AI & Algorithm Assurance: Governance, Risk & Controls

For boards, senior management and control functions, the central question is no longer whether AI is being adopted, but whether the organisation can evidence that its use is understood, controlled and defensible.

Artificial intelligence is increasingly embedded in organisational operations and decision-making, from recruitment and credit assessment to fraud detection, customer interaction and operational analytics. While these applications can improve efficiency and support faster, more informed decisions, they also create governance, regulatory and operational risks that traditional technology controls may not fully address.

An organisation may have an AI policy, defined responsibilities and documented controls. However, these alone do not demonstrate that an AI system is operating fairly, that its outputs can be appropriately explained or that the controls surrounding it are working effectively.

The EU Artificial Intelligence Act (EU AI Act) establishes a risk-based framework for AI and introduces requirements in areas including risk management, data governance, documentation, transparency, human oversight, accuracy, robustness and cybersecurity. The nature and extent of the applicable obligations depend on matters including the organisation’s role and the classification and intended use of the AI system, with more extensive requirements applying to high-risk systems.

For organisations, the challenge is therefore moving from “we have AI governance” to “we can demonstrate that our AI systems are appropriately governed and operating as intended.”

Why This Matters Now

AI adoption can move considerably faster than the governance structures designed to oversee it. AI functionality may be introduced through internally developed systems, third-party platforms or existing software products, sometimes without organisations having a complete view of where AI is being used or the risks associated with individual use cases.

This is particularly relevant within Malta’s regulated financial services sector. The Malta Financial Services Authority (MFSA) has reinforced expectations around AI governance, including board and senior management accountability, third-party dependencies, model risk, explainability, ongoing monitoring and data governance. These expectations increase the importance of being able to evidence not only that governance arrangements exist but also that they operate effectively in practice.

Alongside evolving regulatory requirements, ISO/IEC 42001:2023 provides an internationally recognised framework for establishing, implementing and continually improving an Artificial Intelligence Management System (AIMS). While distinct from the EU AI Act, the standard offers a structured approach to managing AI-related risks, defining accountability and embedding governance throughout the AI lifecycle.

The regulatory direction is clear: AI should not be treated solely as a technology or innovation matter. Organisations need to understand the risks associated with each use case and retain evidence that governance arrangements, controls and safeguards operate effectively in practice.

This requires more than establishing policies. Organisations need evidence that governance arrangements operate effectively and that the behaviour and outcomes of relevant AI systems have been appropriately assessed.

What Effective AI Assurance Looks Like in Practice

Effective AI assurance examines both the technology and the governance environment surrounding it. It tests whether documented controls provide meaningful oversight throughout the AI lifecycle and whether relevant systems continue to operate within defined and acceptable parameters.

In practice, effective AI assurance typically includes:

AI Inventory and Classification: Establishing where AI is being used, for what purpose, who is responsible for it and which regulatory or risk classification may apply.
Bias and Fairness Testing: Assessing data, models and outputs to identify whether AI systems produce unintended or inappropriate differences across relevant groups.
Explainability and Transparency: Evaluating whether relevant AI outputs and decisions can be appropriately understood, challenged and explained to management, users and other stakeholders.
Governance and Accountability: Assessing whether ownership, approval processes, risk responsibilities, escalation mechanisms and oversight arrangements are clearly defined and operating effectively.
Data Governance: Considering whether data used by AI systems is appropriately governed, relevant and of sufficient quality for its intended purpose.
Human Oversight: Determining whether individuals responsible for overseeing AI have the information, competence and authority necessary to understand and, where appropriate, intervene in AI-driven processes.
Ongoing Validation and Monitoring: Assessing whether changes in model performance, data, use or risk are identified and addressed throughout the AI lifecycle.

A useful way to think about this is: an AI system should not be considered appropriately controlled simply because its intended behaviour has been documented. Organisations should be able to demonstrate, through evidence and testing, that the system continues to behave within defined and acceptable parameters.

What Good AI Assurance Delivers

High-value AI assurance provides management with insight that goes beyond a compliance checklist. It helps leadership understand where AI creates material exposure, whether existing controls remain appropriate and where additional action may be required.

