Agent Identity & Access (IdP)
50% of use casesPer-agent identities, short-lived scoped tokens, OBO flow enforcement — the identity substrate of agentic zero trust.
- practice-derived1 triggering use case require Agent Identity & Access (IdP)
Regulated AI Navigator
Turn an AI use case into its EU AI Act risk class, the regulations it triggers, the obligations, the architecture and the evidence you owe — in about two minutes.
Community-curated knowledge graph, peer-reviewed by experts across law, engineering and governance. Every change traceable →
Every regulation in the graph resolves into concrete technical components — through an article obligation, a control objective, a design pattern or an evidence artefact that a component must produce. Pick a regulation to see its technical surface, how each component is derived, and in which of the triggering use cases it is actually part of the required stack. The gap counts show where the graph reaches a component that no use case yet requires — those are open contribution targets, not settled answers. Open the full graph →
Access and re-use rights for (industrial) data, switching and interoperability duties — affects data sourcing for RAG pipelines and connected products.
Per-agent identities, short-lived scoped tokens, OBO flow enforcement — the identity substrate of agentic zero trust.
Agents never hold target-system keys; the gateway injects centrally managed credentials after policy checks.
Provenance tracking of datasets, features and embeddings; write-time attribution (source, actor, timestamp, confidence).
Policy-as-code decision point (PDP) with enforcement points (PEP) in front of every tool call: versioned policies in Git, microsecond evaluation, typed action schemas — authorization decided outside the model's reasoning space, never in the prompt.
Operator stop controls and degraded-mode fallbacks; real-time override (veto) channels for HOTL operation.
Compliant collection of public marketplace signals with robots/ToS policy checks, rate governance and provenance capture per observation.
Continuous evaluation against golden sets and sampled human verdicts; raises drift alerts and feeds the recertification cycle.
Abstraction layer decoupling application logic from model providers: dynamic routing on capability, cost, latency SLA and regulatory constraint (sensitive-data classes pinned to ZDR private/VPC endpoints or on-prem open-weight instances); real-time health monitoring with automatic fallback to secondary endpoints or local fine-tuned models on outage/latency spikes. Discharges resilience duties (DORA-class), prevents provider lock-in, and makes model deprecations a routing-table change instead of a re-architecture. Router decisions are logged into the decision trace — model version per event is an audit-packet field.
Hierarchical trace spans for every sub-task, prompt, retrieved document and API call — the reconstructible decision path for Art. 12/14 and PLD disclosure.
Software bill of materials incl. model weights and datasets; automated vulnerability patching pipeline.
Verified boot chain and hardened runtimes for edge/IoT deployments per CRA security-by-design.
One procedure reconciling AI Act Art. 73, GDPR Art. 33 (72h), DORA and NIS2 (24h/72h) timelines and recipients.
Cost/iteration caps, loop detection, anomaly-triggered mandatory approval (CodeBuddy 'suspicious command override').
Append-only, hash-chained audit vault (WORM object-lock storage, AES-256 at rest, TLS 1.3 in transit). Guarantees tamper-evidence within the organization's trust domain — which stops your own team, but not an admin who can rebuild the vault. Pair with an external trust anchor and key ceremonies outside the operating team for evidence that holds against the insider scenario.