   Traditional security systems suffer from an invisible success
   problem: when an attack is prevented, there is no incident to
   investigate. The prevented disaster is invisible, making it difficult
   to:

   -  Prove that security controls are working
   -  Learn from near-misses
   -  Justify security investments
   -  Distinguish luck from competence

   The Flight Recorder solves this by recording every Silent Veto with
   the same detail as an allowed action. A prevented attack is visible
   in the record:

      "At 14:32:07, Agent X attempted action Y with risk 85.
       E_trust was 42 due to elevated Heat (0.78) from active
       attack indicators. Silent Veto triggered. Action denied.
       If this action had been permitted, blast radius would have
       included 47 downstream services."

   The disaster that didn't happen is now visible, auditable, and
   valuable for learning.

1.2.  Flight Recorder Philosophy

   The Flight Recorder follows these principles:

   1. Record Everything: Every authorization decision, not just denials
      or anomalies. The baseline is as important as the exceptions.

   2. Context Over Outcome: The outcome (allow/deny) is one bit. The
      context (why, what was the environment, what would have happened)
      is where the value lies.

   3. Immutability: Once written, records cannot be altered or deleted.
      This is essential for legal defensibility and forensic integrity.

   4. Queryability: Data that can't be queried is data that won't be
      used. The Flight Recorder must support rich queries for forensic
      analysis.

   5. Forward Secrecy: Records should be usable for learning without
      exposing sensitive operational details.

1.3.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174].

2.  Terminology

   Attestation: A cryptographically signed statement about an agent's
   behavior, issued by the Trust Oracle after successful transaction.

   Decision Geometry: The multi-dimensional record of a single
   authorization decision, including agent context, environmental state,
   action details, and outcome.

   Flight Recorder: The immutable audit log system that records all KTP
   decisions and state changes.

   Forensic Reconstruction: The process of recreating the exact
   conditions that led to a specific decision, using Flight Recorder
   data.

   Hot Storage: Fast, frequently accessed storage for recent records
   (hours to days).

   Cold Storage: Archival storage for historical records (months to
   years).

   Record Chain: A sequence of records cryptographically linked via hash
   pointers, ensuring tamper detection.

   Warm Storage: Intermediate storage for less frequently accessed
   records (days to months).

3.  Decision Geometry

   Every authorization decision is recorded as a Decision Geometry - a
   multi-dimensional snapshot that captures not just what happened, but
   why it happened and what would have happened under different
   conditions.

3.1.  Core Components

   A Decision Geometry record contains:

      {
        "record_id": "dr-uuid-12345",
        "record_type": "authorization_decision",
        "timestamp": "2025-11-25T14:32:07.123Z",
        "chain": {
          "previous_hash": "sha256:abc123...",
          "sequence_number": 1847392
        },

        "agent": { ... },        // Agent context
        "environment": { ... },  // Environmental snapshot
        "action": { ... },       // Requested action
        "decision": { ... },     // Outcome and reasoning
        "counterfactual": { ... } // What-if analysis
      }

3.1.1.  Record Identification

   record_id: Globally unique identifier for this record. Format: "dr-"
   + UUIDv4 for decision records.

   record_type: Type of record. One of:
   -  authorization_decision
   -  trust_score_change
   -  tier_transition
   -  soul_veto
   -  attestation
   -  system_event

   timestamp: ISO 8601 timestamp with millisecond precision. MUST be in
   UTC.

3.1.2.  Chain Integrity

   previous_hash: SHA-256 hash of the immediately preceding record.
   Creates tamper-evident chain.

   sequence_number: Monotonically increasing sequence number. Gaps
   indicate potential tampering or data loss.

3.2.  Environmental Snapshot

   The environmental snapshot captures the complete Context Tensor state
   at decision time:

      "environment": {
        "context_tensor": {
          "m": 0.45,
          "p": 0.78,
          "h": 0.82,
          "t": 0.30,
          "i": 0.25,
          "o": 0.15
        },
        "r": 0.523,
        "soul": {
          "s": 0,
          "constraint_type": null
        },
        "sensor_health": {
          "m_feeds_active": 4,
          "m_feeds_total": 5,
          "p_feeds_active": 3,
          "p_feeds_total": 3,
          "degraded_dimensions": ["m"]
        },
        "trust_oracle": {
          "oracle_id": "oracle-east-1",
          "quorum": "3-of-5",
          "latency_ms": 3
        },
        "zone": {
          "zone_id": "zone-blue-prod-01",
          "zone_type": "blue",
          "federation": ["zone-blue-prod-02", "zone-cyan-staging"]
        }
      }

   This snapshot enables forensic reconstruction: given the exact
   environmental conditions, we can replay the decision logic and verify
   that the correct outcome was reached.

