Standards and Regulatory Alignment¶

Aevum does not invent its own audit record format. The sigchain, the pre-call envelope, and the consent model are designed to implement emerging standards rather than compete with them. This page maps Aevum's primitives to the relevant standards.

OpenTelemetry GenAI semantic conventions¶

Aevum's AuditEvent fields map to the OTel GenAI semantic conventions. Custom OTel integration can be built using the audit event structure.

Map AuditEvent fields to OTel GenAI attributes in your custom integration:

# Example custom OTel mapping from AuditEvent fields
attrs = {
    "gen_ai.content.reference": audit_event.audit_id,
    "aevum.gen_ai.prompt_hash": audit_event.payload.get("prompt_hash"),
    "aevum.gen_ai.response_hash": audit_event.payload.get("response_hash"),
    "gen_ai.operation.name": audit_event.payload.get("operation", "chat"),
    "gen_ai.system": audit_event.payload.get("provider"),
}

Privacy posture: Aevum uses OTel's "external storage" mode. Raw prompts and responses are never emitted. The audit_id is the reference — it points to the tamper-evident, signed record in the episodic ledger. The consumer retrieves content via engine.replay(audit_id=...) with a valid consent grant.

OTel fields captured automatically by LlmComplication:

OTel GenAI key	Aevum source
`gen_ai.request.model`	LiteLLM model string
`gen_ai.response.model`	LiteLLM response object
`gen_ai.system`	Provider prefix from model string
`gen_ai.operation.name`	Always `"chat"` for completions
`gen_ai.conversation.id`	Caller-supplied `model_context`

IETF Agent Audit Trail¶

The IETF Agent Audit Trail draft (draft-sharif-agent-audit-trail-00) specifies an audit record format using SHA-256 per RFC 8785 JCS canonicalization with optional ECDSA signatures. It explicitly cites EU AI Act Article 12 as motivation.

The episodic ledger exports a parallel IETF-format chain:

# Build IETF AAT records from AuditEvent fields (custom adapter)
# See docs/learn/quickstart.md for current examples
events = []  # use engine.replay() for current API
records = [
    {
        "agent_id": e.actor,                  # → actor
        "action_type": e.event_type,          # → event_type
        "outcome": e.payload.get("outcome", "success"),
        "timestamp": e.valid_from,            # ISO 8601
        "aevum:audit_id": e.audit_id,         # cross-reference
    }
    for e in events
]

Two chains coexist: Aevum's internal sigchain uses SHA3-256 for tamper detection (engine.verify_sigchain()). The IETF export produces a parallel SHA-256 JCS chain for external interoperability. Both are derived from the same AuditEvent data.

Draft status

draft-sharif-agent-audit-trail-00 expires September 2026 and has not been adopted as an IETF standard. Aevum implements against the draft format for forward compatibility. Field names may change before standardization.

EU AI Act Article 12¶

Article 12 of Regulation (EU) 2024/1689 requires high-risk AI systems to "technically allow for the automatic recording of events (logs) over the lifetime of the system."

What Aevum provides:

Art. 12 requirement	Aevum implementation
Automatic recording	Every engine call appends to the episodic ledger unconditionally (Barrier 4)
Tamper-evident	Ed25519 + SHA3-256 hash chain; `verify_sigchain()` detects any modification
Traceable to inputs	Pre-call context stored in `AuditEvent.payload` with `payload_hash`
Actor identity	`actor` field required, non-empty (enforced by `AuditEvent.__post_init__`)
Purpose declaration	`purpose` field required on all data operations

Retention: Art. 26(6) requires a minimum six months (183 days). Aevum records are append-only by design (Barrier 4) — deletion requires external database action outside the kernel.

Reference policy bundle: See packages/aevum-core/policies/opa/eu-ai-act-art12.rego for an OPA policy that gates operations on Art. 12 session requirements. [Reference example only — consult legal counsel before production use.]

Deployment note: Art. 12 requires that the recording happens automatically and cannot be disabled. Pattern 2 or Pattern 3 from Deployment Patterns provides the infrastructure-level guarantee that the recording path is always traversed.

NIST AI RMF¶

NIST AI RMF 1.0 (January 2023) and the Generative AI Profile (AI 600-1, July 2024) organize AI risk management across four functions: GOVERN, MAP, MEASURE, MANAGE.

RMF function	Aevum primitive
GOVERN — policies and accountability	Cedar / OPA policy bundles; five barriers as unconditional governance layer
MAP — risk identification	`review` function (GOVERN verb) with L1–L5 autonomy levels
MEASURE — risk measurement	Episodic ledger provides the evidence base; `replay` enables incident reconstruction
MANAGE — risk mitigation	Consent enforcement (Barrier 3); classification ceiling (Barrier 2); crisis detection (Barrier 1)

The NIST RMF Agentic Profile (CSA Lab Space, 2025) extends RMF 1.0 to tool-using agents. Aevum's consent model maps to its "minimal permission" and "human-in-the-loop" controls.

OWASP Top 10 for Agentic Applications¶

OWASP Top 10 for Agentic Applications 2025

Risk	Aevum coverage	Notes
Goal / task hijacking (prompt injection)	Partial — records context, does not filter	Use Lakera Guard or NeMo Guardrails for content filtering
Tool misuse (unauthorized tool calls)	Addresses — consent required per operation; denials logged	Combine with MCP gateway for mandatory enforcement
Identity / permission abuse	Addresses — `actor` required; consent scoped by `grantee_id` and `purpose`	OWASP ASI03: Cryptographic agent identity can be implemented via a custom SPIFFE complication using a SPIFFE Workload API.
Memory / knowledge poisoning	Addresses — Barrier 3 blocks writes without consent; classification ceiling limits read access
Code / plugin execution	Out of scope	Use gVisor, Firecracker, NVIDIA OpenShell
Insecure agent communications	Out of scope	Use TLS/mTLS on the transport layer
Data exfiltration	Partial — classification ceiling (Barrier 2) restricts data access	Does not redact at the model boundary
Insufficient human oversight	Addresses — `review` function (GOVERN verb) with configurable autonomy levels L1–L5
Supply chain compromise	Out of scope	Use SBOM / SLSA / Sigstore on releases
Rogue / shadow agents	Partial — consent required for all kernel operations; sigchain creates accountability trail	Does not prevent agents from bypassing the kernel

External chain verification¶

Sigstore Rekor v2 / Transparency logs: Chain checkpoints can be submitted to an external Signed Tree Head log via a custom integration, enabling adversarial-resistant verification. Without this, tamper-detection requires the verifier to trust the operator. With this, tampering is detectable by any party with access to the Rekor log.

What Aevum does not cover¶

Regulatory scope is as important as coverage:

Aevum produces evidence — it does not interpret it. The episodic ledger is the technical artifact. A compliance program, legal team, or compliance-reporting tool interprets whether that artifact satisfies a specific regulatory obligation.

Aevum does not generate compliance reports. It provides the signed, tamper-evident records that feed a compliance report.

Sector-specific overlays are not built in. HIPAA minimum-necessary, FDA SaMD logging requirements, FINRA trade-surveillance requirements, and NIS2 incident-reporting timelines all layer on top of the general AI-governance requirements covered here. Reference policy bundles for HIPAA are in packages/aevum-core/policies/opa/.