# How Should Indonesia Build an AI Risk Register in 2026?

infonesia.fyi · October 1, 2026

> What Is an AI Risk Register? An AI risk register is a controlled record of every material AI system used by an Indonesian organisation, the business...

## What Is an AI Risk Register?

An AI risk register is a controlled record of every material AI system used by an Indonesian organisation, the business activity it supports, the people affected, the possible failure modes, and the controls assigned to those risks. It is more than a spreadsheet of vendor names: an effective register connects system ownership to applicable laws, decision rights, testing evidence, incidents, and remediation deadlines. This matters because AI risk changes when a model, prompt, data source, integration, or intended purpose changes, even if the original procurement remains unchanged. A useful register therefore records versions and review dates rather than treating a system as permanently approved.

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For Indonesian teams, the document should cover internal tools, employee-facing copilots, customer chatbots, credit or fraud models, recommendation engines, biometric systems, software with generative features, and third-party APIs. It should also include AI used informally by staff, because an unapproved public chatbot can expose customer data, source code, contracts, or personal information just as readily as an enterprise deployment. The immediate objective is not to label every model as high risk; it is to identify where an inaccurate, unlawful, unsafe, or opaque outcome could cause material harm. The register then gives risk owners a defensible way to prioritise limited controls, budget, and review capacity.

## Why Indonesian Organisations Need One Now

Indonesia’s regulatory environment combines sector-specific supervision, personal-data obligations, electronic-system requirements, consumer protection, employment rules, and existing financial, health, or telecommunications governance. The Personal Data Protection Law, commonly known as UU PDP, imposes data-processing duties, while government regulation No. 71 of 2019 on Electronic Systems and Transactions provides a broader operating framework for electronic systems. Financial institutions may face additional requirements from OJK, while banks, insurers, payment firms, and capital-market participants operate under sector rules that can be stricter than general governance expectations. As of October 2026, organisations should verify current implementing measures and regulator guidance rather than rely on a 2024 compliance map.

Global developments also affect Indonesian operations. The European Union AI Act applies to certain providers and deployers connected to EU markets, with prohibited-practice and AI-literacy provisions already relevant and higher-risk obligations entering the framework in phases from 2025 through 2027. Supply chains add another layer: an Indonesian company may build a service for an EU client using a US model provider and European subprocessors, creating contractual and extraterritorial questions even when all servers are located in Indonesia. This does not mean every organisation must copy the EU classification system. It does mean multinational customers may require equivalent inventories, documentation, notices, and control evidence.

A register addresses a governance weakness exposed by rapid experimentation. Research published around Indonesia’s large-scale public-sector AI experiment indicates that deployment capacity can grow faster than safety supervision. Meanwhile, recent incidents involving sexualised deepfakes demonstrate that general-purpose AI tools can generate abusive imagery involving adults and minors without a conventional enterprise software defect. For boards and executives, the central question is therefore not whether AI is accurate on average, but whether the organisation can identify harmful cases, stop them promptly, explain decisions, and notify affected parties where required.

## Which Risks Should Be Recorded?

Risk classification should begin with intended use and potential harm, not with the technical label attached by a vendor. A chatbot that drafts internal code is different from one that recommends credit limits, and both may use the same underlying model. Record at least the system owner, business purpose, users, affected groups, data categories, model or vendor, deployment geography, suppliers, autonomy level, decision impact, existing controls, risk rating, review date, incident history, and approved use conditions. For material systems, the register should link to a model card, data documentation, privacy assessment, security test, impact assessment, human-review procedure, or vendor assurance report where one exists.

Probability and impact should be scored separately. A common five-by-five method scores likelihood from 1, meaning rare, to 5, meaning expected repeatedly, and impact from 1, meaning negligible, to 5, meaning potentially severe. A 25 maximum score identifies priority review, but it should not suppress mandatory treatment of prohibited or unlawful uses; a low estimated probability does not make unlawful processing acceptable. Scores should include evidence such as test results, complaint rates, override patterns, known vulnerabilities, and exposure volume. The resulting rating needs an owner and deadline, because an unowned “high” score merely transfers uncertainty upward.

| Feature | Lower-risk approach | Higher-risk approach |
| --- | --- | --- |
| Typical use | Drafting, summarising, coding assistance | Credit decisions, employment screening, essential services, biometric identification |
| Main harm | Inefficient work or rework | Financial loss, discrimination, safety impact, privacy breach, reputational damage |
| Human involvement | Optional review before external use | Mandatory review for adverse or material decisions |
| Evidence baseline | Inventory record, user policy, basic security controls | Documented testing, impact assessment, appeal route, monitoring, incident plan, named owner |
| Review trigger | Quarterly or after material change | Monthly for high-impact systems and after every model, data, prompt, or purpose change |

## How to Build the Register in 90 Days
The first 30 days should create visibility. Appoint an accountable executive, usually supported by a cross-functional working group covering risk, technology, legal, privacy, security, compliance, internal audit, and business operations. Identify systems through procurement records, cloud inventories, software licences, finance records, developer repositories, departmental pilots, and employee surveys. Assign provisional owners and record unknown fields as gaps rather than silently treating them as acceptable. Target every production system and every tool handling confidential or personal data, then expand outward to lower-consequence internal tools.