Management should be able to answer, with evidence:

Where is AI being used across the organisation, and who is accountable for each use case?
Which AI systems present the greatest regulatory, operational or reputational risk?
Have relevant risks relating to bias and fairness been appropriately assessed and addressed?
Can significant AI-generated outputs or decisions be appropriately explained and challenged?
Are human oversight arrangements meaningful and effective in practice?
Are AI models subject to appropriate validation and ongoing monitoring?
Are third-party AI solutions subject to appropriate due diligence and oversight?
Is sufficient documentation available to demonstrate how AI-related risks and regulatory requirements are being addressed?

Being able to answer these questions provides management with a clearer view of the organisation’s AI risk exposure and supports more informed decisions about the adoption and continued use of AI.

Common Challenges We See

One of the most common challenges is visibility. AI may be introduced independently across different business functions or embedded within third-party applications, resulting in an incomplete understanding of where AI is being used and who is responsible for it.

Governance can also become fragmented. Accountability may be distributed across technology, data, risk, compliance and business teams, creating uncertainty over who approves AI use, who monitors performance and who is responsible when outcomes fall outside acceptable parameters.

An organisation may state that its AI systems should be fair, transparent and subject to human oversight, but demonstrating these principles requires evidence proportionate to the nature, purpose and potential impact of each system. Where relevant, this may include assessing bias, determining an appropriate level of explainability and evaluating whether human oversight operates effectively in practice.

Third-party AI also presents particular challenges. Organisations may have limited visibility over how externally developed models operate, the data on which they were trained or how they change over time. This can make it more difficult to assess risk and demonstrate appropriate oversight, particularly where AI supports important business decisions.

These challenges are interconnected. Limited visibility weakens accountability; fragmented ownership makes monitoring inconsistent; and insufficient evidence makes it harder to demonstrate that safeguards operate as intended. An independent, risk-led assessment can help management identify where exposure is greatest, validate whether existing controls are effective and define proportionate priorities for remediation.

A Practical Next Step

Organisations do not need to wait for a regulatory deadline or an AI-related incident before assessing whether their governance arrangements are effective.

A practical starting point is a focused AI & Algorithm Assurance assessment. The scope can be tailored to the organisation’s maturity and highest-risk use cases, covering areas such as AI inventory and classification, governance and accountability, bias and explainability, risk management, human oversight, model validation, third-party dependencies and EU AI Act readiness.

The objective is not simply to create more AI governance documentation but to establish confidence that AI risks are understood, controls operate effectively and the organisation can demonstrate this through appropriate evidence.

As AI becomes increasingly embedded in business operations and decision-making, the organisations best positioned to adopt it responsibly will be those that can move beyond stating that AI is governed to demonstrating that it can be trusted.

How BDO Malta Can Help

BDO Malta’s AI & Algorithm Assurance services combine technical testing, governance assessment and regulatory readiness support to help organisations understand and manage the risks associated with AI.

Our approach is proportionate and risk-led, connecting regulatory expectations with how AI systems are actually selected, developed, deployed, used and monitored within the organisation.

Our support can include:

Bias and Explainability Testing: Assessing data, models and outputs to identify potential bias and evaluate whether algorithmic decisions can be appropriately understood and explained.
AI Governance Framework Reviews: Evaluating governance arrangements across the AI lifecycle, including accountability, validation, monitoring, documentation and oversight, with reference to recognised frameworks such as ISO/IEC 42001, where relevant.
EU AI Act Readiness Assessments: Supporting organisations in identifying and classifying AI systems, understanding applicable requirements, identifying gaps and prioritising remediation activities.
AI Risk Assessment and Management: Identifying foreseeable risks associated with AI use and assessing whether proportionate controls, monitoring and mitigation measures have been established.
AI Inventory and Use-Case Assessments: Helping organisations establish a structured view of where AI is used and identify systems requiring enhanced governance or oversight.
Fundamental Rights Impact Assessment Support: Where applicable, helping organisations assess the potential impact of relevant high-risk AI deployments on fundamental rights as part of broader AI risk management.
AI Governance Briefings: Supporting boards, risk committees and senior management in understanding AI exposure, regulatory expectations and their governance responsibilities.

Depending on the organisation’s maturity and risk profile, an engagement can provide a prioritised view of AI exposure, practical remediation actions and evidence to support scrutiny by management, the board and regulators.

If your organisation is developing its AI governance approach, preparing for regulatory requirements or seeking independent confidence over a significant AI use case, BDO Malta can help you identify an appropriate starting point and define a proportionate scope for assessment.