Audit-Ready Agentic Workflows

Regulators are finished with “innovation theater.” If you operate in a regulated market and you’re deploying agents that act—issuing refunds, moving money, approving claims, routing incidents—you need a control system that stands up to supervisory scrutiny. This article translates that requirement into a concrete approach: align the EU AI Act’s obligations with ISO/IEC 27001 and run your program through policy-as-code, full-lifecycle documentation, and operational evidence. It’s how we design and ship agentic workflows at Working Excellence—without slowing the business. 

Why the EU AI Act and ISO 27001 belong in the same room 

The EU AI Act is risk-based. High-risk systems require a documented risk-management system, data governance and quality, technical documentation, event logging, human oversight, accuracy/robustness/cybersecurity, post-market monitoring, and incident reporting. ISO/IEC 27001 is outcomes-based security management: leadership, risk assessment, controls, measurement, and improvement. Pair them and you get both the what (regulatory duties) and the how (a certified management system with controls, audits, and continual improvement). 

Two additional anchors make the picture complete: 

• NIST AI RMF 1.0 for risk categories, measurement, and trustworthy-AI language your engineers can work with. 

• ISO/IEC 42001 (AI Management System) to formalize AI-specific governance alongside your ISMS. 

From policy on paper to policy in code 

The hard truth: most gaps aren’t philosophical—they’re operational. A policy written in a binder doesn’t prevent an over-entitled agent from triggering a payment. So we design for runtime enforcement: 

• Entitlements, approvals, and risk thresholds are compiled into guardrails the system can evaluate on every action. We use Open Policy Agent (OPA) so “who may do what, under which conditions” runs as code, not as a slide. 

• Every decision and tool call produces a tamper-evident trail of inputs, model versions, policies, approvals, and outputs. That trail becomes your technical documentation and post-market monitoring feed under the EU AI Act. 

• Transparency isn’t optional; we maintain Model Cards for each production model and agent, linking evaluations, known limits, rollback plans, and human-in-the-loop checkpoints. 

This is how compliance stops being a blocker and becomes a gatekeeper for safe speed. 

The control model: map obligations to controls to evidence 

Think in three columns: obligation → control → evidence. Here’s how the core EU AI Act requirements translate: 

Risk management & governance 

• Obligation: documented risk-management system, human oversight, post-market monitoring. 

• Control: ISO 27001 leadership and risk processes mapped to an AI governance cadence; NIST AI RMF functions drive risk registers with owners. 

• Evidence: risk register entries tied to monitors; board and committee readouts; incident/near-miss logs. 

Data governance & quality 

• Obligation: quality, relevance, representativeness, and bias controls. 

• Control: data lineage, access control, and minimization within the ISMS; curated evaluation datasets with drift detection. 

• Evidence: lineage graphs; data quality dashboards; access logs; drift alerts and remediations. 

Technical documentation & logging 

• Obligation: maintain comprehensive documentation and event logs. 

• Control: model/agent registries; decision logging standardized across services; retention policies integrated with ISMS. 

• Evidence: model/agent cards; log samples with timestamps and hash attestations; change history. 

Accuracy, robustness, cybersecurity 

• Obligation: appropriate performance and resilience. 

• Control: model evaluation suites; canary/blue-green releases; least-privilege identities and network isolation; vuln management aligned to ISO Annex A controls. 

• Evidence: evaluation reports; rollback tests; pen-test summaries; vulnerability remediation records; SLO reports. 

Human oversight 

• Obligation: meaningful human control over high-impact actions. 

• Control: human-in-the-loop approvals for money movement, PII disclosure, destructive ops; compensating transactions for rollback. 

• Evidence: approval artifacts; exception queues; rollback traces.

Incident reporting & post-market monitoring 

• Obligation: monitor in production, report serious incidents. 

• Control: live telemetry and thresholds; severity classification; regulator-ready runbooks. 

• Evidence: monitoring snapshots; incident tickets with root-cause; regulator submissions where applicable. 

If your footprint includes the EU, build a clean hand-off between technical logs and regulatory artifacts so your team can respond quickly to competent authority inquiries. ENISA’s work on the AI threat landscape is a useful adjunct for cyber-risk posture (ENISA AI Threat Landscape). 

What “agentic and audit-ready” looks like in practice 

CX: adverse-action transparency 

A claims agent proposes a denial. Before the decision, the system checks policy thresholds and fairness constraints; it surfaces the factors used and routes high-risk cases to human review. The approval, rationale, and notice template are logged. If challenged, you can reconstruct what was decided, why, and under which policy—a regulatory expectation across markets, supported by NIST AI RMF guidance and your ISMS. 

Security operations: containment with blast-radius limits 

A SOC agent composes an EDR isolation plan, but containment actions require a human click for crown-jewel assets. OPA policies embed asset criticality and time-bound elevation. Every step is traced. The result: faster MTTR with a defensible segregation-of-duties story tied to ISO 27001 Annex A and your CSF Detect/Respond functions. 

GRC: evidence that writes itself 

Control testing and evidence collection run continuously. The agent fetches logs, hashes artifacts, and records links in the register. Auditors receive a package with timestamps, cryptographic proofs, and associated policies. That’s post-market monitoring in motion, not a year-end scramble. 

A narrative boards and regulators will trust 

You do not need a deck of a hundred bullets; you need a story that holds. Start with your operating posture: agents are first-class identities; permissions and approvals are code; actions are observable; and rollback is rehearsed. Show where each EU AI Act duty lives in your system and how it maps to ISO 27001 controls, then demonstrate the lived reality with logs, approvals, and test results. Round it out with the one-slide portfolio—internal vs. external, everyday vs. game-changing—colored by feasibility so capital flows to what’s truly ready. 

How Working Excellence (WEX) part ners with GRC leaders 

Our team builds the control planes—identity, policy, and observability—so your agents can act safely. We instrument the documentation that satisfies the EU AI Act while nesting it inside ISO/IEC 27001, and we use NIST AI RMF to prioritize risk mitigations that actually move loss and likelihood. We’ll help you choose an initial action family, turn on policy-as-code with Open Policy Agent, stand up Model Cards, and prove value in twelve weeks—without compromising control. 

Bring one page that shows the mapping from EU AI Act obligations to ISO 27001 controls and links to living evidence: the policy file that blocked an unauthorized action, the log that shows the approval, the model card that lists known limits, the monitor that caught a drift event, and the post-market entry that led to an improvement ticket. It’s a short story with receipts—and it changes the tone of the conversation. 

If you need agentic workflows that your chief compliance officer can defend and your auditors can verify, we’ll help you ship them. Start with a Regulated AI Readiness engagement: EU AI Act scoping, ISO 27001 alignment, policy-as-code activation, and a regulator-style readout—plus a 90-day plan to scale.