Days 31 through 60 should establish a consistent assessment method. Define the organisation’s risk taxonomy, scoring scales, acceptable-use rules, escalation thresholds, review frequency, and required evidence. Map each system to its purpose, users, data flows, suppliers, jurisdictions, and affected parties. Ask whether the tool merely assists a human or can automatically trigger a payment, rejection, ranking, investigation, disciplinary process, or customer communication. The assessment should also test whether staff understand the system’s limitations and whether users can challenge an adverse result.

Days 61 through 90 should turn inventory entries into managed risks. Prioritise perhaps the top 10 to 20 systems by harm, exposure, regulatory sensitivity, and vendor uncertainty, but begin documenting even basic controls for all material entries. Establish monthly or quarterly reviews, a change-notification process, and a central repository for evidence. Validate results with business owners rather than relying only on central IT. A register claiming full coverage but showing no owner, date, or test result is administrative completion; a partially complete register with explicit gaps and deadlines supports a more honest governance process.

## Practical Controls That Reduce Risk

Controls must match the risk. Access controls, encryption, secure development, vendor clauses, data minimisation, and prompt-injection testing may be appropriate for a low-impact drafting tool, but they do not replace fairness testing or an appeal process for an employment-screening model. For consequential systems, require documented performance thresholds by relevant population groups, human review at the point of decision, traceable reasons, periodic recertification, and a process for correcting data or reversing errors. Define what constitutes failure before deployment; for example, a credit model should not pass merely because aggregate accuracy exceeds 80% if approval rates vary sharply for comparable applicants.

Generative AI requires specific safeguards. Prohibit entering passwords, authentication secrets, board material, customer records, health information, or other restricted data into tools unless the service has been approved and contractual protections are confirmed. Configure retention settings, restrict plugins and external actions, log administrative changes, and test for prompt injection, sensitive-information leakage, fabricated citations, unsafe code, and unauthorised tool execution. Employees should receive role-specific training, but training is not a substitute for technical restrictions. A 30-minute annual awareness session may satisfy an awareness objective, yet it will not prevent a sales employee from uploading a customer list to an unapproved service.

Third-party AI requires contractual visibility. The register should name the provider, foundation model, major subprocessors, hosting locations, support channels, service changes, deletion practices, audit rights, and incident-notification period. Contracts should address confidentiality, intellectual property, security, data use, model training, localisation where required, service levels, vulnerability remediation, exit assistance, and cooperation with regulators. Because a cloud provider may host the application while an API supplier supplies the model, contract ownership can become unclear. Map responsibility for each layer and avoid assuming that one supplier’s certification covers the complete service.

## Register, Assessment Tool, or Full GRC Platform?

A spreadsheet can be effective for an organisation with fewer than roughly 20 AI systems and stable ownership, provided it has controlled fields, access rights, version history, validation rules, and regular review. It becomes brittle when used by multiple business units, relies on free-text statuses, or must preserve hundreds of test artefacts. Lightweight registers are inexpensive to start, but they often lack automated reminders, supplier linkage, evidence collection, and audit trails. They remain suitable for a first 90-day programme and can later export data into a larger system.

Dedicated AI governance tools sit between a spreadsheet and an enterprise governance, risk, and compliance platform. They can provide AI-specific taxonomies, intake workflows, model inventories, questionnaire libraries, policy controls, and dashboards. A general GRC platform may be better when the organisation already uses one for operational risk, third-party risk, privacy, audit findings, and corrective actions. Integration usually matters more than branding: an AI register should connect incidents and vendor reviews to existing processes instead of creating an isolated compliance file. It should also accommodate technical evidence that conventional risk modules cannot store cleanly.

| Feature | Spreadsheet register | Dedicated AI governance tool | Existing GRC platform |
| --- | --- | --- | --- |
| Setup cost | Lowest, often free to low cost | Subscription plus configuration | Often incremental cost if module already licensed |
| Best scale | Small or pilot portfolio | Growing multi-team portfolio | Regulated enterprise with formal audit workflows |
| Strength | Fast, transparent, flexible | AI-specific prompts and evidence workflows | Integrated audit, vendor, issue, and executive reporting |
| Weakness | Manual reminders and weak audit history | May become another disconnected silo | Requires configuration for AI-specific risks |
| Selection test | Can owners reliably update it monthly? | Does it reduce review effort without hiding risk? | Does it connect to existing controls and evidence? |

## Common Mistakes and How to Avoid Them
A major mistake is equating model accuracy with acceptable risk. Accuracy does not by itself address false negatives, unequal error rates, privacy, cybersecurity, explainability, or the cost of reversing a decision. Another error is treating all third-party models as external risks that IT does not own; the deploying organisation remains responsible for how its staff use the output. Teams also frequently inventory sanctioned projects but omit shadow AI used for research, recruitment, customer support, or coding. Anonymous surveys, cloud discovery, and procurement reconciliation help reveal these hidden uses.