3.3.  Agent Context

   Agent context captures the identity and state of the requesting
   agent:

      "agent": {
        "agent_id": "agent:7gen:optimized:a1b2c3d4",
        "lineage": "persistent",
        "generation": 7,
        "sponsor": null,
        "e_base": 87,
        "e_trust": 42,
        "de_dt": -2.3,
        "sigma": 0.15,
        "tier": "analyst",
        "previous_tier": "operator",
        "tier_duration_seconds": 847,
        "trajectory_hash": "sha256:def456...",
        "resilience_score": 0.82,
        "session": {
          "session_id": "sess-xyz789",
          "session_start": "2025-11-25T13:00:00Z",
          "actions_this_session": 1247,
          "vetoes_this_session": 3
        }
      }

   Key fields:

   e_base: Agent's intrinsic capability (from Proof of Resilience)

   e_trust: Effective Trust Score after environmental deflation

   de_dt: Trust velocity (rate of change)

   tier: Current Trust Tier

   previous_tier: Tier before most recent transition (if recent)

   trajectory_hash: Hash of agent's trajectory chain for verification

3.4.  Decision Outcome

   The decision outcome records what happened and why:

      "decision": {
        "outcome": "deny",
        "reason": "TRUST_INSUFFICIENT",
        "evaluation": {
          "soul_check": "pass",
          "tier_check": "pass",
          "zeroth_law_check": "fail",
          "a": 85,
          "e_trust": 42,
          "gap": 43
        },
        "enforcement_point": {
          "pep_id": "pep-api-gateway-east",
          "pep_type": "api_gateway",
          "latency_ms": 2
        },
        "notification": {
          "agent_notified": true,
          "response_code": 403,
          "response_body_hash": "sha256:ghi789..."
        }
      }

   Key fields:

   outcome: "allow" or "deny"

   reason: KTP error code if denied

   evaluation: Step-by-step evaluation trace

   gap: Difference between A and E_trust (how far from threshold)

3.5.  Counterfactual Analysis

   The counterfactual section records what would have happened under
   different conditions:

      "counterfactual": {
        "if_allowed": {
          "blast_radius": {
            "services_affected": 47,
            "users_affected": 12500,
            "data_records_at_risk": 1847392
          },
          "reversibility": "partial",
          "estimated_recovery_time_minutes": 45
        },
        "threshold_analysis": {
          "e_trust_needed": 85,
          "e_trust_actual": 42,
          "r_needed_for_allow": 0.03,
          "r_actual": 0.52,
          "primary_contributor": "heat",
          "heat_value": 0.82,
          "heat_source": "waf_blocks_elevated"
        }
      }

   This analysis is invaluable for:
   -  Understanding near-misses
   -  Quantifying prevented damage
   -  Identifying which tensor dimension drove the decision

4.  Record Types

4.1.  Authorization Decision

   The most common record type, created for every action request.

   Schema:

      {
        "record_type": "authorization_decision",
        "action": {
          "method": "DELETE",
          "resource": "/api/users/12345",
          "action_class": "delete_permanent",
          "action_risk": 85,
          "parameters": {
            "user_id": "12345",
            "cascade": true
          },
          "source_ip": "10.0.1.45",
          "request_id": "req-uuid-67890"
        },
        "agent": { ... },
        "environment": { ... },
        "decision": { ... },
        "counterfactual": { ... }
      }

4.2.  Trust Score Change

   Recorded when an agent's E_trust changes significantly (threshold
   configurable, default: change >= 5 points or crosses tier boundary).

