KLA vs Credo AI
Credo-style platforms are strong for inventories, assessments, and governance artifacts. KLA focuses on runtime workflow governance + evidence exports tied to real executions.
Tracing is necessary. Regulated audits usually ask for decision governance + proof: enforceable policy gates and approvals, packaged as a verifiable evidence bundle (not just raw logs).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
Ultimo aggiornamento: 17 dic 2025 · Versione v1.0 · Non costituisce consulenza legale.
A chi è rivolta questa pagina
Un inquadramento dal punto di vista dell'acquirente (non una denigrazione).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
A cosa serve realmente Credo AI
Basato sulla sua funzione principale (e dove si sovrappone).
Credo AI is built for program governance: inventories, assessments, policies, and standardized transparency artifacts/reports that help coordinate responsible AI work across stakeholders.
Sovrapposizione
- Both can support compliance teams producing artifacts and coordinating reviews.
- Both can improve audit readiness: Credo through program-level workflows, KLA through runtime decision evidence and exports.
- Many regulated teams use both: a governance system of record plus a runtime evidence layer for high-risk workflows.
In cosa eccelle Credo AI
Riconosciamo i punti di forza dello strumento, distinguendoli dai deliverable di audit.
- Governance program scaffolding (inventories, assessments, policies, standardized reporting).
- Helping teams coordinate compliance work across many systems and stakeholders.
Dove i team regolamentati hanno ancora bisogno di un livello aggiuntivo
- Runtime capture of "what actually happened" in an agent workflow (actions taken, approvals, overrides, and context).
- Decision-time enforcement evidence at checkpoints (block/review/allow) for high-risk actions.
- A verifiable evidence pack export tied to executions (manifest + checksums) rather than only program artifacts.
Pronto all'uso vs da costruire
Una suddivisione equa tra ciò che è disponibile come workflow principale e ciò che va assemblato tra più sistemi.
Pronto all'uso
- Program governance workflows: system inventories, risk assessments, policies, and reporting.
- Standardized artifacts for transparency and internal/external review.
- Coordination across stakeholders and evidence mapping at the program level.
Possibile, ma lo costruite voi
- Runtime instrumentation and collection for agent workflows (traces, actions, approvals) across teams and systems.
- Decision-time gates and approval queues for high-risk actions (with escalation and overrides).
- Evidence bundle packaging that maps runtime evidence to Annex IV/oversight deliverables, with verification artifacts.
- Retention/integrity posture for long-lived audit evidence and exports.
Esempio concreto di workflow regolamentato
Uno scenario che mostra dove si colloca ciascun livello.
Governance program + one high-risk workflow
A compliance team runs inventories and assessments for many AI systems. For one high-risk agent workflow (e.g., account closure recommendations), auditors also want runtime decision evidence: who approved, what policy applied, and what happened in production.
Dove Credo AI è utile
- Track inventories, owners, and risk assessments across systems.
- Produce standardized reports and transparency artifacts for stakeholders.
Dove KLA è utile
- Enforce decision-time gates on the workflow (block/review/allow) with role-aware approvals.
- Capture execution evidence (actions, approvals, sampling outcomes) tied to the exact versions running in production.
- Export a verifiable evidence pack suitable for auditor handoff (manifest + checksums).
Decisione rapida
Quando scegliere l'uno o l'altro (e quando acquistare entrambi).
Scegliete Credo AI quando
- You need a governance system of record for assessments and policy workflows.
- You are standardizing risk and compliance reporting across the organization.
Scegliete KLA quando
- You need a runtime control plane around agent workflows (gates + sampling + oversight).
- You need to export audit-ready evidence bundles tied to actual executions.
Quando non acquistare KLA
- You only need program governance artifacts and do not need runtime workflow controls or evidence exports.
Se acquistate entrambi
- Use Credo AI to manage inventories, policies, and assessments.
- Use KLA to generate runtime evidence and deliver verifiable exports for audits.
Cosa KLA non fa
- KLA is not designed to replace a governance system of record for inventories, assessments, and policy workflows.
- KLA is not a request gateway/proxy layer for model calls.
- KLA is not a prompt experimentation suite.
Il ciclo di controllo di KLA (Governare / Misurare / Dimostrare)
Cosa significa "evidenze di livello audit" in termini di funzionalità di prodotto.
Governare
- Checkpoint policy-as-code che bloccano o richiedono revisione per le azioni ad alto rischio.
- Code di approvazione basate sui ruoli, escalation e override registrati come record decisionali.
Misurare
- Revisioni a campione basate sul rischio (baseline + intensificate durante incidenti o dopo modifiche).
- Tracciamento dei near-miss (passaggi bloccati o quasi bloccati) come segnale di controllo misurabile.
Dimostrare
- Traccia di audit tamper-proof, append-only, con timestamping esterno e verifica di integrità.
- Bundle di esportazione dall'Evidence Room (manifesto + checksum) verificabili in modo indipendente dagli auditor.
Nota: alcuni controlli (SSO, workflow di revisione, finestre di conservazione) dipendono dal piano. Consultate /pricing?ref=confronto.
Checklist RFP (scaricabile)
Un artefatto di procurement condivisibile.
# Checklist RFP: KLA vs Credo AI Utilizzate questa checklist per valutare se gli strumenti di "osservabilità / gateway / governance" coprono effettivamente i deliverable di audit per workflow regolamentati basati su agenti. ## Requisiti essenziali (deliverable di audit) - Mappatura delle esportazioni in stile Annex IV (campi della documentazione tecnica -> evidenze) - Registri di supervisione umana (code di approvazione, escalation, override) - Piano di monitoraggio post-market + sampling policy basata sul rischio - Traccia di audit tamper-evident (verifiche di integrità + conservazione a lungo termine) ## Chiedete a Credo AI (e al vostro team) - Can you enforce decision-time controls (block/review/allow) for high-risk actions in production? - How do you distinguish “human annotation” from “human approval” for business actions? - Can you export a self-contained evidence bundle (manifest + checksums), not just raw logs/traces? - What is the retention posture (e.g., 7+ years) and how can an auditor verify integrity independently? - How do you connect program artifacts to runtime execution evidence for audits (approvals, enforcement, and exports)?
Fonti
Riferimenti pubblici utilizzati per mantenere questa pagina accurata e imparziale.
Nota: le funzionalità dei prodotti cambiano. Se notate informazioni obsolete, segnalatelo tramite /contact?ref=confronto.