ABDULLAH AL OWASIOutcome-based assurance packs for controls, suppliers, AI systems and agentic workflows. Automation can prepare the work; accountable reviewers retain evidence acceptance, control-effectiveness, exception and risk decisions.
Commercial unit: one scoped AI system or release decision; timeline and fixed fee confirmed after evidence review.
AI register, oversight responsibilities, evaluation gaps, release criteria and change-review plan.
Commercial unit: one defined supplier or supplier group, including contract and subprocessor review criteria.
Supplier register, evidence requests, data-use and subprocessor review, treatment decisions and remediation owners.
Commercial unit: one agreed control population and reporting period, with evidence freshness and exception ownership.
Control-to-evidence map, dated test results, prioritized exceptions, retest owners and management summary.
Commercial unit: one defined GRC/AI workflow, agent population or automation boundary.
Automation inventory, decision-rights map, evidence/provenance checks, tool/data authorization boundaries, change triggers, escalation rules and a human-accountability review memo.
Confirm inputs, delivery date, commercial terms and review responsibilities before starting. Handover includes editable records and calculation notes.
The local core is available under AGPL-3.0. Engagement fees cover review, configuration, deployment, integration and handover. Agree the population, evidence inputs, timeline and acceptance criteria before delivery.
Commission an AI use-case review pack: inventory, applicability notes, evaluation gaps and a human release-review checklist. A 14-day scope starts after agreed inputs and reviewer availability are confirmed. Supplier decision packs include due-diligence gaps, concentration review, data-use terms and a decision memo. Control evidence readiness packs include request coverage, freshness exceptions and an editable treatment register. Every scope names the population, output files, review iteration and acceptance criteria. Scheduled support retainers define hours and outputs separately.
Inspect the community and enterprise boundary →Managed identity, shared storage, operated collectors and scheduled reporting require a specific deployment agreement.
AI-assisted GRC boundary
Open agent-skill libraries now automate evidence gathering, register drafting, cross-checking and audit preparation. I design the operating boundary around what automation should prepare and what a person must still own: scope, effectiveness, exceptions, risk acceptance and sign-off.
Inspect the 60-skill external benchmark at grcskills.com ↗ · Review my agentic assurance model →
Set the boundary, material risks, accountable owner and acceptance criteria.
Separate verified observations, missing evidence, exceptions and assumptions. Preserve source dates and review rationale.
Record the decision, action owner, due date and evidence required for closure. Escalate exceptions against agreed authority.
Reassess material changes, retest controls and hand over editable records with explicit calculation definitions.
An agreed register, evidence-gap list and decision memo with owners, next actions and acceptance criteria.
Share a short brief. We confirm inputs, deliverables, review responsibilities, timeline and commercial terms before starting.
The handover includes editable records, calculation notes and a walkthrough. Any follow-up support is agreed separately.
Yes. Review the professional profile and governance workflows, then use the contact brief to share the role and interview timeline.