   Schema:

      {
        "record_type": "trust_score_change",
        "agent_id": "agent:7gen:optimized:a1b2c3d4",
        "change": {
          "e_trust_before": 88,
          "e_trust_after": 72,
          "e_base": 95,
          "r_before": 0.07,
          "r_after": 0.24,
          "delta": -16,
          "velocity": -3.2
        },
        "cause": {
          "primary_dimension": "heat",
          "heat_before": 0.15,
          "heat_after": 0.65,
          "trigger_event": "waf_block_spike",
          "trigger_details": {
            "waf_blocks_per_minute": 5420,
            "baseline": 200
          }
        },
        "impact": {
          "tier_before": "operator",
          "tier_after": "analyst",
          "capabilities_lost": [
            "write:production",
            "execute:deployments",
            "admin:config"
          ]
        }
      }

4.3.  Tier Transition

   Recorded when an agent crosses a Trust Tier boundary.

   Schema:

      {
        "record_type": "tier_transition",
        "agent_id": "agent:7gen:optimized:a1b2c3d4",
        "transition": {
          "from_tier": "operator",
          "to_tier": "analyst",
          "direction": "demotion",
          "e_trust_at_transition": 68,
          "threshold_crossed": 70
        },
        "duration_in_previous_tier_seconds": 14832,
        "actions_in_previous_tier": 4721,
        "vetoes_in_previous_tier": 12,
        "agent_state": {
          "in_flight_operations": 2,
          "operations_cancelled": 1,
          "graceful_degradation": true
        }
      }

4.4.  Soul Veto

   Recorded when a sovereignty constraint blocks an action. These
   records receive special handling due to their legal and cultural
   significance.

   Schema:

      {
        "record_type": "soul_veto",
        "agent_id": "agent:7gen:optimized:a1b2c3d4",
        "action": {
          "method": "POST",
          "resource": "/api/data/export",
          "action_class": "read_sensitive",
          "action_risk": 40
        },
        "soul_constraint": {
          "s": 1,
          "constraint_type": "tk_label",
          "constraint_id": "TK-NC-001",
          "constraint_name": "TK Non-Commercial",
          "authority": "https://localcontexts.org/label/tk-nc/",
          "community": "Example Indigenous Community",
          "data_lineage": {
            "data_id": "data-record-xyz",
            "origin": "tribal-repository",
            "collection_date": "2024-06-15",
            "labels_applied": ["TK-NC", "TK-A"]
          }
        },
        "note": {
          "e_trust": 92,
          "action_risk": 40,
          "would_have_passed_zeroth_law": true,
          "blocked_by_sovereignty": true
        },
        "remediation_provided": "Contact community data steward"
      }

   Soul Veto records MUST be:
   -  Retained for minimum 7 years (or as required by jurisdiction)
   -  Exportable for community review upon request
   -  Protected from modification by any party

4.5.  Attestation

   Recorded when the Trust Oracle issues an attestation of successful
   transaction, contributing to an agent's Proof of Resilience.

   Schema:

      {
        "record_type": "attestation",
        "agent_id": "agent:7gen:optimized:a1b2c3d4",
        "attestation": {
          "attestation_id": "att-uuid-12345",
          "action_completed": "deploy_service_update",
          "action_risk": 75,
          "e_trust_at_action": 82,
          "environment_state": "degraded",
          "r_at_action": 0.14,
          "resilience_contribution": 0.003
        },
        "signatures": {
          "agent": "sig:agent:abc123...",
          "oracle": "sig:oracle:def456...",
          "timestamp_authority": "sig:tsa:ghi789..."
        }
      }

5.  Immutability Requirements

5.1.  Append-Only Storage

   The Flight Recorder MUST use append-only storage:

   1. Records can only be added, never modified or deleted

   2. Storage system must enforce append-only at infrastructure level

   3. Deletions are only permitted via retention policy expiration

   4. Even expired records should be archived before deletion

   Implementation options:

   -  Append-only databases (e.g., Datomic, XTDB)
   -  Immutable storage services (e.g., AWS S3 Object Lock)
   -  Blockchain or distributed ledger (for highest assurance)
   -  Write-once media (for air-gapped compliance)

5.2.  Cryptographic Chaining

   Records MUST be cryptographically chained:

   1. Each record includes hash of previous record

   2. Hash algorithm: SHA-256 (minimum)

   3. Chain creates tamper-evident sequence

   4. Any modification breaks the chain

   Chain structure:

      Record N:
        previous_hash: SHA256(Record N-1)
        sequence_number: N
        content: { ... }
        record_hash: SHA256(previous_hash + sequence_number + content)

      Record N+1:
        previous_hash: record_hash from Record N
        sequence_number: N+1
        ...