Organisations also mistake a policy for operation. A policy may say that human review is mandatory, while employees approve every transaction without examining the model’s recommendation or receive no information about the reason for a decision. Reviewers need authority, time, training, and access to relevant data. Similarly, a one-time vendor questionnaire does not detect a new foundation-model release, altered retention setting, or added subprocessor. Require change notifications and event-driven reassessment, then reconcile contractual commitments with technical configuration at least annually for consequential systems.

Finally, avoid collecting excessive sensitive information in the register itself. Record findings and controlled links, not raw customer records, medical files, authentication secrets, or full identity documents. Protect the register as a governance asset because it reveals vulnerable systems, unresolved findings, and responsible executives. Set access on a need-to-know basis, maintain audit logs, and test restoration. A weak register can become a convenient attack map, while an overly restricted register may prevent business owners from maintaining it.

## Timing, Budget, and Decision Thresholds

Start immediately if the organisation already uses AI in customer service, hiring, credit, fraud, healthcare, insurance, biometrics, identity, education, legal advice, or safety-related operations. Even a low-budget team should create an inventory within 30 days, assign owners within 60, and document priority controls within 90. For lower-impact internal tools, quarterly review may be reasonable, provided any material change triggers reassessment. Higher-impact systems may need monthly operational monitoring and formal review at least every six to twelve months, with more frequent testing for unstable models or rapidly changing data.

Budget depends heavily on existing tools and exposure. A spreadsheet programme may cost little beyond staff time, although Indonesia’s regulated sectors may require external legal, privacy, security, model-risk, or assurance support. Dedicated software commonly uses subscription pricing based on users, workflows, modules, integrations, or assessed assets; quotations vary, so public price ranges would be misleading without a vendor and scope. Budget should cover inventory, legal mapping, security testing, fairness or reliability testing where relevant, privacy assessments, staff training, monitoring, independent validation, and remediation rather than software licences alone.

A useful escalation threshold is based on the worst credible harm, not average performance. Automatic decisions affecting eligibility, pricing, employment, access to essential services, personal identity, or physical safety should receive senior risk, legal, and operational review before deployment. An incident, material model change, new data category, new population, use in a new jurisdiction, or supplier refusal to disclose necessary information should pause expansion while reassessment occurs. Not every issue requires system shutdown, but the organisation should have a named decision-maker, a documented rationale, and a deadline. This combination allows innovation where controls are adequate without treating speed as evidence of safety.

## Quick answers

### Is an AI risk register mandatory for every Indonesian company?

There is no single universal rule that requires every Indonesian company to maintain a document with the name “AI risk register.” However, sector rules, data-protection duties, governance expectations, contracts, and global requirements may make an inventory and documented risk assessment necessary in practice. Regulated firms should confirm the exact requirements with their legal and sector-compliance teams.

### Who should own an AI risk register in Indonesia?

A senior executive should be accountable, while a risk, technology, compliance, or assurance function usually maintains the process. Business owners must remain responsible for the systems they deploy, and legal, privacy, cybersecurity, internal audit, and procurement teams should provide specialist review. Assigning ownership only to IT is insufficient because AI risks arise from business use and decisions.

### How often should an AI risk register be reviewed?

Low-impact internal systems may be reviewed quarterly, while systems affecting credit, employment, identity, safety, or essential services may need monthly monitoring and at least annual formal review. Reviews should also occur after a material model, data, prompt, supplier, integration, or purpose change. An event-driven review is often more valuable than relying on a fixed calendar alone.

### Should small businesses use a spreadsheet for AI risk?

Yes, a controlled spreadsheet is often the most practical starting point for a small organisation with limited AI use. It should include mandatory fields, named owners, risk scores, review dates, controls, incidents, and evidence links rather than unrestricted free text. Small firms should upgrade to dedicated software only when reminders, integrations, auditability, or scale justify the cost.

### What triggers an emergency AI risk review?

Emergency review is appropriate after a serious incident, material customer complaint, discriminatory outcome, data breach, hallucination causing harm, or unauthorised model action. New sensitive data, a new supplier, a changed model version, expansion to a new jurisdiction, or use in a more consequential decision should also trigger reassessment. The organisation should pause expansion when the potential harm is severe and controls cannot yet be verified.

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