Security

Map enforcement, granularity, realtime, and audit to APP 1.7 homework: inventory PEPs, PI→resource maps, revoke/sync, and audit/export for privacy-policy statements.

APP 1.7-1.9 commence 10 December 2026. Honest ADM privacy-policy disclosure needs AuthZ inventory, PI→resource maps, PEPs, realtime revoke, and decision-log evidence, plus a soft APRA parallel.

EN 18286:2026 is Europe's first harmonised standard under the EU AI Act. It turns Article 17's QMS requirements into auditable processes. Here's what it means for teams shipping AI agents.

Cheat-sheet: NIST NCCoE's four focus areas (identification, authorization, access delegation, and logging) mapped to concrete AuthZ controls. Soft alignment aid, not a certification checklist.

Treat AI agents as privileged NHIs: distinct identity, least privilege, attributable delegation, and forensic decision logs, not shared keys. Map to NIST NCCoE's identification · authorization · delegation · logging.

For human-to-agent delegation, bind human principal, agent ID, tools, data, policy decision, and HITL, not just tool traces, into an attributable audit trail.

AI agent authorization checks every agent action against policy, delegation, resource and context. Add per-action checks, trust ceilings and decision logs.

What ISO 42001 certification involves: the AI management system, accredited certification bodies, stage 1 and 2 audits, Annex A controls, and agent evidence.

EU AI Act logging requirements under Article 12 for high-risk AI systems, log retention rules (at least six months for deployers under Article 26(6)), ISO 42001 A.6.2.8, and practical logging design for AI agents.

Production authorization enforcement requires four pillars: a policy decision at every sensitive action, granular policy models, realtime revocation, and auditable decisions. Control plane to OPAL to PDP to PEP to logs is the architecture that makes them work together.

AI agent audit logs need about twelve fields: identity, delegation, tool, resource, policy, decision, HITL, and outcome.

AI agent audit trail guide: record identity, delegation, tool, resource, policy version, allow or deny, HITL approval and outcome for every agent action. Tool traces are not enough.

SOC 2 CC6.1 access control checklist for AI agents: what CC6 and CC7 assume, how agents break them, ISO 27001 mapping, and the evidence to produce.

Regulators’ “prove it” maps to PEP + PDP + decision logs: enforce in the app, decide in a local PDP synced by OPAL, and keep explainable allow/deny evidence.

DORA vs NIST on access control: both point to least privilege and attributable logs, for NHIs and AI agents too. Decision logs turn access control into evidence.

AI agents get their own IdP token, separate from the user session. OAuth scopes are too coarse for resource-level rules; check agent role at each tool call.

Your authorization model shapes least-privilege evidence in access reviews. Role assignments, attribute conditions, and relationship tuples each prove different things.

Hybrid authorization by default: RBAC for org roles, ABAC for context, ReBAC for sharing graphs—one check API across models. How to pick a stack before role explosion, and why least privilege needs explainable entitlements.

AuthN vs AuthZ: authentication (AuthN) checks who a user is, and authorization (AuthZ) decides what they can do. Side by side comparison with real SaaS examples.

SOC 2-style access reviews need more than IdP login events. Explainable allow and deny decision logs — decision provenance from identity to policy to resource — are the authorization evidence that makes application-level access controls concrete.

Every authorization decision should be explainable. Authorization observability is not more logs — it is decision provenance. Here is how to build an audit trail that answers why access was allowed or denied.

Fine-grained authorization belongs in a dedicated control plane—not scattered if-statements. Learn when to externalize authorization and stop rebuilding DIY RBAC in application code.

MCP servers are now operational control surfaces for cloud and API platforms. This article explains the trust model, control stack, and audit architecture enterprises need before AI agents can safely execute infrastructure actions.

A practical review guide for security, privacy, and procurement teams evaluating whether an MCP gateway can meet SOC 2, HIPAA, and privacy requirements — with concrete examples from Permit MCP Gateway.