   Chain verification:

      For each record from oldest to newest:
        1. Compute expected hash from content
        2. Compare to stored hash
        3. Verify previous_hash matches prior record's hash
        4. Verify sequence numbers are contiguous

5.3.  Tamper Detection

   The Flight Recorder MUST detect tampering:

   1. Periodic chain integrity verification (recommended: hourly)

   2. Alert on any hash mismatch or sequence gap

   3. Maintain chain anchors in external system (e.g., public blockchain
      timestamp)

   4. Support third-party audit of chain integrity

   Tamper detection response:

   1. Immediately alert security team

   2. Preserve evidence (snapshot of tampered section)

   3. Isolate affected storage

   4. Begin forensic investigation

   5. Do NOT attempt to "fix" the chain

6.  Storage Architecture

6.1.  Hot Storage

   Hot storage holds recent records for fast query access.

   Characteristics:
   -  Retention: 24-72 hours
   -  Latency: < 10ms query response
   -  Technology: In-memory database, SSD-backed store
   -  Replication: Synchronous, multi-region

   Use cases:
   -  Real-time dashboards
   -  Active incident investigation
   -  Live query by agents

6.2.  Warm Storage

   Warm storage holds intermediate-age records.

   Characteristics:
   -  Retention: 7-90 days
   -  Latency: < 100ms query response
   -  Technology: SSD-backed database, time-series DB
   -  Replication: Asynchronous, multi-region

   Use cases:
   -  Trend analysis
   -  Compliance reporting
   -  Investigation of recent incidents

6.3.  Cold Storage

   Cold storage holds historical records for compliance and forensics.

   Characteristics:
   -  Retention: 1-7+ years (per policy)
   -  Latency: Minutes to hours (acceptable for archival)
   -  Technology: Object storage, tape archive
   -  Replication: Cross-region, immutable

   Use cases:
   -  Legal discovery
   -  Long-term compliance
   -  Historical analysis

6.4.  Retention Policies

   Recommended minimum retention periods:

   +---------------------------+------------+------------------------+
   | Record Type               | Minimum    | Notes                  |
   +---------------------------+------------+------------------------+
   | Authorization Decision    | 1 year     | Standard audit         |
   | Trust Score Change        | 1 year     | Standard audit         |
   | Tier Transition           | 2 years    | Behavioral analysis    |
   | Soul Veto                 | 7 years    | Legal/cultural         |
   | Attestation               | 7 years    | Proof of Resilience    |
   | System Event              | 90 days    | Operational            |
   +---------------------------+------------+------------------------+

   Retention policies MUST be:
   -  Documented and versioned
   -  Approved by legal/compliance
   -  Enforced automatically
   -  Auditable (who changed policy, when)

7.  Query Interface

7.1.  Query Language

   The Flight Recorder MUST provide a query interface supporting:

7.1.1.  Temporal Queries

   Query records by time range:

      {
        "query": "temporal",
        "start": "2025-11-25T00:00:00Z",
        "end": "2025-11-25T23:59:59Z",
        "record_types": ["authorization_decision", "soul_veto"]
      }

7.1.2.  Agent Queries

   Query records for specific agent:

      {
        "query": "agent",
        "agent_id": "agent:7gen:optimized:a1b2c3d4",
        "start": "2025-11-01T00:00:00Z",
        "include": ["decisions", "tier_transitions", "attestations"]
      }

7.1.3.  Outcome Queries

   Query by decision outcome:

      {
        "query": "outcome",
        "outcome": "deny",
        "reason": "TRUST_INSUFFICIENT",
        "start": "2025-11-25T00:00:00Z"
      }

7.1.4.  Environmental Queries

   Query by environmental conditions:

      {
        "query": "environment",
        "conditions": {
          "heat": { "gte": 0.7 },
          "r": { "gte": 0.5 }
        },
        "start": "2025-11-25T00:00:00Z"
      }

7.1.5.  Counterfactual Queries

   Query by potential impact:

