Policy Compliance Log

The Timestamp is a high-precision, coordinated universal time (UTC) record of when the policy evaluation occurred. The Sequence ID is a unique, monotonically increasing identifier that establishes the exact chronological order of events, critical for reconstructing causality and preventing log injection attacks.

• Example Timestamp Format: 2024-05-15T14:30:22.123456Z
• Sequence ID Role: Guarantees total order of events, even when timestamps have millisecond-level collisions.
• Integrity Use: Used in cryptographic chaining (e.g., hash chains) to make log tampering evident.

This component definitively links the log entry to the specific autonomous agent instance and its execution session. It answers 'who' performed the evaluated action.

• Agent ID: A unique identifier for the agent's deployed version and configuration (e.g., agent-purchase-approval-v2.1).
• Session ID: A unique identifier for the specific execution context or user interaction (e.g., session-abc123-def456).
• Purpose: Enables cross-session auditing and aggregates all compliance events for a single agent decision-making chain.

The core of the entry specifies the exact governance policy and the specific rule within it that was evaluated. This provides the legal or operational framework against which compliance was measured.

• Policy ID/Name: References the formal policy document (e.g., EU_AI_ACT_CHAPTER_3, Company_Spending_Policy_v5).
• Rule ID/Logic: The precise conditional logic or rule identifier that was executed (e.g., RULE_MAX_APPROVAL_AMOUNT: 10000, CHECK_DATA_RETENTION_PERIOD).
• Versioning: Includes policy version to account for rule changes over time.

A snapshot of the agent's intended action and the relevant input data that triggered the policy check. This provides the factual basis for the evaluation.

• Action Description: The agent's planned operation (e.g., execute_payment, generate_medical_summary, deny_loan_application).
• Relevant Inputs: The specific data fields used in the policy evaluation, often sanitized or hashed for privacy (e.g., transaction_amount: 12500, user_age: 17, document_classification: CONFIDENTIAL).
• Role: Enables forensic state reconstruction to re-evaluate the policy with the same inputs.

Records the binary outcome of the policy check (COMPLIANT / NON_COMPLIANT) and the system-generated justification. The justification is the audit trail's reasoning, showing why the result was reached.

• Result: The deterministic output of the policy rule engine.
• Justification: A machine-readable trace of the rule logic evaluation (e.g., FAIL: transaction_amount (12500) > max_approval_amount (10000)).
• Enforcement Action: May record the subsequent system action triggered by the result (e.g., ACTION: ESCALATE_TO_MANAGER, ACTION: BLOCK_AND_ALERT).

A cryptographic seal that provides non-repudiation and tamper-evidence for the log entry. This transforms the record into a verifiable action record.

• Digital Signature: A signature generated using a private key (from the agent's secure module or a central log service) over a hash of the entry's core fields.
• Hash Chain Link: The entry's hash may be linked to the previous entry's hash, creating an immutable action ledger.
• Public Verification: Allows auditors to verify the entry's authenticity and integrity using a corresponding public key, providing a deterministic execution proof.

An immutable, chronological record of all actions, decisions, and state changes performed by an autonomous agent. It is the foundational data source from which specialized logs like the Policy Compliance Log are derived. An audit trail is designed for compliance verification and forensic analysis, providing a complete replayable history.

• Core Function: Serves as the system of record for all agent activity.
• Key Attribute: Chronological ordering is critical for causality analysis.
• Use Case: Essential for reconstructing events during a security incident or compliance investigation.

A logging standard that provides cryptographic proof of an action's origin and integrity. It prevents the acting agent or system from later denying its involvement. This is achieved by incorporating digital signatures and secure hashing into each log entry.

• Mechanism: Each log entry is signed with a private key unique to the agent or its secure module.
• Outcome: Creates legally admissible evidence of action provenance.
• Contrast with Policy Log: While a Policy Compliance Log records what policy was checked, a Non-Repudiation Log proves who or what performed the check and that the record is authentic.

A directed graph data structure that models the cause-and-effect relationships between an agent's observations, internal states, decisions, and executed actions. It moves beyond a linear log to explicitly map why an action occurred.

• Structure: Nodes represent states or actions; edges represent causal links.
• Analytical Value: Enables root-cause analysis by tracing backward from an undesirable outcome.
• Integration: Can be generated by processing a raw Audit Trail and Policy Compliance Log to infer and document causal chains.

A predefined point in an agent's execution flow where its state and pending actions are evaluated against regulatory or policy rules before proceeding. It is the procedural mechanism that triggers an entry in the Policy Compliance Log.

• Architectural Pattern: Often implemented as a dedicated function or service call within the agent's code.
• Input: Takes the agent's current context and proposed action.
• Output: Returns a pass/fail/conditional result, which is then logged. This creates a deliberate pause for governance.

A logging technique that uses cryptographic hashes (e.g., in a Merkle Tree or hash chain) to make any unauthorized alteration or deletion of log entries immediately detectable. It ensures the integrity of the audit trail and Policy Compliance Log.

• How it Works: The hash of each new log entry incorporates the hash of the previous entry, creating a chain. Altering any past entry breaks the chain.
• Key Benefit: Provides confidence that the log history has not been modified, which is critical for legal and compliance audits.
• Foundation: A prerequisite for generating a Deterministic Execution Proof.

A structured document or data model that maps high-level business requirements, user intents, or regulatory clauses to the specific agent actions, decisions, and data sources that fulfilled them. It is an analytical tool built from logs.

• Purpose: Provides auditability for decision justification to external regulators or internal auditors.
• Construction: Often generated by querying and correlating the Policy Compliance Log, Audit Trail, and system documentation.
• Output: Answers the question: "Which agent actions and policy evaluations demonstrate compliance with requirement X?"