The Burgeoning AI Audit Market: How Can Business Leaders Stay Ahead?

Forbes

5 days ago

Mark Thirlwell, Global Digital Director at BSI.
While AI represents a huge opportunity for organizations and society, guardrails to ensure it is used safely, securely and responsibly are vital. It's early days, but the shape of those guardrails is emerging. In the EU, the AI Act is a starting point, but even this, arguably one of the most progressed AI regulatory regimes, is facing challenges in its practical roll-out.
Outside of regulation, there are a growing number of international standards outlining agreed-upon best practices and an increasing number of providers offering AI audit or assurance services. Indeed, the market has the potential to be sizeable (the U.K. government has projected it could surpass £6.5 billion by 2035). The Financial Times reported that leading accountancy firms, including the Big Four, are launching AI auditing programs. Others are already in the field, responding to market demand from businesses to be able to demonstrate they are taking the right steps and reassure their customers.
According to research from my company, BSI, 63% of senior leaders would trust AI tools more if they were validated by an external organization. Yet the confusion of the corporate sustainability landscape offers a cautionary tale. It's possible that myriad schemes or ways of 'proving' compliance will pop up, just as they have around ESG reporting.
For businesses, the risk is that this becomes a wild west of unchecked providers, with regulators, customers and investors struggling to differentiate credible AI governance implementations. Having certainty about what "good" looks like is imperative; otherwise, it undermines the role of assurance in the first place.
Here are six key questions business leaders should be considering when it comes to quality assurance in AI systems.
As regulatory frameworks evolve and public scrutiny grows, organizations must be able to demonstrate that their AI systems are not only effective but also safe, fair and accountable: this means validating performance against both functional requirements (like accuracy and robustness) and ethical expectations, such as fairness, explainability and safety.
A robust assurance process involves validation spanning the full AI lifecycle, including pre-deployment testing and validation, real-world performance monitoring, bias and risk assessments, and dataset quality checks.
For decades, businesses have relied on internationally recognized management systems—like ISO 9001 for quality and ISO/IEC 27001 for information security—to provide structure, rigor and trust. The same principle now applies to AI, with the world's first AI management system standard (ISO/IEC 42001) published in 2024. Organizations are already certifying to it to demonstrate robust governance and compliance around ethical and trustworthy AI.
An AI management system provides a structured framework for overseeing the development, deployment and ongoing use of AI technologies. It is designed to support:
• Ethical and transparent AI design and operations
• Identification and mitigation of AI-specific risks
• Clear assignment of responsibilities and internal controls
• Continuous improvement and lifecycle oversight
• Alignment with current and upcoming regulations
As more organizations adopt AI governance frameworks, the need for credible, independent assessments becomes critical. But not all audits offer the same level of assurance. To ensure a consistent and impartial evaluation, businesses should work with an accredited certification body. This can help build confidence with regulators, investors and customers that assessments are independent and meet globally recognized best practices.
The international standard ISO/IEC 42006:2025 outlines requirements for certification bodies when auditing AI management systems. It aims to ensure auditors have the technical competence and governance safeguards necessary to evaluate AI systems responsibly and consistently, regardless of sector or geography. Ask your auditor whether they are adhering to this.
AI is not just a technical discipline—it's a governance challenge. Yet BSI research found that only 22% of business leaders say their organization has an established AI governance program.
It's essential to build internal capabilities across all levels, from engineers and risk managers to board members. This should include an understanding of best practice AI standards, conducting internal audits, managing technical controls and overseeing strategic alignment and compliance.
The ISO/IEC 38507:2022 standard provides clear guidance for organizational governing bodies—whether boards, executives or trustees—to enable and govern the use of AI. It aims to ensure that AI systems are aligned with organizational goals, ethical norms and stakeholder expectations. Designed for all types of organizations, it addresses the unique challenges AI poses, such as ambiguity in interpretation, opaque decision-making and shifting regulatory requirements.
Investing in AI governance can de-risk AI initiatives, support regulatory readiness and ensure leadership teams are equipped to make informed, ethical decisions in a rapidly evolving landscape.
AI assurance doesn't end at deployment. Models operating in dynamic environments can degrade over time due to data drift, concept drift or shifting user behavior. Without continuous monitoring, issues like bias may go undetected, leading to unintended outcomes, performance drops or compliance failures.
To manage this, implement mechanisms for:
• Tracking real-world model performance
• Detecting drift or unexpected behavior
• Monitoring fairness and bias over time
• Incorporating human oversight where appropriate
Monitoring is more than a technical safeguard—it's a governance imperative. Done correctly, it supports compliance, reduces risk and helps build long-term trust in AI systems.
Being able to show how and why decisions were made is just as important as making the right decisions in the first place.
Structured documentation—covering everything from data sourcing and model design to risk assessments and deployment approvals—is essential for audit readiness, regulatory compliance and building internal accountability. It enables organizations to demonstrate due diligence, respond confidently to external scrutiny and learn from experience.
Ultimately, AI use is accelerating at pace. There is an urgent call for practical steps from businesses to bolster confidence in AI, safeguard stakeholder interests and pave the way for responsible innovation in this rapidly evolving field.
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