      {
        "query": "counterfactual",
        "if_allowed": {
          "blast_radius.services_affected": { "gte": 10 }
        },
        "outcome": "deny",
        "start": "2025-11-25T00:00:00Z"
      }

7.2.  Common Queries

7.2.1.  "Show me all prevented incidents today"

      {
        "query": "compound",
        "filters": [
          { "record_type": "authorization_decision" },
          { "outcome": "deny" },
          { "counterfactual.if_allowed.blast_radius.services_affected":
            { "gte": 5 } }
        ],
        "start": "2025-11-25T00:00:00Z",
        "sort": "counterfactual.if_allowed.blast_radius.services_affected",
        "order": "desc"
      }

7.2.2.  "What was Agent X doing when trust dropped?"

      {
        "query": "correlation",
        "anchor": {
          "record_type": "trust_score_change",
          "agent_id": "agent:7gen:optimized:a1b2c3d4",
          "change.delta": { "lte": -10 }
        },
        "correlate": {
          "record_type": "authorization_decision",
          "window_before_seconds": 300,
          "window_after_seconds": 60
        }
      }

7.2.3.  "Show all Soul vetoes for community X"

      {
        "query": "soul_veto",
        "filters": [
          { "soul_constraint.community": "Example Indigenous Community" }
        ],
        "start": "2025-01-01T00:00:00Z"
      }

7.2.4.  "Agents with highest veto rate this week"

      {
        "query": "aggregate",
        "record_type": "authorization_decision",
        "group_by": "agent_id",
        "metrics": [
          { "name": "total_decisions", "function": "count" },
          { "name": "vetoes", "function": "count",
            "filter": { "outcome": "deny" } },
          { "name": "veto_rate", "function": "ratio",
            "numerator": "vetoes", "denominator": "total_decisions" }
        ],
        "start": "2025-11-18T00:00:00Z",
        "sort": "veto_rate",
        "order": "desc",
        "limit": 10
      }

7.3.  Forensic Reconstruction

   The Flight Recorder MUST support forensic reconstruction: the ability
   to recreate the exact conditions that led to a decision.

   Reconstruction query:

      {
        "query": "reconstruct",
        "record_id": "dr-uuid-12345",
        "include": [
          "full_context_tensor",
          "sensor_values",
          "agent_trajectory",
          "oracle_state",
          "pep_configuration"
        ]
      }

   Reconstruction output:

      {
        "reconstruction": {
          "record_id": "dr-uuid-12345",
          "timestamp": "2025-11-25T14:32:07.123Z",
          "complete": true,
          "context_tensor": {
            "m": { "value": 0.45, "feeds": [...] },
            "p": { "value": 0.78, "feeds": [...] },
            ...
          },
          "agent_trajectory": {
            "last_100_actions": [...],
            "trust_history_1h": [...]
          },
          "decision_replay": {
            "step_1_soul_check": "pass",
            "step_2_tier_check": "pass",
            "step_3_zeroth_law": "fail",
            "deterministic": true,
            "same_outcome_on_replay": true
          }
        }
      }

8.  Accountability Analysis

8.1.  Human Negligence vs. Environmental Force

   A key use of the Flight Recorder is distinguishing between:

   1. Human negligence: A human made a decision that violated policy or
      caused harm through carelessness.

   2. Environmental force majeure: The system correctly responded to
      environmental conditions beyond anyone's control.

   The Decision Geometry provides the evidence:

   Negligence indicators:
   -  Action was allowed despite high risk
   -  Trust Score was artificially elevated
   -  Sensor data was ignored or overridden
   -  Policy was modified without authorization

   Force majeure indicators:
   -  Action was correctly vetoed by physics
   -  Environmental conditions changed rapidly
   -  All sensors functioning correctly
   -  System behaved according to specification

8.2.  Attribution Framework

   The Flight Recorder enables precise attribution:

   +------------------+--------------------------------------------+
   | Question         | Flight Recorder Evidence                   |
   +------------------+--------------------------------------------+
   | Who requested?   | agent_id, session, source_ip               |
   | What was asked?  | action.method, action.resource             |
   | When?            | timestamp                                  |
   | What was state?  | environment.context_tensor, e_trust        |
   | What decided?    | decision.outcome, decision.reason          |
   | Why?             | decision.evaluation, counterfactual        |
   | What was impact? | counterfactual.if_allowed.blast_radius     |
   +------------------+--------------------------------------------+

