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AI Governance

Our structured AI Governance services help organizations manage AI risks while supporting ethical use, regulatory alignment, and appropriate risk oversight across the AI lifecycle, strengthening trust and clear accountability.

Without clear governance, organizations face:

  • Unintended bias and unfair outcomes
  • Lack of transparency and explainability
  • Regulatory and legal exposure
  • Data misuse and privacy violations
  • Operational and security risks

AI Governance provides a structured method to consistently evaluate and govern relevant AI risks, such as ethical, regulatory, security, and reputational considerations, based on the specific AI use case, using fit-for-purpose governance frameworks.

Cisometric helps organizations implement practical, risk-based AI Governance frameworks that enable ethical, compliant, and trustworthy AI use, with clear accountability and appropriate safeguards .

Why Cisometric for AI Governance

  • Professional Credentials

    Delivered by consultants with internationally recognized AI governance credentials, including the first ISACA AAIA-certified professional in Indonesia, and supported by team members who have completed ISO/IEC 42001 training programs.

  • Risk-Driven Approach

    We evaluate AI risks in the context of business use, regulatory exposure, and the characteristics of the AI system, ensuring governance decisions are risk-informed and proportionate.

  • Local to Global Standards

    We align global AI governance frameworks with Indonesian regulatory guidance, supporting consistent and defensible governance practices.

Scope of Our Service

Our service covers AI Governance, maturity assessment, or risk assessment based on:

ISO 42001:2023

COBIT 2019

NIST AI RMF

Global Insights

The OECD AI Principles are the first intergovernmental standard for trustworthy AI, promoting respect for human rights and democratic values. They consist of five principles and five recommendations that guide policymakers and AI actors. To align with these principles, organizations should proactively identify and manage risks throughout the AI lifecycle to ensure AI systems remain robust, fit for purpose, and low risk.

Value-based principles

  • Inclusive growth, sustainable development and well-being

  • Human rights and democratic values, including fairness and privacy

  • Transparency and explainability

  • Robustness, security and safety

  • Accountability

Recommendations for policymakers

  • Investing in AI research and development

  • Fostering an inclusive AI-enabling ecosystem

  • Shaping an enabling interoperable governance and policy environment for AI

  • Building human capacity and preparing for labour market transition

  • International co-operation for trustworthy Artificial Intelligence

According to ISACA, effective AI Governance is built on several common types of controls:

Governance and Organizational

  • Accountability
  • AI Policy
  • AI Assist Management
  • Risk Management

System Development Life Cycle

  • Project Management
  • Ideation Phase
  • Design Phase
  • Deployment

Operation and Life Cycle Management

  • Data Flow Diagram
  • Version Control
  • Change Management
  • Monitoring and Logging

Technical, Security and Privacy

  • Data Quality
  • AI Incident Management
  • Privacy
  • Data Management

Legal, Compliance and Regulatory

  • FRIA
  • Legal & Regulatory
  • AI Legal Contracts
  • Risk Management

Ethical and Human Values

  • Consent Management
  • Bias Management
  • Transparency
  • Human Oversight

Indonesian Regulatory Landscape

Ministry of Communication and Digital Republic of Indonesia

In August 2025, Komdigi published “Konsep Pedoman Kecerdasan Artifisial” (AI Ethical Values), reflecting principles that help guide the development, deployment, and use of AI systems, which may have implications for fundamental rights.

Inclusivity

Humanity

Security

Accessibility

Transparency

Accountability

Data Privacy

Environment Sustainability

Intellectual Property

Trustworthy Artificial Intelligence governance wheel: accountability, reliability and human oversight across people, process and technology

Otoritas Jasa Keuangan (OJK)

OJK has introduced the Artificial Intelligence Governance for Indonesian Banking to support the responsible development and deployment of AI in the banking sector, aligned with existing digital transformation, cybersecurity, and digital resilience regulations.

Frequently Asked Question

What is AI Governance, and how does it differ from traditional IT governance?

AI Governance is a structured method to evaluate and manage the specific risks introduced by artificial intelligence, such as algorithmic bias, lack of explainability, and autonomous decision-making.

While traditional IT governance focuses on the management and control of IT systems, including security, availability, and compliance, AI Governance goes further by addressing the ethical, legal, and societal impacts of AI. It ensures that the use of AI within an organization remains trustworthy, responsible, and aligned with organizational values and regulatory expectations throughout the AI lifecycle.

Which frameworks does Cisometric use to assess AI maturity and risk?

We use a “fit-for-purpose” approach, selecting frameworks that best match your organization’s industry and risk profile. Our primary references include ISO/IEC 42001:2023 (the international standard for AI Management Systems), NIST AI RMF, and COBIT 2019. We also align these with the OECD AI Principles to ensure your strategy supports robust and globally interoperable standards.

How does this service help us comply with Indonesian regulations?

Our service explicitly aligns global standards with local requirements. We help you navigate the “Konsep Pedoman Kecerdasan Artifisial” from Komdigi (Ministry of Communication and Digital) and the specific OJK Artificial Intelligence Governance for Indonesian Banking. We assist in aligning your AI governance framework with local mandates on digital resilience and consumer protection, while enhancing organizational readiness for future regulatory developments.

What do you mean by a “risk-based approach” to AI Governance?

Not all AI systems pose the same level of risk. A chatbot for internal IT support carries different risks than an AI model used for credit scoring. We evaluate risks in the context of your specific business use case. This allows us to design controls that are proportionate, maximizing the benefits of the technology without imposing unnecessary bureaucratic hurdles, ensuring the system remains fit for purpose.

At what stage of the AI lifecycle should we implement governance?

Governance should be applied proactively across the full AI lifecycle, from initial design and data collection to development, deployment, and ongoing monitoring. Early identification of AI specific risks, including bias, lack of transparency, and inappropriate use cases, is significantly more cost effective than post deployment remediation.

What types of AI are covered by this service?

This service is technology and model agnostic. It covers AI risk assessments for a wide range of AI systems and use cases, including predictive models, decision support systems, and Generative AI, based on how the AI is used and the risks it introduces.

Is AI Governance only for technical teams?

No. Effective AI Governance requires collaboration between technical teams, legal, compliance, and business leadership. Our service bridges these gaps by establishing clear accountability structures.

What are the credentials of the team delivering this service?

Cisometric’s consultants hold internationally recognized credentials. Our team includes the first professional in Indonesia certified by the AAIA (Advanced in AI Audit) by ISACA, and members who have completed specialized training in ISO/IEC 42001:2023. This ensures you are advised by experts who understand the nuances of both technical AI risks and global compliance standards.

What is the tangible output of an AI Governance engagement?

You will receive a set of practical and prioritized recommendations to address gaps in AI governance, including risk management, transparency, accountability, and human oversight, tailored to your specific AI use cases.

How does this service support “interoperability” with global markets?

The service uses internationally recognized AI governance principles as a reference point to support consistent governance practices and clearer communication with global partners.

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