AI Assurance: Changing Risk into Better Decisions

Why the need for AI assurance? We’ve experienced a hallmark year for artificial intelligence in Africa. Looking to the future, Carnegie Endowment states, “In Africa, AI has the potential to grow the continent’s economy by an estimated $2.9 to 4.8 billion by 2030.” Yet, AI governance is still in its early stages, with the major challenge being balancing the risks and rewards of this technology.

However, many boards are still taking the step without the correct foundations in place, as seen in Diligent Institute’s “What Directors Think 2026” report. The results show:

• 60% of legal, compliance, and audit leaders cite technology as their top risk concern
• 29% of organizations have comprehensive AI governance plans
• 8% of directors report strong AI knowledge
• 40% say rapid technological change is the most challenging issue to oversee

This is where AI assurance becomes crucial.

Ultimately, boards do not need another dense technical update on models, prompts, or architectures. They need clear, credible, board-ready insight on:

1. Where AI is being used
2. What could go wrong
3. Whether controls are working
4. What decisions need to be made

Needless to say, for internal audit and risk leaders, that means translating complex AI risk into oversight language that directors can actually use.

How AI Assurance Became Board-Level Priority

AI is now core to strategy and governance, not just a technology topic. When AI influences material processes, decisions, or exposures, directors are expected to understand how management governs it and whether the organization’s controls are fit for purpose.

That expectation is being reinforced by regulation. The EU AI Act entered into force in August 2024, with obligations phasing in over time, and high-risk AI requirements becoming mandatory in August 2026. The Act classifies AI systems into unacceptable, high, limited, and minimal risk categories, with stricter obligations for higher-risk use cases. For providers and deployers of high-risk AI, those obligations include risk management, data governance, transparency, human oversight, documentation, and ongoing monitoring.

Where does this lead African countries? While each country provides its own context, looking at the bigger picture shows an interesting trend:

Out of 55 members in the African Union, 11 have AI policies in development, including:

• Morocco
• Tunisia
• Chad
• Cameroon
• Togo
• Djibouti
• Tanzania
• Zimbabwe
• Namibia
• Botswana
• South Africa

 12 have published some form of AI policy:

• Senegal
• Mauritania
• Algeria
• Libya
• Egypt
• Nigeria
• Benin
• Ghana
• Côte D’Ivoire
• Ethiopia
• Kenya
• Zambia

The message remains clear: AI governance cannot be handled as an informal side project. Boards need evidence that governance is real, not just documented. As Keith Enright, VP and Chief Privacy Officer at Google and Board Director at ZoomInfo, notes, boards need to apply the right level of governance pressure to whoever oversees the AI landscape, the risk exposure, the disruption, and the opportunity.

What Directors Need from AI Assurance

Directors are not asking for more jargon. They are asking for clarity.

They need:

• A clear AI map showing where AI, GenAI, and Agentic AI are used across products, processes, and decisions
• A concise view of risks and controls across strategic, operational, compliance, ethical, and reputational categories
• Oversight linkage that connects AI issues to strategy, risk appetite, and assurance plans
• Governance comfort on ownership, monitoring, and the metrics that matter.

Boards should ask practical questions: Where does the data come from? Has the model been validated? How is bias being tested? Can decisions be explained? What ethical guardrails are in place?

Those are not technical questions. They are governance questions.

Where Does this Leave Internal Audit Teams?

This is where internal audit becomes indispensable.

The Institute of Internal Auditors positions internal audit as a key player in AI governance, providing independent assurance that AI risk and control frameworks are robust, regularly updated, and effectively implemented across the organization. That includes evaluating AI decision-making processes, data quality controls, and algorithmic bias assessments.

The role of internal audit is also changing quickly, creating both opportunity and new exposure. Auditors are increasingly expected to scrutinize data provenance, model validation, bias testing, explainability, risk assessments and ethical guardrails for deployment and monitoring, which are also questions a board will focus on. 

In other words, with respect to AI, internal audit is no longer just checking whether a process exists. It is translating technical AI risk into the language of oversight, assurance and fiduciary duty.

That shift is already underway. Audit teams are using AI to automate control testing, detect fraud in procurement data and expand audit coverage with continuous analytics.

AI Assurance: A Board-Ready Practical Framework

The most useful AI assurance programs do not overwhelm directors with technical detail. They convert AI risk into a repeatable board process.

A practical approach has four steps:

1. Inventory and classify
2. Map risks
3. Rate control strength
4. Decide actions.

This maps well to broader governance frameworks. The NIST AI Risk Management Framework, for example, provides a practical structure through its four core functions: Govern, Map, Measure and Manage. For multinational organizations, that can be especially useful alongside the EU AI Act: NIST offers a flexible operational model, while the Act sets binding legal obligations.

Board Reporting

If the goal is better board oversight, reporting format matters as much as reporting content.

Good board-ready AI reporting should use plain language, avoid unnecessary technical jargon and favor visuals over text. It should show, in one place:

• A risk overview by severity or exposure
• Control maturity and recent testing status
• Key findings and emerging concerns
• Clear management requests and required board decisions.

That is what makes reporting board-ready: not just describing risk, but framing the decision.

A Big Step from Periodic Review to Continuous Assurance

One of the biggest changes in audit and assurance is timing.

Traditional, backward-looking audit cycles are increasingly too slow for AI-related risk. Continuous risk monitoring offers an alternative: an automated, real-time approach that uses AI and analytics to evaluate business processes, transactions and controls on an ongoing basis. Instead of asking what happened last quarter, teams can ask what is happening now, update their annual risk assessments and deliver more impactful, timely findings.

That shift is also visible in skills and tooling. In the IIA’s 2025 North American Pulse survey, 78% of chief audit executives said data analytics was their teams’ most needed competency improvement. At the same time, we are seeing audit teams reducing management time by nearly 70% and cutting audit-cycle admin from roughly 120 hours to about 34.

That is not just an efficiency play. It is an assurance play. When findings can flow into enterprise risk posture and board reporting faster, governance becomes more responsive.

The EU vs AU: Key Differences

For EU audiences, the compliance starting point has been clear for a long time.

The EU AI Act provides a prescriptive framework with defined risk categories and explicit obligations for providers and deployers. That gives assurance teams a more concrete benchmark for evaluating governance effectiveness and control design.

The most comparable to the EU AI Act is the African Union’s Continental Artificial Intelligence Strategy, but there is a crucial difference in focus between these two policies. While the EU AI Act focuses on risk mitigation, the AU’s strategy is concerned with bridging the technological gap. The policy states, “The development of AI and the societal and economic changes it will bring are just beginning. Africa should be well prepared for the AI Revolution, not only to address the challenges of AI but also to become a key player in harnessing it.”

Regardless, organizations find themselves needing to know more about harnessing this technology without leaving themselves wide open to risks.

What Should African Organizations Do?

If you want to make the AI assurance board-ready, start with the basics:

• Develop an AI inventory
• Classify use cases by risk
• Integrate AI governance requirements into ERM and internal audit planning
• Define ownership clearly
• Upgrade reporting
• Invest in capability
• Move toward continuous monitoring where possible.

Having directors who can account for all outcomes, explain where AI matters in their organization, and what is being done about it marks a successful AI assurance policy.

To get on the same page about this as leading organizations, you can book a demo with our GRC experts today.

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