Govern AI with confidence, from data to value delivery

Data quality begins with building on existing foundations

AI systems are only as trustworthy as the data they are trained on and the information they consume. An AI system using poorly understood or poorly controlled data will produce outputs that cannot be defended, and no amount of oversight can compensate for a poorly trained model. This means investments in data governance are an accelerator for responsible AI.

In our experience, organisations often focus on establishing AI policies and governance committees without also addressing the underlying data foundations that support effective decision-making. While governance frameworks are important, it is essential to have confidence in the quality, ownership and traceability of the data being used by AI systems.

Some key aspects of data governance that directly enable responsible AI include:

• Ownership and accountability frameworks: Oversight functions used to govern enterprise data should be extended to support AI. Data owners, stewards and custodians are important stakeholders in the adoption, deployment and management of AI systems and use cases.
• Data quality management: The old maxim of ‘rubbish in, rubbish out’ is acutely relevant when considering AI models. Efforts to identify, map, profile and manage the quality of critical data elements are essential for enabling the development of reliable AI models.
• Metadata management: AI governance requires organisations to understand what data is being used, where it comes from and how it is being used. Metadata management capabilities, including business glossaries, data lineage and data catalogues, provide the traceability and transparency needed to support explainability, accountability and auditability.
• Data security and classification: Controls used to identify, classify and protect sensitive information are critical in an AI-enabled environment. This is particularly important for AI agents and other systems that may access enterprise data, tools or workflows, where weak cyber controls can increase the risk of unauthorised access, data leakage or unintended actions. Existing data security capabilities – including access management, least-privilege permissions, logging, monitoring and incident response – should be extended to AI use cases.

AI introduces new governance challenges

While strong data governance is important, AI introduces new risks that traditional governance models were not designed to address. Unlike conventional technology systems, AI solutions can generate content, make recommendations, identify patterns and influence decisions in ways that may not always be transparent, predictable or easily explained. Agentic AI further complicates this because these systems may be able to act with greater autonomy, access enterprise tools and data, trigger workflows, or act on a user’s behalf.

This creates a practical challenge for many organisations: AI adoption is moving faster than the governance capabilities needed to manage it. In many cases, organisations have strong ambition and significant investments in AI, but limited visibility over where and how AI is being used, who is accountable for it, and whether appropriate controls are in place.

Effective AI governance needs to extend beyond policies and oversight. Organisations require practical mechanisms to identify AI use cases, assess risk, assign accountability, apply proportionate controls and monitor AI systems over time. This includes managing AI model risk, which is the possibility of negative outcomes arising because an AI system behaves in ways that are unintended, unreliable or difficult to explain.

A robust approach to responsible AI adoption needs to include the following:

• Accountability: Building on existing governance roles, organisations must define clear accountability for AI systems, including who approves, owns, monitors and escalates AI-related risks. These roles should include both technical and business stakeholders so that AI decisions are governed in their operational context.
• AI use case intake and inventory management: Organisations should establish a formal intake and registration process to ensure proposed AI use cases are assessed for risk, compliance and business value before implementation. This also enables organisations to triage use cases into proportionate governance pathways, where higher-risk use cases are subject to more rigorous review, approval and control requirements, while lower-risk use cases can be progressed through a simpler approval process. Maintaining a centralised inventory of AI systems enables ongoing oversight, monitoring and reporting across the organisation's AI landscape.
• AI system lifecycle management: AI systems require governance from ideation and design through to development, deployment, monitoring and retirement. Risk-tiered stage gates, approval processes and control requirements help ensure higher-risk use cases receive greater scrutiny and oversight, while lower-risk use cases can be progressed through a more streamlined pathway. This keeps governance proportionate as AI systems, technologies and business requirements evolve.
• Cost and usage control: Organisations should monitor cost and usage metrics, especially where pricing is based on token consumption, model calls, and compute or integration activity. Cost ownership should be clearly articulated, and controls should be implemented to cap consumption, limit access to higher-cost models and ensure periodic re-evaluation of AI use against expected business value.
• Cybersecurity and access controls: AI systems, particularly agentic solutions, need to be subject to cyber controls throughout their lifecycle. This includes identity and access management, least-privilege permissions, secure configuration, vulnerability management, activity logging, monitoring, incident response, and clear controls over the tools, systems and data the AI system can access. These controls should be risk-tiered, with higher-risk AI use cases subject to stronger security review and assurance before deployment.
• AI model testing, evaluation, validation, verification and ongoing monitoring: Organisations should test, evaluate, validate and verify AI models before production, assessing accuracy, robustness, bias, explainability and alignment with intended purpose. These controls should be supported by ongoing monitoring to detect model drift, changes in performance, emerging risks and unexpected outputs over time.
• Third-party risk management: Many AI capabilities are sourced from external vendors, platforms and service providers, extending the organisation’s risk landscape beyond its direct control. Organisations should enhance existing third-party risk management processes to assess AI-specific considerations, including data handling, system transparency, security controls, regulatory compliance and the vendor’s approach to managing AI-related risks throughout the service lifecycle.

