This article is published by J-10, Jalubro's proprietary governance enforcement platform. It is part of a series exploring how regulated enterprises can enforce compliance inside operational workflows. To learn how Jalubro's advisory and implementation services support governed enterprise operations, visit our services page.

The compliance operating model most enterprises use was designed for a world that no longer exists.

Most enterprise compliance operating models were designed when decisions were made by people, in structured processes, within defined departmental boundaries. The compliance team wrote policies. The business followed them. Internal audit tested whether the policies were being followed.

That model assumed three things. That humans make all material decisions. That decisions happen within a single system or department. And that periodic testing is sufficient to detect non-compliance before it causes material harm.

None of these assumptions hold.

AI tools are generating contract clauses, producing regulatory analyses, triaging legal requests and drafting procurement recommendations. Automated workflows are processing approvals, routing invoices and executing business rules without human intervention at every step. Decisions that were once contained within a single department now flow across legal, procurement, finance, compliance and commercial through interconnected systems.

Why incremental adjustments are not sufficient

Most enterprises have responded to AI and automation by extending their existing compliance model. They add an AI acceptable use policy. They include AI risk in the risk register. They add AI-related questions to the control testing programme. They schedule AI training for staff.

These are reasonable steps within the existing model. They are not sufficient because the existing model is structurally unable to govern the way the enterprise now operates.

Policies without enforcement cannot govern AI. An acceptable use policy has no enforcement mechanism. The policy exists in a document. The AI interaction happens in a workflow. Nothing connects the two.

Periodic testing cannot keep pace with automated decisions. Quarterly testing reviews a fraction of a fraction. Non-compliance that occurs between testing cycles is invisible.

Departmental compliance cannot govern cross-functional workflows. A compliance model organised by function cannot govern a workflow that crosses all three. The connections between the segments, where the highest risk lives, are nobody's responsibility.

Risk registers cannot govern operational decisions. Adding AI to the risk register acknowledges the risk. It does not mitigate it.

The four pillars of a modern compliance operating model

Pillar 1: Policy-to-control conversion

The traditional compliance model treats policies as documents that humans read and follow. The modern model treats policies as rules that systems enforce.

Delegation of authority becomes a dynamic rule engine that validates every approval in every system against the live delegation matrix in real time. Approved clause libraries become validation services. Data classification policies become input gates. Regulatory obligations become automated monitoring rules.

The compliance team still owns the policy. But the policy is no longer a document that people read. It is a control that systems enforce.

Pillar 2: Enforcement at the point of decision

The traditional model discovers non-compliance after the fact. The modern model prevents non-compliance at the point of decision.

This means governance controls are embedded inside the operational workflows where decisions happen. When a procurement approval is submitted, the delegation is enforced before the approval is processed. When an AI tool generates a contract clause, the clause is validated before it enters the CLM. When a user attempts to provide data to an AI tool, the data classification is enforced before the data reaches the tool.

Prevention replaces detection. Real-time replaces retrospective. Continuous replaces periodic.

Pillar 3: Cross-functional governance

The traditional model assigns compliance responsibility by function. The modern model requires governance that follows the workflow, not the organisational chart.

A vendor contract originates in legal. When executed, it creates a supplier commitment in procurement. That commitment triggers an invoice stream in finance. The governance controls that apply to the contract must also apply to the procurement commitment and the financial obligation.

Cross-functional governance means that a single policy, enforced consistently, follows the data and the decision across every system and every function it touches.

Pillar 4: Continuous evidence as a by-product

The traditional model collects evidence through control testing. The modern model produces evidence automatically, as a by-product of governance enforcement.

When a governance control is enforced at the point of decision, the enforcement itself generates evidence. Every governed decision produces a timestamped, attributable, tamper-proof evidence record. The evidence is produced continuously as operations run. It does not need to be gathered after the fact.

Designing the model: practical steps

Step 1: Map the decision landscape. Identify every operational workflow where material decisions are made. For each workflow, identify which governance policies apply and how they are currently enforced.

Step 2: Prioritise by risk and volume. Prioritise the workflows where the combination of decision volume, decision value and regulatory exposure is highest.

Step 3: Convert policies to controls. For each prioritised workflow, convert the relevant governance policies into executable controls. Define the enforcement logic.

Step 4: Deploy the enforcement layer. Implement a governance enforcement platform that connects to your operational systems and enforces the converted controls at the point of decision.

Step 5: Connect to your GRC. The enforcement layer feeds continuous evidence back to the GRC, keeping the system of record up to date with real operational data.

Step 6: Establish governance over the governance. The compliance operating model itself needs governance. Define processes for updating controls when policies change, onboarding new systems, and reviewing effectiveness.

The role of the compliance function in the new model

The compliance function does not become smaller in this model. It becomes more strategic.

When evidence collection is automated and enforcement is embedded, the compliance team is freed from the operational burden of testing and evidence gathering. Their role shifts to governance design: defining which policies need to be enforced, how enforcement logic should work, where new risks are emerging, and how the governance framework should evolve.

The CCO becomes the architect of the enterprise's governance operating model, not the manager of a testing programme.

How J-10 enables the modern compliance operating model

J-10 is the governance enforcement layer that the modern compliance operating model requires.

J-10 converts governance policies into executable controls and enforces them at the point of decision inside your operational systems. It works across your ERP, CLM, procurement platform, matter management system, AI tools and finance systems. It produces continuous, audit-grade evidence as a by-product of enforcement. The evidence feeds back to your GRC platform, keeping your system of record aligned with operational reality.

J-10 does not replace your compliance programme. It gives your compliance programme the enforcement capability it has always needed but never had.

To learn more about building a modern compliance operating model with J-10, visit j10.ai or contact the Jalubro team to book a briefing.