8.3.  Liability Boundaries

   The Flight Recorder creates clear liability boundaries:

   If action was vetoed:
   -  System functioned correctly
   -  Damage was prevented
   -  Liability lies with environmental conditions
   -  No human negligence (unless sensors were manipulated)

   If action was allowed and caused harm:
   -  Was A correctly classified? (If not: policy author liable)
   -  Was E_trust correctly calculated? (If not: Oracle operator liable)
   -  Were sensors functioning? (If not: operations liable)
   -  Was PEP functioning? (If not: infrastructure liable)
   -  Were all systems correct? (If yes: environmental conditions
      changed faster than system could respond - force majeure)

9.  External Integration

9.1.  SIEM Integration

   Flight Recorder records SHOULD be exportable to SIEM systems:

   Export format (CEF):

      CEF:0|NMCITRA|KTP|1.0|DENY|Trust Insufficient|7|
      src=10.0.1.45 suser=agent:7gen:optimized:a1b2c3d4
      act=DELETE request=/api/users/12345 reason=TRUST_INSUFFICIENT
      cs1=85 cs1Label=ActionRisk cs2=42 cs2Label=ETrust
      cs3=0.52 cs3Label=RiskFactor

   Export format (JSON):

      {
        "event_type": "ktp_authorization",
        "timestamp": "2025-11-25T14:32:07.123Z",
        "agent": "agent:7gen:optimized:a1b2c3d4",
        "action": "DELETE /api/users/12345",
        "outcome": "deny",
        "reason": "TRUST_INSUFFICIENT",
        "action_risk": 85,
        "e_trust": 42,
        "r": 0.52
      }

9.2.  Compliance Export

   Flight Recorder MUST support compliance-ready exports:

   SOC 2 format:
   -  Control evidence mapping
   -  Exception reports
   -  Access logs

   GDPR format:
   -  Data subject access requests
   -  Processing activity records
   -  Consent verification

   HIPAA format:
   -  Access audit trails
   -  Disclosure tracking
   -  Security incident documentation

9.3.  Machine Learning Pipeline

   Flight Recorder data can feed ML models for:

   1. Anomaly detection: Learn normal patterns, alert on deviation

   2. Trust prediction: Predict E_trust trajectory from current
      conditions

   3. Risk calibration: Learn optimal A values from historical outcomes

   4. Sensor validation: Detect sensor drift or manipulation

   ML export format:

      {
        "features": {
          "agent_generation": 7,
          "e_base": 87,
          "r": 0.52,
          "m": 0.45, "p": 0.78, "h": 0.82,
          "t": 0.30, "i": 0.25, "o": 0.15,
          "action_risk": 85,
          "hour_of_day": 14,
          "day_of_week": 1
        },
        "label": {
          "outcome": "deny",
          "correct": true
        }
      }

10.  Security Considerations

10.1.  Record Confidentiality

   Flight Recorder records contain sensitive information.

   Mitigations:
   -  Encrypt records at rest
   -  Encrypt records in transit
   -  Implement role-based access to queries
   -  Audit all query access
   -  Redact sensitive fields for lower-privilege queries

10.2.  Record Integrity

   Attackers may attempt to modify records to hide activity.

   Mitigations:
   -  Cryptographic chaining (Section 5.2)
   -  External chain anchors
   -  Multi-party signing of records
   -  Tamper detection alerts (Section 5.3)

10.3.  Availability

   The Flight Recorder is critical infrastructure.

   Mitigations:
   -  Multi-region replication
   -  Async write path (don't block authorization on logging)
   -  Queue-based architecture for resilience
   -  Regular backup and recovery testing

11.  References

11.1.  Normative References

   [KTP-CORE]
              Perkins, C., "Kinetic Trust Protocol - Core
              Specification", NMCITRA, November 2025.

   [KTP-ENFORCE]
              Perkins, C., "Kinetic Trust Protocol - Enforcement Layer
              Specification", NMCITRA, November 2025.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997.

Authors' Addresses

   Chris Perkins
   New Mexico Cyber Intelligence & Threat Response Alliance (NMCITRA)

   Email: cperkins@nmcitra.org