Australia’s emerging expectations for responsible AI

Regulatory expectations relating to AI continue to evolve, both in Australia and globally. While Australia does not currently have a comprehensive AI-specific legislative framework, organisations are facing increasing scrutiny around how AI systems are governed, monitored and used. As a result, many organisations are moving ahead with AI governance frameworks that leverage international standards such as ISO/IEC 42001:2023, NIST AI RMF or the EU AI Act, rather than waiting for future regulation to dictate requirements.

The Australian legislative environment should also be considered alongside the Privacy Act. From December 2026, entities regulated by the Privacy Act will be required to disclose instances where computer programmes make, or substantially support, decisions that could have a significant effect on an individual’s rights or interests. The Office of the Australian Information Commissioner (OAIC) has also made clear that existing privacy principles apply to AI today and expects organisations to embed human oversight, be transparent with customers and avoid deploying tools simply because they are available.

Practical steps to strengthen AI governance

For many organisations, the AI challenge lies in how to balance effective governance and risk management whilst enabling innovation. The following actions can help establish a practical and scalable AI governance framework that builds on existing governance capabilities.

1. Assess the maturity of your data governance practices: Identify which existing data governance capabilities can be leveraged for AI governance and where uplift is required to support responsible AI adoption.
2. Define the organisation’s AI risk appetite: Set clear thresholds for acceptable AI risk to guide which AI use cases are permitted, what controls and assurance are required, when escalation or executive approval is needed, and how governance should be proportionate to risk.
3. Establish an AI inventory: Create a centralised record of AI systems and AI use cases to improve visibility, enable risk-tiering, support proportionate governance pathways and strengthen ongoing oversight across the organisation.
4. Establish baseline policy documentation: Set clear expectations for responsible AI principles, lifecycle controls, model risk management, third-party risk management and risk-based approval pathways, so that governance and control requirements are proportionate to the risk of each AI use case.
5. Embed cyber controls and AI observability: AI systems should be brought within existing cyber control frameworks. As agentic AI adoption matures, organisations should also consider specialised AI observability and control capabilities to monitor agent activity, manage tool and data access, detect anomalous behaviour, and support ongoing assurance.
6. Leverage existing governance structures to embed responsible AI: Integrate AI into existing data governance forums, oversight structures, training pathways and communications to avoid duplication and support consistent adoption.
7. Link AI governance to business value: Assess AI use cases against both risk and expected benefit so investments are focused on business applications that are safe, cost effective, scalable and aligned to strategic objectives.

Organisations looking to realise the opportunities presented by AI must adopt governance programmes and frameworks that meet emerging expectations, manage risk and maintain trust as AI use scales.

Protiviti helps organisations build these foundations so they can move with confidence and scale AI responsibly.