Business workflows, incident response, SOAR automation, and governance processes.
Stand up an AI/HAI Process Assurance program that discovers, inventories, and strategically governs all business workflows that embed AI/HAI, with shadow-AI-in-processes prevention as the primary L1 outcome and a defensible risk-tier rubric as the primary L2 deliverable.
Explore →Publish the priority policies and compliance map that make the AI/HAI Process Assurance program enforceable, so every AI-embedded business workflow the organization operates is governed by a documented set of rules, gated before it goes live, and defensible to auditors, regulators, and affected individuals.
Explore →Build the AI-assurance workforce literacy every process owner, product manager, operations manager, and business analyst touching AI-embedded workflows needs and the practitioner skills the smaller population performing FRIA composition, HITL design assessment, process intake review, and workflow-archetype threat modeling must have, with shadow-AI-in-processes awareness as the primary L1 cultural outcome.
Explore →Build and maintain a reusable threat library for the business workflows that embed AI/HAI, one archetype-level threat model per workflow type, so every workflow entering the SM inventory produces a threat snapshot in minutes rather than a blank-page exercise.
Explore →Translate the threats from TA-Processes and the policies from PC-Processes into a reusable Requirements Pack for AI/HAI-embedded business workflows the organization operates, a base set plus per-archetype deltas, so every workflow entering production carries a testable Requirements-Evidence Map (REM) rather than a blank slate.
Explore →Publish the reference patterns for safely designing each AI/HAI-embedded workflow archetype the organization operates, so process designers have a vetted "green path" that already implements SR-Processes requirements and contains the threats identified by TA-Processes.
Explore →Operate the design checkpoint between intake approval and build-out for every new AI-embedded business workflow, confirming the proposed design follows the applicable SA-Processes reference pattern, covers the SR-Processes requirements pack, satisfies applicable compliance obligations, and documents residual risks before the workflow goes live.
Explore →Verify, at go-live and on a recurring cadence, that the actually-running AI-embedded business workflow matches the design approved at DR, and that it stays there as the workflow and its underlying AI components evolve.
Explore →Prove that every AI-embedded business workflow behaves correctly under adversarial and failure conditions, by running a foundational per-archetype test battery, maintaining versioned regression corpora, and escalating to scheduled red-team exercises and continuous adversarial testing at higher maturity levels.
Explore →Run the single unified backlog for AI/HAI issues across the Processes domain, findings from TA-Processes threat snapshots, SR-Processes REM gaps, DR-Processes conditions, IR-Processes drifts, ST-Processes failures, ML-Processes detections, and external advisories, with a tier-calibrated incident playbook containing AI-specific workflow containment plays, and regulatory SLA tracking covering GDPR Arts. 22/33, EU AI Act Arts. 26/50/73, HIPAA, FCRA, FINRA, NYC LL 144, CO SB-21-169, and sector-specific obligations.
Explore →Harden the workflow operational envelope, the controls around how a business workflow embedding AI/HAI executes, who can change it, and what data flows through it, so each workflow runs within a least-privilege, observable, and auditable boundary and unsanctioned workflow modifications or shadow AI insertions are detectable before they affect outputs.
Explore →Establish the logging baseline per AI/HAI process archetype, operate a small high-signal detection set targeting the top TA-Processes threats, and produce the evidence trail that proves EU AI Act deployer duties, GDPR processor obligations, and ISO/IEC 42001 AIMS requirements for business workflows embedding AI/HAI, on demand, inside a published SLA.
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