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Kakunin vs. the Alternatives

Why purpose-built AI agent compliance infrastructure outperforms DIY builds, generic monitoring, and manual governance under MiCA and EU AI Act scrutiny.

Supported~ Partial / requires custom build Not supported
FeatureKakuninDIY BuildGeneric MonitoringManual Governance
Per-instance X.509 agent identity
Cryptographic identity bound to each agent deployment, not a shared API key or service account.
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KMS-backed private key custody
Private key generated in AWS KMS HSM and never exposed to application code.
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Certificate-embedded scope policy
Authority limits are signed into the certificate and cannot be changed without CA reissuance.
Scope enforcement independent of LLM
Scope is checked at the tool layer before any action executes.
Behavioral baseline profiling
Per-agent baseline established from observation and scored against recent deviation patterns.
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Per-action anomaly scoring
Every action produces a normalized risk score against the behavioral baseline.
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Automatic certificate revocation
Score ≥ 0.85 triggers instant revocation with no human required.
Pre-revocation human review window
Score 0.75–0.84 triggers a configurable grace period before auto-revocation.
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WORM audit log
PostgreSQL rules block UPDATE and DELETE on audit_log.
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Cryptographic action signatures
Each logged action includes a KMS signature over the payload.
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MiCA Articles 67–72 compliance evidence
Compliance report exports the evidence package regulators request.
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EU AI Act Annex III technical documentation
Article 11 package generated on demand with risk and oversight records.
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LangChain / AutoGen / CrewAI integration
Native SDK integrations support common agent orchestration stacks.
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Time to first agent registered
5 minutes via API or CLI; DIY requires building CA and enforcement plumbing from scratch.
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Why Not Build It Yourself?

CA infrastructure is non-trivial

Building a certificate authority requires KMS key policies, certificate profile design, OCSP responder, revocation propagation, and renewal workflows. Most teams underestimate the ongoing maintenance.

Behavioral baselines require statistical modeling

Anomaly scoring that avoids false positives on variable workloads while catching real threats requires percentile tracking, weighted deviation models, and configurable thresholds per agent type.

WORM logs require database-level enforcement

Audit trail integrity under MiCA and the EU AI Act requires that even admin code cannot modify records. Postgres-rule WORM enforcement is straightforward to implement but easy to get wrong.

Compliance evidence must be regulator-ready

MiCA Article 71 and EU AI Act Article 11 require structured evidence packages on request. Generating these from raw logs is an engineering project — Kakunin generates them on demand.

Ship compliance with less guesswork

If your team is evaluating options, the real question is whether you want a bundle of point tools or a single compliance layer that already understands identity, scope, risk, and evidence.

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